code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 1 |
import numpy
# List of input, output pairs
lowerCAmelCase_ = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
lowerCAmelCase_ = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
lowerCAmelCase_ = [2, 4, 1, 5]
lowerCAmelCase_ = len(train_data)
lowerCAmelCase_ = 0.009
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase="train" ) -> List[str]:
"""simple docstring"""
return calculate_hypothesis_value(_UpperCamelCase , _UpperCamelCase ) - output(
_UpperCamelCase , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Optional[int] = 0
for i in range(len(_UpperCamelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=m ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Union[str, Any] = 0
for i in range(_UpperCamelCase ):
if index == -1:
summation_value += _error(_UpperCamelCase )
else:
summation_value += _error(_UpperCamelCase ) * train_data[i][0][index]
return summation_value
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : int = summation_of_cost_derivative(_UpperCamelCase , _UpperCamelCase ) / m
return cost_derivative_value
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
global parameter_vector
# Tune these values to set a tolerance value for predicted output
snake_case_ : Optional[int] = 0.000_002
snake_case_ : List[str] = 0
snake_case_ : List[str] = 0
while True:
j += 1
snake_case_ : str = [0, 0, 0, 0]
for i in range(0 , len(_UpperCamelCase ) ):
snake_case_ : str = get_cost_derivative(i - 1 )
snake_case_ : Any = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
_UpperCamelCase , _UpperCamelCase , atol=_UpperCamelCase , rtol=_UpperCamelCase , ):
break
snake_case_ : Dict = temp_parameter_vector
print(('''Number of iterations:''', j) )
def lowerCamelCase_ ( ) -> int:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
print(('''Actual output value:''', output(_UpperCamelCase , '''test''' )) )
print(('''Hypothesis output:''', calculate_hypothesis_value(_UpperCamelCase , '''test''' )) )
if __name__ == "__main__":
run_gradient_descent()
print('''\nTesting gradient descent for a linear hypothesis function.\n''')
test_gradient_descent()
| 60 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowerCAmelCase_ = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
lowerCAmelCase_ = parser.parse_args()
lowerCAmelCase_ = '''cpu'''
lowerCAmelCase_ = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
lowerCAmelCase_ = '''path-to-your-trained-model'''
lowerCAmelCase_ = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
lowerCAmelCase_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowerCAmelCase_ = pipe.to(device)
# to channels last
lowerCAmelCase_ = pipe.unet.to(memory_format=torch.channels_last)
lowerCAmelCase_ = pipe.vae.to(memory_format=torch.channels_last)
lowerCAmelCase_ = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
lowerCAmelCase_ = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
lowerCAmelCase_ = torch.randn(2, 4, 6_4, 6_4)
lowerCAmelCase_ = torch.rand(1) * 9_9_9
lowerCAmelCase_ = torch.randn(2, 7_7, 7_6_8)
lowerCAmelCase_ = (sample, timestep, encoder_hidden_status)
try:
lowerCAmelCase_ = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
lowerCAmelCase_ = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowerCAmelCase_ = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowerCAmelCase_ = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
lowerCAmelCase_ = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
lowerCAmelCase_ = 6_6_6
lowerCAmelCase_ = torch.Generator(device).manual_seed(seed)
lowerCAmelCase_ = {'''generator''': generator}
if args.steps is not None:
lowerCAmelCase_ = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
lowerCAmelCase_ = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 60 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
from manim import *
class __lowerCAmelCase ( _a ):
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
snake_case_ : List[str] = Rectangle(height=0.25 , width=0.25 )
snake_case_ : Any = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
snake_case_ : int = [mem.copy() for i in range(6 )]
snake_case_ : Dict = [mem.copy() for i in range(6 )]
snake_case_ : Optional[Any] = VGroup(*__magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : Any = VGroup(*__magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : Optional[Any] = VGroup(__magic_name__ , __magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : Optional[int] = Text('''CPU''' , font_size=24 )
snake_case_ : Tuple = Group(__magic_name__ , __magic_name__ ).arrange(__magic_name__ , buff=0.5 , aligned_edge=__magic_name__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__magic_name__ )
snake_case_ : Union[str, Any] = [mem.copy() for i in range(4 )]
snake_case_ : Dict = VGroup(*__magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : List[Any] = Text('''GPU''' , font_size=24 )
snake_case_ : Dict = Group(__magic_name__ , __magic_name__ ).arrange(__magic_name__ , buff=0.5 , aligned_edge=__magic_name__ )
gpu.move_to([-1, -1, 0] )
self.add(__magic_name__ )
snake_case_ : str = [mem.copy() for i in range(6 )]
snake_case_ : Tuple = VGroup(*__magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : str = Text('''Model''' , font_size=24 )
snake_case_ : Tuple = Group(__magic_name__ , __magic_name__ ).arrange(__magic_name__ , buff=0.5 , aligned_edge=__magic_name__ )
model.move_to([3, -1.0, 0] )
self.add(__magic_name__ )
snake_case_ : List[str] = []
snake_case_ : List[Any] = []
snake_case_ : str = []
for i, rect in enumerate(__magic_name__ ):
rect.set_stroke(__magic_name__ )
snake_case_ : Tuple = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__magic_name__ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__magic_name__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=__magic_name__ , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=__magic_name__ , buff=0.0 )
self.add(__magic_name__ )
model_cpu_arr.append(__magic_name__ )
self.add(*__magic_name__ , *__magic_name__ , *__magic_name__ )
snake_case_ : List[Any] = [mem.copy() for i in range(6 )]
snake_case_ : Optional[int] = VGroup(*__magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : Any = Text('''Loaded Checkpoint''' , font_size=24 )
snake_case_ : List[Any] = Group(__magic_name__ , __magic_name__ ).arrange(__magic_name__ , buff=0.5 , aligned_edge=__magic_name__ )
checkpoint.move_to([3, 0.5, 0] )
self.add(__magic_name__ )
snake_case_ : str = []
snake_case_ : Tuple = []
for i, rect in enumerate(__magic_name__ ):
snake_case_ : Optional[int] = fill.copy().set_fill(__magic_name__ , opacity=0.7 )
target.move_to(__magic_name__ )
ckpt_arr.append(__magic_name__ )
snake_case_ : Union[str, Any] = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(__magic_name__ )
self.add(*__magic_name__ , *__magic_name__ )
snake_case_ : Any = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
snake_case_ : Optional[Any] = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__magic_name__ , __magic_name__ )
snake_case_ : List[str] = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(__magic_name__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(__magic_name__ )
snake_case_ : Tuple = MarkupText(
F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
snake_case_ : Optional[Any] = [meta_mem.copy() for i in range(6 )]
snake_case_ : List[str] = [meta_mem.copy() for i in range(6 )]
snake_case_ : Union[str, Any] = VGroup(*__magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : Optional[Any] = VGroup(*__magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : List[Any] = VGroup(__magic_name__ , __magic_name__ ).arrange(__magic_name__ , buff=0 )
snake_case_ : Dict = Text('''Disk''' , font_size=24 )
snake_case_ : Union[str, Any] = Group(__magic_name__ , __magic_name__ ).arrange(__magic_name__ , buff=0.5 , aligned_edge=__magic_name__ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(__magic_name__ , run_time=3 ) , Write(__magic_name__ , run_time=1 ) , Create(__magic_name__ , run_time=1 ) )
snake_case_ : str = []
for i, rect in enumerate(__magic_name__ ):
snake_case_ : Any = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(__magic_name__ , run_time=1.5 ) )
self.play(*__magic_name__ )
self.play(FadeOut(__magic_name__ ) )
snake_case_ : Dict = MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(__magic_name__ , run_time=3 ) )
self.play(
FadeOut(__magic_name__ , __magic_name__ , *__magic_name__ , *__magic_name__ ) , )
self.wait()
| 60 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 1 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = GPTSwaTokenizer
lowerCamelCase_ : List[str] = False
lowerCamelCase_ : List[str] = True
lowerCamelCase_ : Union[str, Any] = False
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ : List[Any] = GPTSwaTokenizer(__magic_name__ , eos_token='''<unk>''' , bos_token='''<unk>''' , pad_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase (self , __magic_name__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[int] = '''This is a test'''
snake_case_ : Tuple = '''This is a test'''
return input_text, output_text
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : int = '''<s>'''
snake_case_ : int = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__ ) , __magic_name__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__ ) , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(__magic_name__ ) , 2000 )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 2000 )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Union[str, Any] = GPTSwaTokenizer(__magic_name__ )
snake_case_ : Optional[int] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__magic_name__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , [465, 287, 265, 631, 842] )
snake_case_ : int = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
# fmt: off
self.assertListEqual(
__magic_name__ , ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.'''] , )
# fmt: on
snake_case_ : Union[str, Any] = tokenizer.convert_tokens_to_ids(__magic_name__ )
self.assertListEqual(
__magic_name__ , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] , )
snake_case_ : Union[str, Any] = tokenizer.convert_ids_to_tokens(__magic_name__ )
# fmt: off
self.assertListEqual(
__magic_name__ , ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.'''] )
# fmt: on
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = GPTSwaTokenizer(__magic_name__ )
snake_case_ : Tuple = ['''This is a test''', '''I was born in 92000, and this is falsé.''']
snake_case_ : str = [
[465, 287, 265, 631, 842],
[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(__magic_name__ , __magic_name__ ):
self.assertListEqual(tokenizer.encode_fast(__magic_name__ ) , __magic_name__ )
# Test that decode_fast returns the input text
for text, token_ids in zip(__magic_name__ , __magic_name__ ):
self.assertEqual(tokenizer.decode_fast(__magic_name__ ) , __magic_name__ )
@slow
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : int = [
'''<|python|>def fibonacci(n)\n if n < 0:\n print(\'Incorrect input\')''',
'''Hey there, how are you doing this fine day?''',
'''This is a text with a trailing spaces followed by a dot .''',
'''Häj sväjs lillebrör! =)''',
'''Det är inget fel på Mr. Cool''',
]
# fmt: off
snake_case_ : Optional[Any] = {'''input_ids''': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__magic_name__ , model_name='''AI-Sweden/gpt-sw3-126m''' , sequences=__magic_name__ , )
| 60 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
| 60 | 1 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_821 * temperature) / (volume) ) )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
return round(float((moles * 0.0_821 * temperature) / (pressure) ) )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
return round(float((pressure * volume) / (0.0_821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = (DDIMParallelScheduler,)
lowerCamelCase_ : Tuple = (('''eta''', 0.0), ('''num_inference_steps''', 50))
def lowerCamelCase (self , **__magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''clip_sample''': True,
}
config.update(**__magic_name__ )
return config
def lowerCamelCase (self , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.scheduler_classes[0]
snake_case_ : Tuple = self.get_scheduler_config(**__magic_name__ )
snake_case_ : List[Any] = scheduler_class(**__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = 10, 0.0
snake_case_ : Union[str, Any] = self.dummy_model()
snake_case_ : Tuple = self.dummy_sample_deter
scheduler.set_timesteps(__magic_name__ )
for t in scheduler.timesteps:
snake_case_ : Optional[int] = model(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ).prev_sample
return sample
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=__magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=__magic_name__ )
snake_case_ : int = self.scheduler_classes[0]
snake_case_ : List[str] = self.get_scheduler_config(steps_offset=1 )
snake_case_ : Optional[int] = scheduler_class(**__magic_name__ )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=__magic_name__ , beta_end=__magic_name__ )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__magic_name__ )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__magic_name__ )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=__magic_name__ )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=__magic_name__ )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
self.check_over_configs(thresholding=__magic_name__ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=__magic_name__ , prediction_type=__magic_name__ , sample_max_value=__magic_name__ , )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
for t in [1, 10, 49]:
self.check_over_forward(time_step=__magic_name__ )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=__magic_name__ , num_inference_steps=__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=__magic_name__ , eta=__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.scheduler_classes[0]
snake_case_ : List[Any] = self.get_scheduler_config()
snake_case_ : Optional[Any] = scheduler_class(**__magic_name__ )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.14_771 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.32_460 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.00_979 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.02 ) ) < 1e-5
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Any = self.scheduler_classes[0]
snake_case_ : int = self.get_scheduler_config()
snake_case_ : List[str] = scheduler_class(**__magic_name__ )
snake_case_ , snake_case_ : Any = 10, 0.0
scheduler.set_timesteps(__magic_name__ )
snake_case_ : List[Any] = self.dummy_model()
snake_case_ : Tuple = self.dummy_sample_deter
snake_case_ : Optional[Any] = self.dummy_sample_deter + 0.1
snake_case_ : Optional[Any] = self.dummy_sample_deter - 0.1
snake_case_ : str = samplea.shape[0]
snake_case_ : Any = torch.stack([samplea, samplea, samplea] , dim=0 )
snake_case_ : Any = torch.arange(__magic_name__ )[0:3, None].repeat(1 , __magic_name__ )
snake_case_ : int = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
snake_case_ : List[Any] = scheduler.batch_step_no_noise(__magic_name__ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , __magic_name__ )
snake_case_ : str = torch.sum(torch.abs(__magic_name__ ) )
snake_case_ : Optional[int] = torch.mean(torch.abs(__magic_name__ ) )
assert abs(result_sum.item() - 1_147.7_904 ) < 1e-2
assert abs(result_mean.item() - 0.4_982 ) < 1e-3
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = self.full_loop()
snake_case_ : Optional[Any] = torch.sum(torch.abs(__magic_name__ ) )
snake_case_ : Tuple = torch.mean(torch.abs(__magic_name__ ) )
assert abs(result_sum.item() - 172.0_067 ) < 1e-2
assert abs(result_mean.item() - 0.223_967 ) < 1e-3
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.full_loop(prediction_type='''v_prediction''' )
snake_case_ : Optional[int] = torch.sum(torch.abs(__magic_name__ ) )
snake_case_ : List[str] = torch.mean(torch.abs(__magic_name__ ) )
assert abs(result_sum.item() - 52.5_302 ) < 1e-2
assert abs(result_mean.item() - 0.0_684 ) < 1e-3
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.full_loop(set_alpha_to_one=__magic_name__ , beta_start=0.01 )
snake_case_ : Optional[Any] = torch.sum(torch.abs(__magic_name__ ) )
snake_case_ : Dict = torch.mean(torch.abs(__magic_name__ ) )
assert abs(result_sum.item() - 149.8_295 ) < 1e-2
assert abs(result_mean.item() - 0.1_951 ) < 1e-3
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = self.full_loop(set_alpha_to_one=__magic_name__ , beta_start=0.01 )
snake_case_ : int = torch.sum(torch.abs(__magic_name__ ) )
snake_case_ : List[str] = torch.mean(torch.abs(__magic_name__ ) )
assert abs(result_sum.item() - 149.0_784 ) < 1e-2
assert abs(result_mean.item() - 0.1_941 ) < 1e-3
| 60 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : int = CycleDiffusionPipeline
lowerCamelCase_ : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'''negative_prompt''',
'''height''',
'''width''',
'''negative_prompt_embeds''',
}
lowerCamelCase_ : Union[str, Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
lowerCamelCase_ : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''} )
lowerCamelCase_ : Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase_ : List[str] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : Union[str, 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 , )
snake_case_ : Dict = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , num_train_timesteps=1000 , clip_sample=__magic_name__ , set_alpha_to_one=__magic_name__ , )
torch.manual_seed(0 )
snake_case_ : Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case_ : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case_ : Optional[Any] = CLIPTextModel(__magic_name__ )
snake_case_ : Union[str, Any] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
snake_case_ : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowerCamelCase (self , __magic_name__ , __magic_name__=0 ) -> Dict:
'''simple docstring'''
snake_case_ : str = floats_tensor((1, 3, 32, 32) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
snake_case_ : Union[str, Any] = image / 2 + 0.5
if str(__magic_name__ ).startswith('''mps''' ):
snake_case_ : int = torch.manual_seed(__magic_name__ )
else:
snake_case_ : Optional[int] = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
snake_case_ : List[str] = {
'''prompt''': '''An astronaut riding an elephant''',
'''source_prompt''': '''An astronaut riding a horse''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''eta''': 0.1,
'''strength''': 0.8,
'''guidance_scale''': 3,
'''source_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Optional[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ : str = self.get_dummy_components()
snake_case_ : Union[str, Any] = CycleDiffusionPipeline(**__magic_name__ )
snake_case_ : Optional[Any] = pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : Optional[Any] = self.get_dummy_inputs(__magic_name__ )
snake_case_ : int = pipe(**__magic_name__ )
snake_case_ : Dict = output.images
snake_case_ : Tuple = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ : List[str] = np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.get_dummy_components()
for name, module in components.items():
if hasattr(__magic_name__ , '''half''' ):
snake_case_ : Optional[int] = module.half()
snake_case_ : str = CycleDiffusionPipeline(**__magic_name__ )
snake_case_ : Any = pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : List[str] = self.get_dummy_inputs(__magic_name__ )
snake_case_ : int = pipe(**__magic_name__ )
snake_case_ : Union[str, Any] = output.images
snake_case_ : Dict = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ : Optional[int] = np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return super().test_save_load_local()
@unittest.skip('''non-deterministic pipeline''' )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return super().test_inference_batch_single_identical()
@skip_mps
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return super().test_save_load_optional_components()
@skip_mps
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ : Any = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''' )
snake_case_ : Dict = init_image.resize((512, 512) )
snake_case_ : Optional[int] = '''CompVis/stable-diffusion-v1-4'''
snake_case_ : List[str] = DDIMScheduler.from_pretrained(__magic_name__ , subfolder='''scheduler''' )
snake_case_ : Optional[int] = CycleDiffusionPipeline.from_pretrained(
__magic_name__ , scheduler=__magic_name__ , safety_checker=__magic_name__ , torch_dtype=torch.floataa , revision='''fp16''' )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
pipe.enable_attention_slicing()
snake_case_ : List[Any] = '''A black colored car'''
snake_case_ : int = '''A blue colored car'''
snake_case_ : int = torch.manual_seed(0 )
snake_case_ : List[Any] = pipe(
prompt=__magic_name__ , source_prompt=__magic_name__ , image=__magic_name__ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=__magic_name__ , output_type='''np''' , )
snake_case_ : List[Any] = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ : str = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''' )
snake_case_ : Union[str, Any] = init_image.resize((512, 512) )
snake_case_ : Optional[int] = '''CompVis/stable-diffusion-v1-4'''
snake_case_ : Optional[int] = DDIMScheduler.from_pretrained(__magic_name__ , subfolder='''scheduler''' )
snake_case_ : int = CycleDiffusionPipeline.from_pretrained(__magic_name__ , scheduler=__magic_name__ , safety_checker=__magic_name__ )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
pipe.enable_attention_slicing()
snake_case_ : Any = '''A black colored car'''
snake_case_ : List[Any] = '''A blue colored car'''
snake_case_ : Any = torch.manual_seed(0 )
snake_case_ : Optional[Any] = pipe(
prompt=__magic_name__ , source_prompt=__magic_name__ , image=__magic_name__ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=__magic_name__ , output_type='''np''' , )
snake_case_ : Dict = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 60 |
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 __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
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 lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
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
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : str = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCamelCase_ ( _UpperCamelCase ) -> dict[str, str]:
"""simple docstring"""
snake_case_ : Tuple = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
snake_case_ : Optional[Any] = remove_duplicates(key.upper() )
snake_case_ : str = len(_UpperCamelCase )
# First fill cipher with key characters
snake_case_ : Any = {alphabet[i]: char for i, char in enumerate(_UpperCamelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_UpperCamelCase ) , 26 ):
snake_case_ : str = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
snake_case_ : Union[str, Any] = alphabet[i - offset]
snake_case_ : Optional[Any] = char
return cipher_alphabet
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
return "".join(cipher_map.get(_UpperCamelCase , _UpperCamelCase ) for ch in message.upper() )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : List[Any] = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_UpperCamelCase , _UpperCamelCase ) for ch in message.upper() )
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
snake_case_ : str = input('''Enter message to encode or decode: ''' ).strip()
snake_case_ : int = input('''Enter keyword: ''' ).strip()
snake_case_ : Optional[Any] = input('''Encipher or decipher? E/D:''' ).strip()[0].lower()
try:
snake_case_ : Optional[int] = {'''e''': encipher, '''d''': decipher}[option]
except KeyError:
raise KeyError('''invalid input option''' )
snake_case_ : Optional[int] = create_cipher_map(_UpperCamelCase )
print(func(_UpperCamelCase , _UpperCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 60 |
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_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''UperNetForSemanticSegmentation''',
'''UperNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 1 |
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 __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=10 , __magic_name__=3 , __magic_name__=2 , __magic_name__=2 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__="divided_space_time" , __magic_name__=None , ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Union[str, Any] = image_size
snake_case_ : Tuple = num_channels
snake_case_ : int = patch_size
snake_case_ : str = num_frames
snake_case_ : str = is_training
snake_case_ : Dict = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Dict = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : int = hidden_act
snake_case_ : int = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : Tuple = attention_type
snake_case_ : Any = initializer_range
snake_case_ : Optional[int] = scope
snake_case_ : List[str] = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : List[str] = (num_frames) * self.num_patches_per_frame + 1
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[str] = None
if self.use_labels:
snake_case_ : Dict = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ : List[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : 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 , )
snake_case_ : Tuple = self.num_labels
return config
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Dict = TimesformerModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = TimesformerForVideoClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Union[str, Any] = model(__magic_name__ )
# verify the logits shape
snake_case_ : Tuple = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Union[str, Any] = config_and_inputs
snake_case_ : str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : Optional[int] = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowerCamelCase_ : Union[str, Any] = (
{'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : List[str] = False
lowerCamelCase_ : List[Any] = False
lowerCamelCase_ : Dict = False
lowerCamelCase_ : List[Any] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = TimesformerModelTester(self )
snake_case_ : Tuple = ConfigTester(
self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=False ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = copy.deepcopy(__magic_name__ )
if return_labels:
if model_class in get_values(__magic_name__ ):
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__magic_name__ )
return inputs_dict
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ , snake_case_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ , snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Optional[int] = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : List[str] = [*signature.parameters.keys()]
snake_case_ : str = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> int:
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : Optional[Any] = TimesformerModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
if not self.has_attentions:
pass
else:
snake_case_ , snake_case_ : int = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Union[str, Any] = True
for model_class in self.all_model_classes:
snake_case_ : List[Any] = self.model_tester.seq_length
snake_case_ : List[Any] = self.model_tester.num_frames
snake_case_ : List[str] = True
snake_case_ : Union[str, Any] = False
snake_case_ : List[str] = True
snake_case_ : List[Any] = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
snake_case_ : Optional[Any] = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
snake_case_ : Optional[Any] = outputs.attentions
self.assertEqual(len(__magic_name__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ : Dict = True
snake_case_ : Optional[int] = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
snake_case_ : List[Any] = outputs.attentions
self.assertEqual(len(__magic_name__ ) , 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] , )
snake_case_ : Dict = len(__magic_name__ )
# Check attention is always last and order is fine
snake_case_ : Dict = True
snake_case_ : Any = True
snake_case_ : int = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
snake_case_ : Dict = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
self.assertEqual(out_len + 1 , len(__magic_name__ ) )
snake_case_ : Optional[Any] = outputs.attentions
self.assertEqual(len(__magic_name__ ) , 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 lowerCamelCase (self ) -> str:
'''simple docstring'''
def check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ ):
snake_case_ : List[Any] = model_class(__magic_name__ )
model.to(__magic_name__ )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(__magic_name__ , __magic_name__ ) )
snake_case_ : str = outputs.hidden_states
snake_case_ : List[Any] = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__magic_name__ ) , __magic_name__ )
snake_case_ : Optional[Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
snake_case_ , snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Optional[Any] = True
check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ : List[str] = True
check_hidden_states_output(__magic_name__ , __magic_name__ , __magic_name__ )
def lowerCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
snake_case_ : Dict = np.load(_UpperCamelCase )
return list(_UpperCamelCase )
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
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 lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__magic_name__ )
snake_case_ : List[str] = self.default_image_processor
snake_case_ : List[str] = prepare_video()
snake_case_ : int = image_processor(video[:8] , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
# verify the logits
snake_case_ : Any = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : Any = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''falcon'''
lowerCamelCase_ : Any = ['''past_key_values''']
def __init__(self , __magic_name__=6_5024 , __magic_name__=4544 , __magic_name__=32 , __magic_name__=71 , __magic_name__=1e-5 , __magic_name__=0.02 , __magic_name__=True , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=None , __magic_name__=False , __magic_name__=False , __magic_name__=True , __magic_name__=True , __magic_name__=False , __magic_name__=11 , __magic_name__=11 , **__magic_name__ , ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Optional[Any] = vocab_size
# Backward compatibility with n_embed kwarg
snake_case_ : Dict = kwargs.pop('''n_embed''' , __magic_name__ )
snake_case_ : Optional[int] = hidden_size if n_embed is None else n_embed
snake_case_ : List[str] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Optional[Any] = layer_norm_epsilon
snake_case_ : List[str] = initializer_range
snake_case_ : List[str] = use_cache
snake_case_ : Optional[int] = hidden_dropout
snake_case_ : List[Any] = attention_dropout
snake_case_ : Any = bos_token_id
snake_case_ : Dict = eos_token_id
snake_case_ : Optional[Any] = num_attention_heads if num_kv_heads is None else num_kv_heads
snake_case_ : Optional[Any] = alibi
snake_case_ : Optional[int] = new_decoder_architecture
snake_case_ : str = multi_query # Ignored when new_decoder_architecture is True
snake_case_ : List[str] = parallel_attn
snake_case_ : List[str] = bias
super().__init__(bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
@property
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
return self.hidden_size // self.num_attention_heads
@property
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
return not self.alibi
| 60 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def lowerCamelCase_ ( _UpperCamelCase ) -> List[str]:
"""simple docstring"""
if not is_accelerate_available():
return method
snake_case_ : Any = version.parse(accelerate.__version__ ).base_version
if version.parse(_UpperCamelCase ) < version.parse('''0.17.0''' ):
return method
def wrapper(self , *_UpperCamelCase , **_UpperCamelCase ):
if hasattr(self , '''_hf_hook''' ) and hasattr(self._hf_hook , '''pre_forward''' ):
self._hf_hook.pre_forward(self )
return method(self , *_UpperCamelCase , **_UpperCamelCase )
return wrapper
| 60 |
import unittest
import numpy as np
from datasets import load_dataset
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 BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if "model" in orig_key:
snake_case_ : Optional[Any] = orig_key.replace('''model.''' , '''''' )
if "norm1" in orig_key:
snake_case_ : Optional[int] = orig_key.replace('''norm1''' , '''attention.output.LayerNorm''' )
if "norm2" in orig_key:
snake_case_ : Optional[int] = orig_key.replace('''norm2''' , '''output.LayerNorm''' )
if "norm" in orig_key:
snake_case_ : List[str] = orig_key.replace('''norm''' , '''LayerNorm''' )
if "transformer" in orig_key:
snake_case_ : List[str] = orig_key.split('''.''' )[0].split('''_''' )[-1]
snake_case_ : Optional[Any] = orig_key.replace(f'''transformer_{layer_num}''' , f'''encoder.layer.{layer_num}''' )
if "mha.attn" in orig_key:
snake_case_ : Dict = orig_key.replace('''mha.attn''' , '''attention.self''' )
if "mha" in orig_key:
snake_case_ : Any = orig_key.replace('''mha''' , '''attention''' )
if "W_q" in orig_key:
snake_case_ : Tuple = orig_key.replace('''W_q''' , '''self.query''' )
if "W_k" in orig_key:
snake_case_ : List[str] = orig_key.replace('''W_k''' , '''self.key''' )
if "W_v" in orig_key:
snake_case_ : Optional[int] = orig_key.replace('''W_v''' , '''self.value''' )
if "ff1" in orig_key:
snake_case_ : Any = orig_key.replace('''ff1''' , '''intermediate.dense''' )
if "ff2" in orig_key:
snake_case_ : Dict = orig_key.replace('''ff2''' , '''output.dense''' )
if "ff" in orig_key:
snake_case_ : Union[str, Any] = orig_key.replace('''ff''' , '''output.dense''' )
if "mlm_class" in orig_key:
snake_case_ : Optional[Any] = orig_key.replace('''mlm.mlm_class''' , '''cls.predictions.decoder''' )
if "mlm" in orig_key:
snake_case_ : Dict = orig_key.replace('''mlm''' , '''cls.predictions.transform''' )
if "cls" not in orig_key:
snake_case_ : Optional[int] = '''yoso.''' + orig_key
return orig_key
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
snake_case_ : Union[str, Any] = orig_state_dict.pop(_UpperCamelCase )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
snake_case_ : Dict = val
snake_case_ : int = orig_state_dict['''cls.predictions.decoder.bias''']
snake_case_ : Optional[int] = torch.arange(_UpperCamelCase ).expand((1, -1) ) + 2
return orig_state_dict
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
snake_case_ : Tuple = torch.load(_UpperCamelCase , map_location='''cpu''' )['''model_state_dict''']
snake_case_ : Union[str, Any] = YosoConfig.from_json_file(_UpperCamelCase )
snake_case_ : Tuple = YosoForMaskedLM(_UpperCamelCase )
snake_case_ : Any = convert_checkpoint_helper(config.max_position_embeddings , _UpperCamelCase )
print(model.load_state_dict(_UpperCamelCase ) )
model.eval()
model.save_pretrained(_UpperCamelCase )
print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The json file for YOSO model config.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowerCAmelCase_ = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 60 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@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}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCAmelCase_ = TypeVar('''KT''')
lowerCAmelCase_ = TypeVar('''VT''')
class __lowerCAmelCase ( Generic[KT, VT] ):
def __init__(self , __magic_name__ = "root" , __magic_name__ = None ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = key
snake_case_ : Dict = value
snake_case_ : list[Node[KT, VT]] = []
def __repr__(self ) -> str:
'''simple docstring'''
return F'''Node({self.key}: {self.value})'''
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return len(self.forward )
class __lowerCAmelCase ( Generic[KT, VT] ):
def __init__(self , __magic_name__ = 0.5 , __magic_name__ = 16 ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Node[KT, VT] = Node[KT, VT]()
snake_case_ : Dict = 0
snake_case_ : Any = p
snake_case_ : Optional[Any] = max_level
def __str__(self ) -> str:
'''simple docstring'''
snake_case_ : Dict = list(self )
if len(__magic_name__ ) == 0:
return F'''SkipList(level={self.level})'''
snake_case_ : int = max((len(str(__magic_name__ ) ) for item in items) , default=4 )
snake_case_ : Optional[Any] = max(__magic_name__ , 4 ) + 4
snake_case_ : Union[str, Any] = self.head
snake_case_ : Any = []
snake_case_ : int = node.forward.copy()
lines.append(F'''[{node.key}]'''.ljust(__magic_name__ , '''-''' ) + '''* ''' * len(__magic_name__ ) )
lines.append(''' ''' * label_size + '''| ''' * len(__magic_name__ ) )
while len(node.forward ) != 0:
snake_case_ : List[str] = node.forward[0]
lines.append(
F'''[{node.key}]'''.ljust(__magic_name__ , '''-''' )
+ ''' '''.join(str(n.key ) if n.key == node.key else '''|''' for n in forwards ) )
lines.append(''' ''' * label_size + '''| ''' * len(__magic_name__ ) )
snake_case_ : List[Any] = node.forward
lines.append('''None'''.ljust(__magic_name__ ) + '''* ''' * len(__magic_name__ ) )
return F'''SkipList(level={self.level})\n''' + "\n".join(__magic_name__ )
def __iter__(self ) -> str:
'''simple docstring'''
snake_case_ : int = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
snake_case_ : List[str] = node.forward[0]
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Optional[int] = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def lowerCamelCase (self , __magic_name__ ) -> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
'''simple docstring'''
snake_case_ : List[Any] = []
snake_case_ : Union[str, Any] = 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:
snake_case_ : List[str] = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(__magic_name__ )
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 lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
snake_case_ , snake_case_ : Union[str, Any] = self._locate_node(__magic_name__ )
if node is not None:
for i, update_node in enumerate(__magic_name__ ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
snake_case_ : List[Any] = node.forward[i]
else:
snake_case_ : Any = update_node.forward[:i]
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> Dict:
'''simple docstring'''
snake_case_ , snake_case_ : int = self._locate_node(__magic_name__ )
if node is not None:
snake_case_ : Optional[int] = value
else:
snake_case_ : List[str] = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , __magic_name__ ):
update_vector.append(self.head )
snake_case_ : List[str] = level
snake_case_ : Any = Node(__magic_name__ , __magic_name__ )
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(__magic_name__ )
else:
snake_case_ : Tuple = new_node
def lowerCamelCase (self , __magic_name__ ) -> VT | None:
'''simple docstring'''
snake_case_ , snake_case_ : int = self._locate_node(__magic_name__ )
if node is not None:
return node.value
return None
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
snake_case_ : int = SkipList()
skip_list.insert('''Key1''' , 3 )
skip_list.insert('''Key2''' , 12 )
skip_list.insert('''Key3''' , 41 )
skip_list.insert('''Key4''' , -19 )
snake_case_ : Optional[Any] = skip_list.head
snake_case_ : Optional[int] = {}
while node.level != 0:
snake_case_ : Optional[Any] = node.forward[0]
snake_case_ : Union[str, Any] = node.value
assert len(_UpperCamelCase ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : List[Any] = 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 )
snake_case_ : str = skip_list.head
snake_case_ : Tuple = {}
while node.level != 0:
snake_case_ : List[str] = node.forward[0]
snake_case_ : Union[str, Any] = node.value
if len(_UpperCamelCase ) != 4:
print()
assert len(_UpperCamelCase ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def lowerCamelCase_ ( ) -> int:
"""simple docstring"""
snake_case_ : Optional[Any] = SkipList()
assert skip_list.find('''Some key''' ) is None
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
snake_case_ : List[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 lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : List[Any] = SkipList()
skip_list.delete('''Some key''' )
assert len(skip_list.head.forward ) == 0
def lowerCamelCase_ ( ) -> int:
"""simple docstring"""
snake_case_ : Union[str, Any] = 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 lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ : Optional[int] = 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 lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : int = SkipList()
skip_list.insert('''Key1''' , 12 )
skip_list.insert('''V''' , 13 )
skip_list.insert('''X''' , 142 )
skip_list.insert('''Key2''' , 15 )
skip_list.delete('''X''' )
def traverse_keys(_UpperCamelCase ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(_UpperCamelCase )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
def is_sorted(_UpperCamelCase ):
return all(next_item >= item for item, next_item in zip(_UpperCamelCase , lst[1:] ) )
snake_case_ : str = SkipList()
for i in range(10 ):
skip_list.insert(_UpperCamelCase , _UpperCamelCase )
assert is_sorted(list(_UpperCamelCase ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(_UpperCamelCase ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(_UpperCamelCase ) )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
for _ in range(100 ):
# 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 lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Optional[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(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 60 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
'''SpeechT5Config''',
'''SpeechT5HifiGanConfig''',
],
'''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
'''processing_speecht5''': ['''SpeechT5Processor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''SpeechT5Tokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SpeechT5ForSpeechToText''',
'''SpeechT5ForSpeechToSpeech''',
'''SpeechT5ForTextToSpeech''',
'''SpeechT5Model''',
'''SpeechT5PreTrainedModel''',
'''SpeechT5HifiGan''',
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
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 lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} 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() ) )
| 60 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 1_6
lowerCAmelCase_ = 3_2
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase = 16 ) -> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
snake_case_ : List[str] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_UpperCamelCase ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Optional[Any] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_UpperCamelCase , max_length=_UpperCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : List[Any] = datasets.map(
_UpperCamelCase , batched=_UpperCamelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : Any = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_UpperCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : Dict = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Union[str, Any] = 16
elif accelerator.mixed_precision != "no":
snake_case_ : Union[str, Any] = 8
else:
snake_case_ : Optional[int] = None
return tokenizer.pad(
_UpperCamelCase , padding='''longest''' , max_length=_UpperCamelCase , pad_to_multiple_of=_UpperCamelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
snake_case_ : List[Any] = DataLoader(
tokenized_datasets['''train'''] , shuffle=_UpperCamelCase , collate_fn=_UpperCamelCase , batch_size=_UpperCamelCase )
snake_case_ : List[Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_UpperCamelCase , collate_fn=_UpperCamelCase , batch_size=_UpperCamelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCAmelCase_ = mocked_dataloaders # noqa: F811
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , _UpperCamelCase ) == "1":
snake_case_ : Tuple = 2
# New Code #
snake_case_ : Optional[Any] = int(args.gradient_accumulation_steps )
snake_case_ : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
snake_case_ : Optional[Any] = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_UpperCamelCase )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : Union[str, Any] = config['''lr''']
snake_case_ : Any = int(config['''num_epochs'''] )
snake_case_ : Dict = int(config['''seed'''] )
snake_case_ : int = int(config['''batch_size'''] )
snake_case_ : Tuple = evaluate.load('''glue''' , '''mrpc''' )
set_seed(_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = get_dataloaders(_UpperCamelCase , _UpperCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Any = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=_UpperCamelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Any = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : Tuple = AdamW(params=model.parameters() , lr=_UpperCamelCase )
# Instantiate scheduler
snake_case_ : Any = get_linear_schedule_with_warmup(
optimizer=_UpperCamelCase , num_warmup_steps=100 , num_training_steps=(len(_UpperCamelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ : Union[str, Any] = accelerator.prepare(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Now we train the model
for epoch in range(_UpperCamelCase ):
model.train()
with LocalSGD(
accelerator=_UpperCamelCase , model=_UpperCamelCase , local_sgd_steps=_UpperCamelCase , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(_UpperCamelCase ):
snake_case_ : Union[str, Any] = model(**_UpperCamelCase )
snake_case_ : int = output.loss
accelerator.backward(_UpperCamelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(_UpperCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Dict = model(**_UpperCamelCase )
snake_case_ : Any = outputs.logits.argmax(dim=-1 )
snake_case_ , snake_case_ : int = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_UpperCamelCase , references=_UpperCamelCase , )
snake_case_ : List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , _UpperCamelCase )
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=_UpperCamelCase , default=_UpperCamelCase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' , type=_UpperCamelCase , default=1 , help='''The number of minibatches to be ran before gradients are accumulated.''' , )
parser.add_argument(
'''--local_sgd_steps''' , type=_UpperCamelCase , default=8 , help='''Number of local SGD steps or None to disable local SGD''' )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
snake_case_ : str = parser.parse_args()
snake_case_ : str = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
main()
| 60 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileBertConfig''',
'''MobileBertOnnxConfig''',
],
'''tokenization_mobilebert''': ['''MobileBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''MobileBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileBertForMaskedLM''',
'''MobileBertForMultipleChoice''',
'''MobileBertForNextSentencePrediction''',
'''MobileBertForPreTraining''',
'''MobileBertForQuestionAnswering''',
'''MobileBertForSequenceClassification''',
'''MobileBertForTokenClassification''',
'''MobileBertLayer''',
'''MobileBertModel''',
'''MobileBertPreTrainedModel''',
'''load_tf_weights_in_mobilebert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFMobileBertForMaskedLM''',
'''TFMobileBertForMultipleChoice''',
'''TFMobileBertForNextSentencePrediction''',
'''TFMobileBertForPreTraining''',
'''TFMobileBertForQuestionAnswering''',
'''TFMobileBertForSequenceClassification''',
'''TFMobileBertForTokenClassification''',
'''TFMobileBertMainLayer''',
'''TFMobileBertModel''',
'''TFMobileBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 1 |
def __lowercase ( snake_case ):
"""simple docstring"""
if not isinstance(snake_case, snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1, input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 0 |
import re
from filelock import FileLock
try:
import nltk
__snake_case = True
except (ImportError, ModuleNotFoundError):
__snake_case = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _A ( _lowercase ) -> str:
"""simple docstring"""
re.sub('<n>' , '' , _lowercase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_lowercase ) )
| 1 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase__ :
"""simple docstring"""
@staticmethod
def snake_case_ ( *__lowerCAmelCase : List[str] , **__lowerCAmelCase : Tuple ) -> List[Any]:
pass
@is_pipeline_test
@require_torch
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
a__ : Tuple = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def snake_case_ ( self : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[str] ) -> int:
_A = pipeline('''visual-question-answering''' , model='''hf-internal-testing/tiny-vilt-random-vqa''' )
_A = [
{
'''image''': Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''question''': '''How many cats are there?''',
},
{
'''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
'''question''': '''How many cats are there?''',
},
]
return vqa_pipeline, examples
def snake_case_ ( self : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple ) -> Tuple:
_A = vqa_pipeline(__lowerCAmelCase , top_k=1 )
self.assertEqual(
__lowerCAmelCase , [
[{'''score''': ANY(__lowerCAmelCase ), '''answer''': ANY(__lowerCAmelCase )}],
[{'''score''': ANY(__lowerCAmelCase ), '''answer''': ANY(__lowerCAmelCase )}],
] , )
@require_torch
def snake_case_ ( self : Dict ) -> Any:
_A = pipeline('''visual-question-answering''' , model='''hf-internal-testing/tiny-vilt-random-vqa''' )
_A = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
_A = '''How many cats are there?'''
_A = vqa_pipeline(image=__lowerCAmelCase , question='''How many cats are there?''' , top_k=2 )
self.assertEqual(
__lowerCAmelCase , [{'''score''': ANY(__lowerCAmelCase ), '''answer''': ANY(__lowerCAmelCase )}, {'''score''': ANY(__lowerCAmelCase ), '''answer''': ANY(__lowerCAmelCase )}] )
_A = vqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
__lowerCAmelCase , [{'''score''': ANY(__lowerCAmelCase ), '''answer''': ANY(__lowerCAmelCase )}, {'''score''': ANY(__lowerCAmelCase ), '''answer''': ANY(__lowerCAmelCase )}] )
@slow
@require_torch
def snake_case_ ( self : Dict ) -> List[str]:
_A = pipeline('''visual-question-answering''' , model='''dandelin/vilt-b32-finetuned-vqa''' )
_A = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
_A = '''How many cats are there?'''
_A = vqa_pipeline(image=__lowerCAmelCase , question=__lowerCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}] )
_A = vqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}] )
_A = vqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [[{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}]] * 2 , )
@require_tf
@unittest.skip('''Visual question answering not implemented in TF''' )
def snake_case_ ( self : Union[str, Any] ) -> List[Any]:
pass
| 2 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase : List[str] = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def A_( ):
UpperCamelCase = Github(os.environ['GITHUB_TOKEN'])
UpperCamelCase = g.get_repo('huggingface/diffusers')
UpperCamelCase = repo.get_issues(state='open')
for issue in open_issues:
UpperCamelCase = sorted(issue.get_comments() , key=lambda A: i.created_at , reverse=A)
UpperCamelCase = comments[0] if len(A) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='closed')
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='open')
issue.remove_from_labels('stale')
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.')
issue.add_to_labels('stale')
if __name__ == "__main__":
main()
| 3 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 0 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _SCREAMING_SNAKE_CASE ():
lowerCAmelCase = 2
while True:
if is_prime(_UpperCAmelCase ):
yield num
num += 1
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 200_0000 ):
return sum(takewhile(lambda _UpperCAmelCase : x < n , prime_generator() ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 4 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 0 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_lowercase = logging.get_logger(__name__)
def A (__lowerCamelCase :List[str] ):
_lowerCAmelCase = r"""\w+[.]\d+"""
_lowerCAmelCase = re.findall(__lowerCamelCase , __lowerCamelCase )
for pat in pats:
_lowerCAmelCase = key.replace(__lowerCamelCase , """_""".join(pat.split(""".""" ) ) )
return key
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Any , __lowerCamelCase :int ):
_lowerCAmelCase = pt_tuple_key[:-1] + ("""scale""",)
if (
any("""norm""" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
_lowerCAmelCase = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
_lowerCAmelCase = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
_lowerCAmelCase = pt_tuple_key[:-1] + ("""embedding""",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
_lowerCAmelCase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
_lowerCAmelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_lowerCAmelCase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight":
_lowerCAmelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_lowerCAmelCase = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_lowerCAmelCase = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def A (__lowerCamelCase :Any , __lowerCamelCase :int , __lowerCamelCase :Dict=42 ):
# Step 1: Convert pytorch tensor to numpy
_lowerCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
_lowerCAmelCase = flax_model.init_weights(PRNGKey(__lowerCamelCase ) )
_lowerCAmelCase = flatten_dict(__lowerCamelCase )
_lowerCAmelCase = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_lowerCAmelCase = rename_key(__lowerCamelCase )
_lowerCAmelCase = tuple(renamed_pt_key.split(""".""" ) )
# Correctly rename weight parameters
_lowerCAmelCase , _lowerCAmelCase = rename_key_and_reshape_tensor(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# also add unexpected weight so that warning is thrown
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
| 5 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'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 UpperCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase_ = "nat"
lowerCamelCase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self :List[Any] , __A :Union[str, Any]=4 , __A :Dict=3 , __A :str=64 , __A :Optional[int]=[3, 4, 6, 5] , __A :Tuple=[2, 4, 8, 16] , __A :List[str]=7 , __A :Optional[Any]=3.0 , __A :Tuple=True , __A :Tuple=0.0 , __A :Dict=0.0 , __A :Tuple=0.1 , __A :str="gelu" , __A :Tuple=0.0_2 , __A :str=1E-5 , __A :Tuple=0.0 , __A :List[str]=None , __A :Optional[Any]=None , **__A :Optional[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(**__A )
SCREAMING_SNAKE_CASE__ = patch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = embed_dim
SCREAMING_SNAKE_CASE__ = depths
SCREAMING_SNAKE_CASE__ = len(__A )
SCREAMING_SNAKE_CASE__ = num_heads
SCREAMING_SNAKE_CASE__ = kernel_size
SCREAMING_SNAKE_CASE__ = mlp_ratio
SCREAMING_SNAKE_CASE__ = qkv_bias
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = drop_path_rate
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = 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
SCREAMING_SNAKE_CASE__ = int(embed_dim * 2 ** (len(__A ) - 1) )
SCREAMING_SNAKE_CASE__ = layer_scale_init_value
SCREAMING_SNAKE_CASE__ = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(__A ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_aligned_output_features_output_indices(
out_features=__A , out_indices=__A , stage_names=self.stage_names ) | 6 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
| 60 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ('foo.json',)] )
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : str ):
_A = GenerationConfig(
do_sample=_UpperCAmelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_UpperCAmelCase , config_name=_UpperCAmelCase )
_A = GenerationConfig.from_pretrained(_UpperCAmelCase , config_name=_UpperCAmelCase )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , _UpperCAmelCase )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = AutoConfig.from_pretrained('gpt2' )
_A = GenerationConfig.from_model_config(_UpperCAmelCase )
_A = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(_UpperCAmelCase , _UpperCAmelCase )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def lowerCAmelCase_ ( self : Dict ):
_A = GenerationConfig()
_A = {
'max_new_tokens': 1_024,
'foo': 'bar',
}
_A = copy.deepcopy(_UpperCAmelCase )
_A = generation_config.update(**_UpperCAmelCase )
# update_kwargs was not modified (no side effects)
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1_024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(_UpperCAmelCase , {'foo': 'bar'} )
def lowerCAmelCase_ ( self : str ):
_A = GenerationConfig()
_A = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(_UpperCAmelCase )
_A = GenerationConfig.from_pretrained(_UpperCAmelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
_A = GenerationConfig.from_model_config(_UpperCAmelCase )
assert not hasattr(_UpperCAmelCase , 'foo' ) # no new kwargs should be initialized if from config
def lowerCAmelCase_ ( self : Any ):
_A = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , _UpperCAmelCase )
self.assertEqual(default_config.num_beams , 1 )
_A = GenerationConfig(
do_sample=_UpperCAmelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , _UpperCAmelCase )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_UpperCAmelCase )
_A = GenerationConfig.from_pretrained(_UpperCAmelCase , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , _UpperCAmelCase )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def lowerCAmelCase_ ( cls : Any ):
_A = TOKEN
HfFolder.save_token(_UpperCAmelCase )
@classmethod
def lowerCAmelCase_ ( cls : str ):
try:
delete_repo(token=cls._token , repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def lowerCAmelCase_ ( self : List[Any] ):
_A = GenerationConfig(
do_sample=_UpperCAmelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
_A = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_UpperCAmelCase , getattr(_UpperCAmelCase , _UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_UpperCAmelCase , repo_id='test-generation-config' , push_to_hub=_UpperCAmelCase , use_auth_token=self._token )
_A = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_UpperCAmelCase , getattr(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = GenerationConfig(
do_sample=_UpperCAmelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
_A = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_UpperCAmelCase , getattr(_UpperCAmelCase , _UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_UpperCAmelCase , repo_id='valid_org/test-generation-config-org' , push_to_hub=_UpperCAmelCase , use_auth_token=self._token )
_A = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_UpperCAmelCase , getattr(_UpperCAmelCase , _UpperCAmelCase ) )
| 7 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=[30, 30] , _UpperCAmelCase=2 , _UpperCAmelCase=3 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=32 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=37 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=10 , _UpperCAmelCase=0.02 , _UpperCAmelCase=3 , _UpperCAmelCase=None , _UpperCAmelCase=8 , _UpperCAmelCase=10 , ):
'''simple docstring'''
__A : Union[str, Any] = parent
__A : Tuple = batch_size
__A : List[str] = image_size
__A : Dict = patch_size
__A : Optional[Any] = num_channels
__A : Tuple = is_training
__A : Dict = use_labels
__A : List[Any] = hidden_size
__A : Tuple = num_hidden_layers
__A : int = num_attention_heads
__A : Optional[int] = intermediate_size
__A : Tuple = hidden_act
__A : Any = hidden_dropout_prob
__A : Optional[Any] = attention_probs_dropout_prob
__A : List[Any] = type_sequence_label_size
__A : List[Any] = initializer_range
__A : Optional[int] = num_labels
__A : List[Any] = scope
__A : Any = n_targets
__A : Union[str, Any] = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
__A : List[str] = (image_size[1] // patch_size) * (image_size[0] // patch_size)
__A : int = num_patches + 1 + self.num_detection_tokens
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
__A : Tuple = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
__A : List[Any] = []
for i in range(self.batch_size):
__A : Optional[int] = {}
__A : Union[str, Any] = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=_UpperCAmelCase)
__A : str = torch.rand(self.n_targets , 4 , device=_UpperCAmelCase)
labels.append(_UpperCAmelCase)
__A : Any = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return YolosConfig(
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=_UpperCAmelCase , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Any = YolosModel(config=_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
__A : Dict = model(_UpperCAmelCase)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size))
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : Any = YolosForObjectDetection(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
__A : str = model(pixel_values=_UpperCAmelCase)
__A : List[str] = model(_UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4))
__A : Union[str, Any] = model(pixel_values=_UpperCAmelCase , labels=_UpperCAmelCase)
self.parent.assertEqual(result.loss.shape , ())
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4))
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Any = self.prepare_config_and_inputs()
__A ,__A ,__A : Tuple = config_and_inputs
__A : Tuple = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE (a__ , a__ , unittest.TestCase ):
lowerCAmelCase = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
lowerCAmelCase = (
{'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False):
'''simple docstring'''
__A : Optional[Any] = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase)
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
__A : Any = []
for i in range(self.model_tester.batch_size):
__A : Tuple = {}
__A : Tuple = torch.ones(
size=(self.model_tester.n_targets,) , device=_UpperCAmelCase , dtype=torch.long)
__A : Optional[Any] = torch.ones(
self.model_tester.n_targets , 4 , device=_UpperCAmelCase , dtype=torch.float)
labels.append(_UpperCAmelCase)
__A : str = labels
return inputs_dict
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Union[str, Any] = YolosModelTester(self)
__A : Dict = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A ,__A : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : Tuple = model_class(_UpperCAmelCase)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
__A : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_UpperCAmelCase , nn.Linear))
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A ,__A : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : List[Any] = model_class(_UpperCAmelCase)
__A : str = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A : int = [*signature.parameters.keys()]
__A : List[str] = ['pixel_values']
self.assertListEqual(arg_names[:1] , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A ,__A : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__A : Optional[int] = True
# in YOLOS, the seq_len is different
__A : Dict = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
__A : Dict = True
__A : Dict = False
__A : Union[str, Any] = True
__A : Tuple = model_class(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
with torch.no_grad():
__A : Any = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase))
__A : Union[str, Any] = outputs.attentions
self.assertEqual(len(_UpperCAmelCase) , self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__A : List[Any] = True
__A : List[str] = model_class(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
with torch.no_grad():
__A : List[Any] = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase))
__A : Optional[Any] = outputs.attentions
self.assertEqual(len(_UpperCAmelCase) , self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
__A : str = len(_UpperCAmelCase)
# Check attention is always last and order is fine
__A : Dict = True
__A : Dict = True
__A : Dict = model_class(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
with torch.no_grad():
__A : List[str] = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase))
__A : Union[str, Any] = 1
self.assertEqual(out_len + added_hidden_states , len(_UpperCAmelCase))
__A : Optional[Any] = outputs.attentions
self.assertEqual(len(_UpperCAmelCase) , self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
def check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
__A : Tuple = model_class(_UpperCAmelCase)
model.to(_UpperCAmelCase)
model.eval()
with torch.no_grad():
__A : List[str] = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase))
__A : Optional[Any] = outputs.hidden_states
__A : List[str] = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1)
self.assertEqual(len(_UpperCAmelCase) , _UpperCAmelCase)
# YOLOS has a different seq_length
__A : Dict = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [seq_length, self.model_tester.hidden_size] , )
__A ,__A : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A : List[str] = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A : Optional[int] = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase)
@slow
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A : List[Any] = YolosModel.from_pretrained(_UpperCAmelCase)
self.assertIsNotNone(_UpperCAmelCase)
def _lowerCAmelCase ( ) -> int:
__A : Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return AutoImageProcessor.from_pretrained('hustvl/yolos-small') if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Any = YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(_UpperCAmelCase)
__A : Any = self.default_image_processor
__A : str = prepare_img()
__A : int = image_processor(images=_UpperCAmelCase , return_tensors='pt').to(_UpperCAmelCase)
# forward pass
with torch.no_grad():
__A : str = model(inputs.pixel_values)
# verify outputs
__A : Tuple = torch.Size((1, 100, 92))
self.assertEqual(outputs.logits.shape , _UpperCAmelCase)
__A : Dict = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] , device=_UpperCAmelCase , )
__A : int = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] , device=_UpperCAmelCase)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , _UpperCAmelCase , atol=1e-4))
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , _UpperCAmelCase , atol=1e-4))
# verify postprocessing
__A : List[str] = image_processor.post_process_object_detection(
_UpperCAmelCase , threshold=0.3 , target_sizes=[image.size[::-1]])[0]
__A : Optional[int] = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(_UpperCAmelCase)
__A : Union[str, Any] = [75, 75, 17, 63, 17]
__A : Any = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(_UpperCAmelCase)
self.assertEqual(len(results['scores']) , 5)
self.assertTrue(torch.allclose(results['scores'] , _UpperCAmelCase , atol=1e-4))
self.assertSequenceEqual(results['labels'].tolist() , _UpperCAmelCase)
self.assertTrue(torch.allclose(results['boxes'][0, :] , _UpperCAmelCase)) | 8 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE__ = '''MobileNetV1Config'''
# Base docstring
SCREAMING_SNAKE_CASE__ = '''google/mobilenet_v1_1.0_224'''
SCREAMING_SNAKE_CASE__ = [1, 1_0_2_4, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE__ = '''google/mobilenet_v1_1.0_224'''
SCREAMING_SNAKE_CASE__ = '''tabby, tabby cat'''
SCREAMING_SNAKE_CASE__ = [
'''google/mobilenet_v1_1.0_224''',
'''google/mobilenet_v1_0.75_192''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None ) -> List[str]:
A__ = {}
if isinstance(__UpperCamelCase , __UpperCamelCase ):
A__ = model.mobilenet_va
else:
A__ = model
A__ = 'MobilenetV1/Conv2d_0/'
A__ = backbone.conv_stem.convolution.weight
A__ = backbone.conv_stem.normalization.bias
A__ = backbone.conv_stem.normalization.weight
A__ = backbone.conv_stem.normalization.running_mean
A__ = backbone.conv_stem.normalization.running_var
for i in range(13 ):
A__ = i + 1
A__ = i * 2
A__ = backbone.layer[pt_index]
A__ = f'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
A__ = pointer.convolution.weight
A__ = pointer.normalization.bias
A__ = pointer.normalization.weight
A__ = pointer.normalization.running_mean
A__ = pointer.normalization.running_var
A__ = backbone.layer[pt_index + 1]
A__ = f'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
A__ = pointer.convolution.weight
A__ = pointer.normalization.bias
A__ = pointer.normalization.weight
A__ = pointer.normalization.running_mean
A__ = pointer.normalization.running_var
if isinstance(__UpperCamelCase , __UpperCamelCase ):
A__ = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
A__ = model.classifier.weight
A__ = model.classifier.bias
return tf_to_pt_map
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Optional[Any]:
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
A__ = tf.train.list_variables(__UpperCamelCase )
A__ = {}
for name, shape in init_vars:
logger.info(f'''Loading TF weight {name} with shape {shape}''' )
A__ = tf.train.load_variable(__UpperCamelCase , __UpperCamelCase )
A__ = array
# Build TF to PyTorch weights loading map
A__ = _build_tf_to_pytorch_map(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
for name, pointer in tf_to_pt_map.items():
logger.info(f'''Importing {name}''' )
if name not in tf_weights:
logger.info(f'''{name} not in tf pre-trained weights, skipping''' )
continue
A__ = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
A__ = np.transpose(__UpperCamelCase , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
A__ = array.squeeze().transpose()
else:
A__ = np.transpose(__UpperCamelCase , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )
logger.info(f'''Initialize PyTorch weight {name} {array.shape}''' )
A__ = torch.from_numpy(__UpperCamelCase )
tf_weights.pop(__UpperCamelCase , __UpperCamelCase )
tf_weights.pop(name + '/RMSProp' , __UpperCamelCase )
tf_weights.pop(name + '/RMSProp_1' , __UpperCamelCase )
tf_weights.pop(name + '/ExponentialMovingAverage' , __UpperCamelCase )
logger.info(f'''Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}''' )
return model
def A ( __UpperCamelCase , __UpperCamelCase ) -> torch.Tensor:
A__ , A__ = features.shape[-2:]
A__ , A__ = conv_layer.stride
A__ , A__ = conv_layer.kernel_size
if in_height % stride_height == 0:
A__ = max(kernel_height - stride_height , 0 )
else:
A__ = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
A__ = max(kernel_width - stride_width , 0 )
else:
A__ = max(kernel_width - (in_width % stride_width) , 0 )
A__ = pad_along_width // 2
A__ = pad_along_width - pad_left
A__ = pad_along_height // 2
A__ = pad_along_height - pad_top
A__ = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(__UpperCamelCase , __UpperCamelCase , 'constant' , 0.0 )
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , _snake_case : MobileNetVaConfig , _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : Optional[int] = 1 , _snake_case : Optional[int] = 1 , _snake_case : bool = False , _snake_case : Optional[bool] = True , _snake_case : Optional[bool or str] = True , ):
"""simple docstring"""
super().__init__()
A__ = config
if in_channels % groups != 0:
raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.''' )
if out_channels % groups != 0:
raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.''' )
A__ = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
A__ = nn.Convad(
in_channels=_snake_case , out_channels=_snake_case , kernel_size=_snake_case , stride=_snake_case , padding=_snake_case , groups=_snake_case , bias=_snake_case , padding_mode='zeros' , )
if use_normalization:
A__ = nn.BatchNormad(
num_features=_snake_case , eps=config.layer_norm_eps , momentum=0.9997 , affine=_snake_case , track_running_stats=_snake_case , )
else:
A__ = None
if use_activation:
if isinstance(_snake_case , _snake_case ):
A__ = ACTaFN[use_activation]
elif isinstance(config.hidden_act , _snake_case ):
A__ = ACTaFN[config.hidden_act]
else:
A__ = config.hidden_act
else:
A__ = None
def _a ( self : Dict , _snake_case : torch.Tensor ):
"""simple docstring"""
if self.config.tf_padding:
A__ = apply_tf_padding(_snake_case , self.convolution )
A__ = self.convolution(_snake_case )
if self.normalization is not None:
A__ = self.normalization(_snake_case )
if self.activation is not None:
A__ = self.activation(_snake_case )
return features
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : Union[str, Any] = MobileNetVaConfig
A__ : List[Any] = load_tf_weights_in_mobilenet_va
A__ : Tuple = "mobilenet_v1"
A__ : List[Any] = "pixel_values"
A__ : int = False
def _a ( self : Union[str, Any] , _snake_case : Union[nn.Linear, nn.Convad] ):
"""simple docstring"""
if isinstance(_snake_case , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(_snake_case , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
SCREAMING_SNAKE_CASE__ = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
SCREAMING_SNAKE_CASE__ = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , UpperCAmelCase_ , )
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Optional[int] , _snake_case : MobileNetVaConfig , _snake_case : bool = True ):
"""simple docstring"""
super().__init__(_snake_case )
A__ = config
A__ = 32
A__ = max(int(depth * config.depth_multiplier ) , config.min_depth )
A__ = MobileNetVaConvLayer(
_snake_case , in_channels=config.num_channels , out_channels=_snake_case , kernel_size=3 , stride=2 , )
A__ = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
A__ = nn.ModuleList()
for i in range(13 ):
A__ = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
A__ = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
_snake_case , in_channels=_snake_case , out_channels=_snake_case , kernel_size=3 , stride=strides[i] , groups=_snake_case , ) )
self.layer.append(
MobileNetVaConvLayer(
_snake_case , in_channels=_snake_case , out_channels=_snake_case , kernel_size=1 , ) )
A__ = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def _a ( self : List[str] , _snake_case : List[str] ):
"""simple docstring"""
raise NotImplementedError
@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 _a ( self : Optional[Any] , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[bool] = None , ):
"""simple docstring"""
A__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
A__ = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
A__ = self.conv_stem(_snake_case )
A__ = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
A__ = layer_module(_snake_case )
if output_hidden_states:
A__ = all_hidden_states + (hidden_states,)
A__ = hidden_states
if self.pooler is not None:
A__ = torch.flatten(self.pooler(_snake_case ) , start_dim=1 )
else:
A__ = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_snake_case , pooler_output=_snake_case , hidden_states=_snake_case , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , UpperCAmelCase_ , )
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Optional[Any] , _snake_case : MobileNetVaConfig ):
"""simple docstring"""
super().__init__(_snake_case )
A__ = config.num_labels
A__ = MobileNetVaModel(_snake_case )
A__ = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
A__ = nn.Dropout(config.classifier_dropout_prob , inplace=_snake_case )
A__ = nn.Linear(_snake_case , 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 _a ( self : Optional[Any] , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[bool] = None , ):
"""simple docstring"""
A__ = return_dict if return_dict is not None else self.config.use_return_dict
A__ = self.mobilenet_va(_snake_case , output_hidden_states=_snake_case , return_dict=_snake_case )
A__ = outputs.pooler_output if return_dict else outputs[1]
A__ = self.classifier(self.dropout(_snake_case ) )
A__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
A__ = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
A__ = 'single_label_classification'
else:
A__ = 'multi_label_classification'
if self.config.problem_type == "regression":
A__ = MSELoss()
if self.num_labels == 1:
A__ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
A__ = loss_fct(_snake_case , _snake_case )
elif self.config.problem_type == "single_label_classification":
A__ = CrossEntropyLoss()
A__ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
A__ = BCEWithLogitsLoss()
A__ = loss_fct(_snake_case , _snake_case )
if not return_dict:
A__ = (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 , )
| 9 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = 0
def UpperCamelCase_ ( self : Tuple ):
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_A , _A )
def UpperCamelCase_ ( self : str ):
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCamelCase = Path(_A ) / '''preprocessor_config.json'''
_UpperCamelCase = Path(_A ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_A , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_A , '''w''' ) )
_UpperCamelCase = AutoImageProcessor.from_pretrained(_A )
self.assertIsInstance(_A , _A )
def UpperCamelCase_ ( self : Tuple ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCamelCase = Path(_A ) / '''preprocessor_config.json'''
_UpperCamelCase = Path(_A ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_A , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_A , '''w''' ) )
_UpperCamelCase = AutoImageProcessor.from_pretrained(_A )
self.assertIsInstance(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCamelCase = CLIPConfig()
# Create a dummy config file with image_proceesor_type
_UpperCamelCase = Path(_A ) / '''preprocessor_config.json'''
_UpperCamelCase = Path(_A ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_A , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_A , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
_UpperCamelCase = AutoImageProcessor.from_pretrained(_A ).to_dict()
config_dict.pop('''image_processor_type''' )
_UpperCamelCase = CLIPImageProcessor(**_A )
# save in new folder
model_config.save_pretrained(_A )
config.save_pretrained(_A )
_UpperCamelCase = AutoImageProcessor.from_pretrained(_A )
# make sure private variable is not incorrectly saved
_UpperCamelCase = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_A , _A )
def UpperCamelCase_ ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCamelCase = Path(_A ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_A , '''w''' ) , )
_UpperCamelCase = AutoImageProcessor.from_pretrained(_A )
self.assertIsInstance(_A , _A )
def UpperCamelCase_ ( self : List[Any] ):
with self.assertRaisesRegex(
_A , '''clip-base is not a local folder and is not a valid model identifier''' ):
_UpperCamelCase = AutoImageProcessor.from_pretrained('''clip-base''' )
def UpperCamelCase_ ( self : Dict ):
with self.assertRaisesRegex(
_A , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
_UpperCamelCase = AutoImageProcessor.from_pretrained(_A , revision='''aaaaaa''' )
def UpperCamelCase_ ( self : Union[str, Any] ):
with self.assertRaisesRegex(
_A , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
_UpperCamelCase = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def UpperCamelCase_ ( self : List[Any] ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_A ):
_UpperCamelCase = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_A ):
_UpperCamelCase = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_A )
_UpperCamelCase = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_A )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_A )
_UpperCamelCase = AutoImageProcessor.from_pretrained(_A , trust_remote_code=_A )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def UpperCamelCase_ ( self : List[Any] ):
try:
AutoConfig.register('''custom''' , _A )
AutoImageProcessor.register(_A , _A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A ):
AutoImageProcessor.register(_A , _A )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCamelCase = Path(_A ) / '''preprocessor_config.json'''
_UpperCamelCase = Path(_A ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_A , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_A , '''w''' ) )
_UpperCamelCase = CustomImageProcessor.from_pretrained(_A )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_A )
_UpperCamelCase = AutoImageProcessor.from_pretrained(_A )
self.assertIsInstance(_A , _A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCamelCase_ ( self : Optional[Any] ):
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = True
try:
AutoConfig.register('''custom''' , _A )
AutoImageProcessor.register(_A , _A )
# If remote code is not set, the default is to use local
_UpperCamelCase = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
_UpperCamelCase = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_A )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
_UpperCamelCase = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_A )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_A , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 10 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self , A , A ) -> Optional[int]:
"""simple docstring"""
_a = jnp.ones((batch_size, length) ) / length
return scores
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = None
_a = 20
_a = self._get_uniform_logits(batch_size=2 , length=A )
# tweak scores to not be uniform anymore
_a = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
_a = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
_a = jax.nn.softmax(A , axis=-1 )
_a = FlaxTemperatureLogitsWarper(temperature=0.5 )
_a = FlaxTemperatureLogitsWarper(temperature=1.3 )
_a = jax.nn.softmax(temp_dist_warper_sharper(A , scores.copy() , cur_len=A ) , axis=-1 )
_a = jax.nn.softmax(temp_dist_warper_smoother(A , scores.copy() , cur_len=A ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = None
_a = 10
_a = 2
# create ramp distribution
_a = np.broadcast_to(np.arange(A )[None, :] , (batch_size, vocab_size) ).copy()
_a = ramp_logits[1:, : vocab_size // 2] + vocab_size
_a = FlaxTopKLogitsWarper(3 )
_a = top_k_warp(A , A , cur_len=A )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
_a = 5
_a = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
_a = np.broadcast_to(np.arange(A )[None, :] , (batch_size, length) ).copy()
_a = top_k_warp_safety_check(A , A , cur_len=A )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = None
_a = 10
_a = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
_a = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
_a = FlaxTopPLogitsWarper(0.8 )
_a = np.exp(top_p_warp(A , A , cur_len=A ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
_a = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(A , A , atol=1E-3 ) )
# check edge cases with negative and extreme logits
_a = np.broadcast_to(np.arange(A )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
_a = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
_a = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
_a = top_p_warp(A , A , cur_len=A )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = 20
_a = 4
_a = 0
_a = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=A )
# check that min length is applied at length 5
_a = ids_tensor((batch_size, 20) , vocab_size=20 )
_a = 5
_a = self._get_uniform_logits(A , A )
_a = min_dist_processor(A , A , cur_len=A )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float('''inf''' )] )
# check that min length is not applied anymore at length 15
_a = self._get_uniform_logits(A , A )
_a = 15
_a = min_dist_processor(A , A , cur_len=A )
self.assertFalse(jnp.isinf(A ).any() )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = 20
_a = 4
_a = 0
_a = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A )
# check that all scores are -inf except the bos_token_id score
_a = ids_tensor((batch_size, 1) , vocab_size=20 )
_a = 1
_a = self._get_uniform_logits(A , A )
_a = logits_processor(A , A , cur_len=A )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
_a = 3
_a = self._get_uniform_logits(A , A )
_a = logits_processor(A , A , cur_len=A )
self.assertFalse(jnp.isinf(A ).any() )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = 20
_a = 4
_a = 0
_a = 5
_a = FlaxForcedEOSTokenLogitsProcessor(max_length=A , eos_token_id=A )
# check that all scores are -inf except the eos_token_id when max_length is reached
_a = ids_tensor((batch_size, 4) , vocab_size=20 )
_a = 4
_a = self._get_uniform_logits(A , A )
_a = logits_processor(A , A , cur_len=A )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
_a = 3
_a = self._get_uniform_logits(A , A )
_a = logits_processor(A , A , cur_len=A )
self.assertFalse(jnp.isinf(A ).any() )
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = 4
_a = 10
_a = 15
_a = 2
_a = 1
_a = 15
# dummy input_ids and scores
_a = ids_tensor((batch_size, sequence_length) , A )
_a = input_ids.copy()
_a = self._get_uniform_logits(A , A )
_a = scores.copy()
# instantiate all dist processors
_a = FlaxTemperatureLogitsWarper(temperature=0.5 )
_a = FlaxTopKLogitsWarper(3 )
_a = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
_a = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=A )
_a = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A )
_a = FlaxForcedEOSTokenLogitsProcessor(max_length=A , eos_token_id=A )
_a = 10
# no processor list
_a = temp_dist_warp(A , A , cur_len=A )
_a = top_k_warp(A , A , cur_len=A )
_a = top_p_warp(A , A , cur_len=A )
_a = min_dist_proc(A , A , cur_len=A )
_a = bos_dist_proc(A , A , cur_len=A )
_a = eos_dist_proc(A , A , cur_len=A )
# with processor list
_a = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
_a = processor(A , A , cur_len=A )
# scores should be equal
self.assertTrue(jnp.allclose(A , A , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = 4
_a = 10
_a = 15
_a = 2
_a = 1
_a = 15
# dummy input_ids and scores
_a = ids_tensor((batch_size, sequence_length) , A )
_a = input_ids.copy()
_a = self._get_uniform_logits(A , A )
_a = scores.copy()
# instantiate all dist processors
_a = FlaxTemperatureLogitsWarper(temperature=0.5 )
_a = FlaxTopKLogitsWarper(3 )
_a = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
_a = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=A )
_a = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A )
_a = FlaxForcedEOSTokenLogitsProcessor(max_length=A , eos_token_id=A )
_a = 10
# no processor list
def run_no_processor_list(A , A , A ):
_a = temp_dist_warp(A , A , cur_len=A )
_a = top_k_warp(A , A , cur_len=A )
_a = top_p_warp(A , A , cur_len=A )
_a = min_dist_proc(A , A , cur_len=A )
_a = bos_dist_proc(A , A , cur_len=A )
_a = eos_dist_proc(A , A , cur_len=A )
return scores
# with processor list
def run_processor_list(A , A , A ):
_a = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
_a = processor(A , A , cur_len=A )
return scores
_a = jax.jit(A )
_a = jax.jit(A )
_a = jitted_run_no_processor_list(A , A , A )
_a = jitted_run_processor_list(A , A , A )
# scores should be equal
self.assertTrue(jnp.allclose(A , A , atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 11 |
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 __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
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 lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
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
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 0 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _snake_case ( unittest.TestCase ):
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Dict = """ylacombe/bark-small"""
lowercase__ : Dict = tempfile.mkdtemp()
lowercase__ : Any = """en_speaker_1"""
lowercase__ : Optional[int] = """This is a test string"""
lowercase__ : Tuple = """speaker_embeddings_path.json"""
lowercase__ : str = """speaker_embeddings"""
def lowercase__ ( self , **SCREAMING_SNAKE_CASE_):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_)
def lowercase__ ( self):
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Dict = self.get_tokenizer()
lowercase__ : int = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_)
processor.save_pretrained(self.tmpdirname)
lowercase__ : List[str] = BarkProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab())
@slow
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Union[str, Any] = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowercase__ : Optional[int] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""")
lowercase__ : Any = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Dict = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowercase__ : Optional[int] = 35
lowercase__ : Tuple = 2
lowercase__ : Dict = 8
lowercase__ : Optional[int] = {
"""semantic_prompt""": np.ones(SCREAMING_SNAKE_CASE_),
"""coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)),
"""fine_prompt""": np.ones((nb_codebooks_total, seq_len)),
}
# test providing already loaded voice_preset
lowercase__ : Tuple = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_)
lowercase__ : Union[str, Any] = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([])).tolist())
# test loading voice preset from npz file
lowercase__ : List[Any] = os.path.join(self.tmpdirname , """file.npz""")
np.savez(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_)
lowercase__ : str = processor(text=self.input_string , voice_preset=SCREAMING_SNAKE_CASE_)
lowercase__ : Optional[Any] = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(SCREAMING_SNAKE_CASE_ , np.array([])).tolist())
# test loading voice preset from the hub
lowercase__ : int = processor(text=self.input_string , voice_preset=self.voice_preset)
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Dict = self.get_tokenizer()
lowercase__ : str = BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_)
lowercase__ : List[Any] = processor(text=self.input_string)
lowercase__ : List[str] = tokenizer(
self.input_string , padding="""max_length""" , max_length=2_56 , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist())
| 12 |
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_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[str] = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any = [
"""SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SEWForCTC""",
"""SEWForSequenceClassification""",
"""SEWModel""",
"""SEWPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
A__ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 13 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 0 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter import NumpyFormatter
a__ = logging.get_logger(__name__)
a__ = {}
a__ = {}
a__ = {}
def __UpperCAmelCase ( __a : type ,__a : Optional[str] ,__a : Optional[List[str]] = None ,) -> Any:
"""simple docstring"""
_a : Any = aliases if aliases is not None else []
if format_type in _FORMAT_TYPES:
logger.warning(
F"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})""" )
_a : Optional[Any] = formatter_cls
for alias in set(aliases + [format_type] ):
if alias in _FORMAT_TYPES_ALIASES:
logger.warning(
F"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})""" )
_a : Optional[int] = format_type
def __UpperCAmelCase ( __a : Exception ,__a : Optional[str] ,__a : Optional[List[str]] = None ) -> str:
"""simple docstring"""
_a : List[str] = aliases if aliases is not None else []
for alias in set(aliases + [format_type] ):
_a : int = unavailable_error
# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')
if config.TORCH_AVAILABLE:
from .torch_formatter import TorchFormatter
_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
a__ = ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])
if config.TF_AVAILABLE:
from .tf_formatter import TFFormatter
_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
a__ = ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])
if config.JAX_AVAILABLE:
from .jax_formatter import JaxFormatter
_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
a__ = ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])
def __UpperCAmelCase ( __a : Optional[str] ) -> Optional[str]:
"""simple docstring"""
if format_type in _FORMAT_TYPES_ALIASES:
return _FORMAT_TYPES_ALIASES[format_type]
else:
return format_type
def __UpperCAmelCase ( __a : Optional[str] ,**__a : Optional[Any] ) -> Formatter:
"""simple docstring"""
_a : str = get_format_type_from_alias(__a )
if format_type in _FORMAT_TYPES:
return _FORMAT_TYPES[format_type](**__a )
if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
else:
raise ValueError(
F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None )}, but got '{format_type}'""" )
| 14 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 0 |
import socket
def UpperCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowercase__ = socket.gethostname()
lowercase__ = 1_2312
sock.connect((host, port) )
sock.send(B"""Hello server!""" )
with open("""Received_file""" , """wb""" ) as out_file:
print("""File opened""" )
print("""Receiving data...""" )
while True:
lowercase__ = sock.recv(1024 )
if not data:
break
out_file.write(__magic_name__ )
print("""Successfully received the file""" )
sock.close()
print("""Connection closed""" )
if __name__ == "__main__":
main()
| 15 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 0 |
def __a ( A__ : int ):
if not isinstance(A__ , A__ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 16 |
import unittest
import numpy as np
from datasets import load_dataset
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 BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Any = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
'''configuration_data2vec_text''': [
'''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Data2VecTextConfig''',
'''Data2VecTextOnnxConfig''',
],
'''configuration_data2vec_vision''': [
'''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Data2VecVisionConfig''',
'''Data2VecVisionOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Data2VecAudioForAudioFrameClassification''',
'''Data2VecAudioForCTC''',
'''Data2VecAudioForSequenceClassification''',
'''Data2VecAudioForXVector''',
'''Data2VecAudioModel''',
'''Data2VecAudioPreTrainedModel''',
]
UpperCAmelCase_ : List[Any] = [
'''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Data2VecTextForCausalLM''',
'''Data2VecTextForMaskedLM''',
'''Data2VecTextForMultipleChoice''',
'''Data2VecTextForQuestionAnswering''',
'''Data2VecTextForSequenceClassification''',
'''Data2VecTextForTokenClassification''',
'''Data2VecTextModel''',
'''Data2VecTextPreTrainedModel''',
]
UpperCAmelCase_ : List[str] = [
'''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Data2VecVisionForImageClassification''',
'''Data2VecVisionForMaskedImageModeling''',
'''Data2VecVisionForSemanticSegmentation''',
'''Data2VecVisionModel''',
'''Data2VecVisionPreTrainedModel''',
]
if is_tf_available():
UpperCAmelCase_ : int = [
'''TFData2VecVisionForImageClassification''',
'''TFData2VecVisionForSemanticSegmentation''',
'''TFData2VecVisionModel''',
'''TFData2VecVisionPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 17 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@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}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 0 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def __a(SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[Any] ):
'''simple docstring'''
_lowerCAmelCase = checkpoint
_lowerCAmelCase = {}
_lowerCAmelCase = vae_state_dict["encoder.conv_in.weight"]
_lowerCAmelCase = vae_state_dict["encoder.conv_in.bias"]
_lowerCAmelCase = vae_state_dict["encoder.conv_out.weight"]
_lowerCAmelCase = vae_state_dict["encoder.conv_out.bias"]
_lowerCAmelCase = vae_state_dict["encoder.norm_out.weight"]
_lowerCAmelCase = vae_state_dict["encoder.norm_out.bias"]
_lowerCAmelCase = vae_state_dict["decoder.conv_in.weight"]
_lowerCAmelCase = vae_state_dict["decoder.conv_in.bias"]
_lowerCAmelCase = vae_state_dict["decoder.conv_out.weight"]
_lowerCAmelCase = vae_state_dict["decoder.conv_out.bias"]
_lowerCAmelCase = vae_state_dict["decoder.norm_out.weight"]
_lowerCAmelCase = vae_state_dict["decoder.norm_out.bias"]
_lowerCAmelCase = vae_state_dict["quant_conv.weight"]
_lowerCAmelCase = vae_state_dict["quant_conv.bias"]
_lowerCAmelCase = vae_state_dict["post_quant_conv.weight"]
_lowerCAmelCase = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
_lowerCAmelCase = len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "encoder.down" in layer} )
_lowerCAmelCase = {
layer_id: [key for key in vae_state_dict if F'''down.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_ )
}
# Retrieves the keys for the decoder up blocks only
_lowerCAmelCase = len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "decoder.up" in layer} )
_lowerCAmelCase = {
layer_id: [key for key in vae_state_dict if F'''up.{layer_id}''' in key] for layer_id in range(SCREAMING_SNAKE_CASE_ )
}
for i in range(SCREAMING_SNAKE_CASE_ ):
_lowerCAmelCase = [key for key in down_blocks[i] if F'''down.{i}''' in key and F'''down.{i}.downsample''' not in key]
if F'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
_lowerCAmelCase = vae_state_dict.pop(
F'''encoder.down.{i}.downsample.conv.weight''' )
_lowerCAmelCase = vae_state_dict.pop(
F'''encoder.down.{i}.downsample.conv.bias''' )
_lowerCAmelCase = renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = {"old": F'''down.{i}.block''', "new": F'''down_blocks.{i}.resnets'''}
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , config=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = [key for key in vae_state_dict if "encoder.mid.block" in key]
_lowerCAmelCase = 2
for i in range(1 , num_mid_res_blocks + 1 ):
_lowerCAmelCase = [key for key in mid_resnets if F'''encoder.mid.block_{i}''' in key]
_lowerCAmelCase = renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = {"old": F'''mid.block_{i}''', "new": F'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , config=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = [key for key in vae_state_dict if "encoder.mid.attn" in key]
_lowerCAmelCase = renew_vae_attention_paths(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , config=SCREAMING_SNAKE_CASE_ )
conv_attn_to_linear(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
_lowerCAmelCase = num_up_blocks - 1 - i
_lowerCAmelCase = [
key for key in up_blocks[block_id] if F'''up.{block_id}''' in key and F'''up.{block_id}.upsample''' not in key
]
if F'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
_lowerCAmelCase = vae_state_dict[
F'''decoder.up.{block_id}.upsample.conv.weight'''
]
_lowerCAmelCase = vae_state_dict[
F'''decoder.up.{block_id}.upsample.conv.bias'''
]
_lowerCAmelCase = renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = {"old": F'''up.{block_id}.block''', "new": F'''up_blocks.{i}.resnets'''}
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , config=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = [key for key in vae_state_dict if "decoder.mid.block" in key]
_lowerCAmelCase = 2
for i in range(1 , num_mid_res_blocks + 1 ):
_lowerCAmelCase = [key for key in mid_resnets if F'''decoder.mid.block_{i}''' in key]
_lowerCAmelCase = renew_vae_resnet_paths(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = {"old": F'''mid.block_{i}''', "new": F'''mid_block.resnets.{i - 1}'''}
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , config=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = [key for key in vae_state_dict if "decoder.mid.attn" in key]
_lowerCAmelCase = renew_vae_attention_paths(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , additional_replacements=[meta_path] , config=SCREAMING_SNAKE_CASE_ )
conv_attn_to_linear(SCREAMING_SNAKE_CASE_ )
return new_checkpoint
def __a(SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , ):
'''simple docstring'''
_lowerCAmelCase = requests.get(
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" )
_lowerCAmelCase = io.BytesIO(r.content )
_lowerCAmelCase = OmegaConf.load(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = 512
_lowerCAmelCase = "cuda" if torch.cuda.is_available() else "cpu"
if checkpoint_path.endswith("safetensors" ):
from safetensors import safe_open
_lowerCAmelCase = {}
with safe_open(SCREAMING_SNAKE_CASE_ , framework="pt" , device="cpu" ) as f:
for key in f.keys():
_lowerCAmelCase = f.get_tensor(SCREAMING_SNAKE_CASE_ )
else:
_lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ , map_location=SCREAMING_SNAKE_CASE_ )["state_dict"]
# Convert the VAE model.
_lowerCAmelCase = create_vae_diffusers_config(SCREAMING_SNAKE_CASE_ , image_size=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = custom_convert_ldm_vae_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = AutoencoderKL(**SCREAMING_SNAKE_CASE_ )
vae.load_state_dict(SCREAMING_SNAKE_CASE_ )
vae.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
_SCREAMING_SNAKE_CASE = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 18 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_a = False
class _UpperCAmelCase( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class _UpperCAmelCase( unittest.TestCase ):
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion''')
pipe.to(__a)
pipe.set_progress_bar_config(disable=__a)
_UpperCamelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''')
_UpperCamelCase = torch.manual_seed(0)
_UpperCamelCase = pipe(
image=__a , generator=__a , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
_UpperCamelCase = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
_UpperCamelCase = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 19 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
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 lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} 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() ) )
| 60 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase: List[str] = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase: int = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase: Any = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase: Optional[int] = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase: Union[str, Any] = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
_lowerCAmelCase: Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 20 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __A ( UpperCamelCase__ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __A ( unittest.TestCase ):
@property
def A__ ( self :int ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def A__ ( self :Dict ):
'''simple docstring'''
__magic_name__ : int =ort.SessionOptions()
__magic_name__ : int =False
return options
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Optional[Any] =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
__magic_name__ : Any =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
__magic_name__ : Union[str, Any] =OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , safety_checker=__snake_case , feature_extractor=__snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : Tuple ="""A red cat sitting on a park bench"""
__magic_name__ : int =np.random.RandomState(0 )
__magic_name__ : List[str] =pipe(
prompt=__snake_case , image=__snake_case , mask_image=__snake_case , guidance_scale=7.5 , num_inference_steps=10 , generator=__snake_case , output_type="""np""" , )
__magic_name__ : Union[str, Any] =output.images
__magic_name__ : str =images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__magic_name__ : Tuple =np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def A__ ( self :Tuple ):
'''simple docstring'''
__magic_name__ : Tuple =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
__magic_name__ : Tuple =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
__magic_name__ : Optional[int] =LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , subfolder="""scheduler""" , revision="""onnx""" )
__magic_name__ : Optional[Any] =OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , scheduler=__snake_case , safety_checker=__snake_case , feature_extractor=__snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__snake_case )
__magic_name__ : str ="""A red cat sitting on a park bench"""
__magic_name__ : Optional[int] =np.random.RandomState(0 )
__magic_name__ : Optional[int] =pipe(
prompt=__snake_case , image=__snake_case , mask_image=__snake_case , guidance_scale=7.5 , num_inference_steps=20 , generator=__snake_case , output_type="""np""" , )
__magic_name__ : Union[str, Any] =output.images
__magic_name__ : Union[str, Any] =images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__magic_name__ : Any =np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 21 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 0 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
_snake_case : str = float('nan')
class A :
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Optional[int] ) -> List[Any]:
"""simple docstring"""
_a = sys.stdout
_a = open(lowerCAmelCase_ , '''a''' )
def __getattr__( self : Dict , lowerCAmelCase_ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return getattr(self.stdout , lowerCAmelCase_ )
def __lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : Optional[int] ) -> str:
"""simple docstring"""
self.stdout.write(lowerCAmelCase_ )
# strip tqdm codes
self.file.write(re.sub(R'''^.*\r''' , '''''' , lowerCAmelCase_ , 0 , re.M ) )
def snake_case_ (UpperCamelCase : List[str]=80 , UpperCamelCase : int=False ):
'''simple docstring'''
_a = []
# deal with critical env vars
_a = ['''CUDA_VISIBLE_DEVICES''']
for key in env_keys:
_a = os.environ.get(UpperCamelCase , UpperCamelCase )
if val is not None:
cmd.append(f'{key}={val}' )
# python executable (not always needed if the script is executable)
_a = sys.executable if full_python_path else sys.executable.split('''/''' )[-1]
cmd.append(UpperCamelCase )
# now the normal args
cmd += list(map(shlex.quote , sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
_a = []
_a = ''''''
while len(UpperCamelCase ) > 0:
current_line += f'{cmd.pop(0 )} '
if len(UpperCamelCase ) == 0 or len(UpperCamelCase ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(UpperCamelCase )
_a = ''''''
return "\\\n".join(UpperCamelCase )
def snake_case_ (UpperCamelCase : List[Any] , UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = re.sub(R'''[\\\n]+''' , ''' ''' , args.base_cmd )
# remove --output_dir if any and set our own
_a = re.sub('''--output_dir\s+[^\s]+''' , '''''' , args.base_cmd )
args.base_cmd += f' --output_dir {output_dir}'
# ensure we have --overwrite_output_dir
_a = re.sub('''--overwrite_output_dir\s+''' , '''''' , args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Any , UpperCamelCase : Union[str, Any] , UpperCamelCase : int , UpperCamelCase : Dict , UpperCamelCase : List[str] , UpperCamelCase : Any ):
'''simple docstring'''
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0 , 100 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222] )} , )
_a = subprocess.run(UpperCamelCase , capture_output=UpperCamelCase , text=UpperCamelCase )
if verbose:
print('''STDOUT''' , result.stdout )
print('''STDERR''' , result.stderr )
# save the streams
_a = variation.replace(''' ''' , '''-''' )
with open(Path(UpperCamelCase ) / f'log.{prefix}.stdout.txt' , '''w''' ) as f:
f.write(result.stdout )
with open(Path(UpperCamelCase ) / f'log.{prefix}.stderr.txt' , '''w''' ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print('''failed''' )
return {target_metric_key: nan}
with io.open(f'{output_dir}/all_results.json' , '''r''' , encoding='''utf-8''' ) as f:
_a = json.load(UpperCamelCase )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def snake_case_ (UpperCamelCase : List[str] , UpperCamelCase : Tuple , UpperCamelCase : Any , UpperCamelCase : str , UpperCamelCase : Dict , UpperCamelCase : Any , UpperCamelCase : str , UpperCamelCase : str , UpperCamelCase : Any , UpperCamelCase : List[str] , ):
'''simple docstring'''
_a = []
_a = []
_a = f'{id}: {variation:<{longest_variation_len}}'
_a = f'{preamble}: '
_a = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(UpperCamelCase ) , desc=UpperCamelCase , leave=UpperCamelCase ):
_a = process_run_single(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
_a = single_run_metrics[target_metric_key]
if not math.isnan(UpperCamelCase ):
metrics.append(UpperCamelCase )
results.append(UpperCamelCase )
outcome += "✓"
else:
outcome += "✘"
_a = f'\33[2K\r{outcome}'
if len(UpperCamelCase ) > 0:
_a = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
_a = round(mean_metrics[target_metric_key] , 2 )
_a = f'{outcome} {mean_target}'
if len(UpperCamelCase ) > 1:
results_str += f' {tuple(round(UpperCamelCase , 2 ) for x in results )}'
print(UpperCamelCase )
_a = variation
return mean_metrics
else:
print(UpperCamelCase )
return {variation_key: variation, target_metric_key: nan}
def snake_case_ ():
'''simple docstring'''
_a = torch.cuda.get_device_properties(torch.device('''cuda''' ) )
return f'\nDatetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch : {torch.__version__}\ncuda : {torch.version.cuda}\npython : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n'
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Optional[int] , UpperCamelCase : Dict , UpperCamelCase : Dict , UpperCamelCase : str ):
'''simple docstring'''
_a = pd.DataFrame(UpperCamelCase )
_a = '''variation'''
_a = '''diff_%'''
_a = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
_a = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(UpperCamelCase ):
# as a fallback, use the minimal value as the sentinel
_a = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(UpperCamelCase ):
_a = df.apply(
lambda UpperCamelCase : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0 , axis='''columns''' , )
# re-order columns
_a = [variation_key, target_metric_key, diff_key, *report_metric_keys]
_a = df.reindex(UpperCamelCase , axis='''columns''' ) # reorder cols
# capitalize
_a = df.rename(str.capitalize , axis='''columns''' )
# make the cols as narrow as possible
_a = df.rename(lambda UpperCamelCase : c.replace('''_''' , '''<br>''' ) , axis='''columns''' )
_a = df.rename(lambda UpperCamelCase : c.replace('''_''' , '''\n''' ) , axis='''columns''' )
_a = ['''''', '''Copy between the cut-here-lines and paste as is to github or a forum''']
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=UpperCamelCase , floatfmt='''.2f''' )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=UpperCamelCase , floatfmt='''.2f''' )]
print('''\n\n'''.join(UpperCamelCase ) )
def snake_case_ ():
'''simple docstring'''
_a = argparse.ArgumentParser()
parser.add_argument(
'''--base-cmd''' , default=UpperCamelCase , type=UpperCamelCase , required=UpperCamelCase , help='''Base cmd''' , )
parser.add_argument(
'''--variations''' , default=UpperCamelCase , type=UpperCamelCase , nargs='''+''' , required=UpperCamelCase , help='''Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'''' , )
parser.add_argument(
'''--base-variation''' , default=UpperCamelCase , type=UpperCamelCase , help='''Baseline variation to compare to. if None the minimal target value will be used to compare against''' , )
parser.add_argument(
'''--target-metric-key''' , default=UpperCamelCase , type=UpperCamelCase , required=UpperCamelCase , help='''Target metric key in output_dir/all_results.json, e.g., train_samples_per_second''' , )
parser.add_argument(
'''--report-metric-keys''' , default='''''' , type=UpperCamelCase , help='''Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples''' , )
parser.add_argument(
'''--repeat-times''' , default=1 , type=UpperCamelCase , help='''How many times to re-run each variation - an average will be reported''' , )
parser.add_argument(
'''--output_dir''' , default='''output_benchmark''' , type=UpperCamelCase , help='''The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked''' , )
parser.add_argument(
'''--verbose''' , default=UpperCamelCase , action='''store_true''' , help='''Whether to show the outputs of each run or just the benchmark progress''' , )
_a = parser.parse_args()
_a = args.output_dir
Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
_a = get_base_command(UpperCamelCase , UpperCamelCase )
# split each dimension into its --foo variations
_a = [list(map(str.strip , re.split(R'''\|''' , UpperCamelCase ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
_a = list(map(str.strip , map(''' '''.join , itertools.product(*UpperCamelCase ) ) ) )
_a = max(len(UpperCamelCase ) for x in variations )
# split wanted keys
_a = args.report_metric_keys.split()
# capture prints into a log file for convenience
_a = f'benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S" )}.txt'
print(f'\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt' )
print(f'and this script\'s output is also piped into {report_fn}' )
_a = Tee(UpperCamelCase )
print(f'\n*** Running {len(UpperCamelCase )} benchmarks:' )
print(f'Base command: {" ".join(UpperCamelCase )}' )
_a = '''variation'''
_a = []
for id, variation in enumerate(tqdm(UpperCamelCase , desc='''Total completion: ''' , leave=UpperCamelCase ) ):
_a = base_cmd + variation.split()
results.append(
process_run(
id + 1 , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , args.target_metric_key , UpperCamelCase , args.repeat_times , UpperCamelCase , args.verbose , ) )
process_results(UpperCamelCase , args.target_metric_key , UpperCamelCase , args.base_variation , UpperCamelCase )
if __name__ == "__main__":
main()
| 22 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 0 |
import logging
from transformers.configuration_utils import PretrainedConfig
snake_case__ : Optional[int] = logging.getLogger(__name__)
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
A_ = """masked_bert"""
def __init__( self , _UpperCAmelCase=30522 , _UpperCAmelCase=768 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=3072 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=2 , _UpperCAmelCase=0.0_2 , _UpperCAmelCase=1e-12 , _UpperCAmelCase=0 , _UpperCAmelCase="topK" , _UpperCAmelCase="constant" , _UpperCAmelCase=0.0 , **_UpperCAmelCase , ) -> Union[str, Any]:
super().__init__(pad_token_id=_UpperCAmelCase , **_UpperCAmelCase )
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = hidden_act
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = type_vocab_size
UpperCamelCase_ = initializer_range
UpperCamelCase_ = layer_norm_eps
UpperCamelCase_ = pruning_method
UpperCamelCase_ = mask_init
UpperCamelCase_ = mask_scale
| 23 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 0 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase_ : Tuple = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
'''
UpperCAmelCase_ : Optional[Any] = '''\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
'''
UpperCAmelCase_ : int = '''
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
predictions: list of predictions to score (as int64),
except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).
references: list of ground truth labels corresponding to the predictions (as int64),
except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
"accuracy": Accuracy
"f1": F1 score
"precision": Precision@10
Examples:
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\') # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')
>>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'precision@10\': 1.0}
'''
def _UpperCamelCase (_lowerCamelCase : Optional[int] , _lowerCamelCase : List[str] )-> str:
'''simple docstring'''
return float((preds == labels).mean() )
def _UpperCamelCase (_lowerCamelCase : Tuple , _lowerCamelCase : List[str] )-> Tuple:
'''simple docstring'''
__snake_case = simple_accuracy(_lowerCamelCase , _lowerCamelCase )
__snake_case = float(fa_score(y_true=_lowerCamelCase , y_pred=_lowerCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def _UpperCamelCase (_lowerCamelCase : List[Any] , _lowerCamelCase : Optional[Any] )-> str:
'''simple docstring'''
__snake_case = np.array(_lowerCamelCase )
__snake_case = np.array(_lowerCamelCase )
__snake_case = en_sentvecs.shape[0]
# mean centering
__snake_case = en_sentvecs - np.mean(_lowerCamelCase , axis=0 )
__snake_case = in_sentvecs - np.mean(_lowerCamelCase , axis=0 )
__snake_case = cdist(_lowerCamelCase , _lowerCamelCase , '''cosine''' )
__snake_case = np.array(range(_lowerCamelCase ) )
__snake_case = sim.argsort(axis=1 )[:, :10]
__snake_case = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCAmelCase ( datasets.Metric):
def lowerCAmelCase ( self ) -> str:
'''simple docstring'''
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
'''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
'''"wiki-ner"]''' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''int64''' )
if self.config_name != '''cvit-mkb-clsr'''
else datasets.Sequence(datasets.Value('''float32''' ) ),
'''references''': datasets.Value('''int64''' )
if self.config_name != '''cvit-mkb-clsr'''
else datasets.Sequence(datasets.Value('''float32''' ) ),
} ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' if self.config_name != '''cvit-mkb-clsr''' else None , )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}
else:
raise KeyError(
'''You should supply a configuration name selected in '''
'''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
'''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
'''"wiki-ner"]''' )
| 24 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 0 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE : List[Any] = ya
SCREAMING_SNAKE_CASE : str = xa
for k in range(_a):
SCREAMING_SNAKE_CASE : Optional[int] = y[k] + step_size * ode_func(_a , y[k])
SCREAMING_SNAKE_CASE : Union[str, Any] = y[k] + (
(step_size / 2) * (ode_func(_a , y[k]) + ode_func(x + step_size , _a))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 0 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
__UpperCamelCase = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"
__UpperCamelCase = "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"
__UpperCamelCase = "\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n >>> references = [\"this is the reference\", \"there is another one\"]\n >>> wer = datasets.load_metric(\"wer\")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _A ( datasets.Metric ):
def lowercase__ ( self : int ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/jitsi/jiwer/"""] , reference_urls=[
"""https://en.wikipedia.org/wiki/Word_error_rate""",
] , )
def lowercase__ ( self : Optional[Any] , __magic_name__ : int=None , __magic_name__ : Dict=None , __magic_name__ : Union[str, Any]=False ) -> str:
"""simple docstring"""
if concatenate_texts:
return compute_measures(__magic_name__ , __magic_name__ )["wer"]
else:
__snake_case : Union[str, Any] = 0
__snake_case : Tuple = 0
for prediction, reference in zip(__magic_name__ , __magic_name__ ):
__snake_case : Dict = compute_measures(__magic_name__ , __magic_name__ )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 26 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Optional[Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class lowerCamelCase( __snake_case ):
'''simple docstring'''
__magic_name__ = 't5'
__magic_name__ = ['past_key_values']
__magic_name__ = {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self , snake_case_=3_2128 , snake_case_=512 , snake_case_=64 , snake_case_=2048 , snake_case_=6 , snake_case_=None , snake_case_=8 , snake_case_=32 , snake_case_=128 , snake_case_=0.1 , snake_case_=1E-6 , snake_case_=1.0 , snake_case_="relu" , snake_case_=True , snake_case_=True , snake_case_=0 , snake_case_=1 , **snake_case_ , ):
_A = vocab_size
_A = d_model
_A = d_kv
_A = d_ff
_A = num_layers
_A = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_A = num_heads
_A = relative_attention_num_buckets
_A = relative_attention_max_distance
_A = dropout_rate
_A = layer_norm_epsilon
_A = initializer_factor
_A = feed_forward_proj
_A = use_cache
_A = self.feed_forward_proj.split('-' )
_A = act_info[-1]
_A = act_info[0] == 'gated'
if len(snake_case_ ) > 1 and act_info[0] != "gated" or len(snake_case_ ) > 2:
raise ValueError(
F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
_A = 'gelu_new'
super().__init__(
pad_token_id=snake_case_ , eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , **snake_case_ , )
class lowerCamelCase( __snake_case ):
'''simple docstring'''
@property
def lowerCAmelCase__ ( self ):
_A = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
_A = 'past_encoder_sequence + sequence'
_A = {0: 'batch'}
_A = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
_A = {0: 'batch', 1: 'decoder_sequence'}
_A = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(snake_case_ , direction='inputs' )
return common_inputs
@property
def lowerCAmelCase__ ( self ):
return 13
| 27 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _a ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
A : Optional[Any] = VideoToVideoSDPipeline
A : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'''video'''} ) - {'''image''', '''width''', '''height'''}
A : Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''video'''} ) - {'''image'''}
A : Optional[Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
A : Optional[Any] = False
# No `output_type`.
A : Tuple = frozenset(
[
'''num_inference_steps''',
'''generator''',
'''latents''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def UpperCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D'), up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D'), cross_attention_dim=32, attention_head_dim=4, )
SCREAMING_SNAKE_CASE : Tuple = DDIMScheduler(
beta_start=0.0_00_85, beta_end=0.0_12, beta_schedule='scaled_linear', clip_sample=A, set_alpha_to_one=A, )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'], up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'], latent_channels=4, sample_size=128, )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1E-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_000, hidden_act='gelu', projection_dim=512, )
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPTextModel(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
SCREAMING_SNAKE_CASE : List[Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
}
return components
def UpperCamelCase_ ( self, A, A=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor((1, 3, 3, 32, 32), rng=random.Random(A ) ).to(A )
if str(A ).startswith('mps' ):
SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(A )
else:
SCREAMING_SNAKE_CASE : Dict = torch.Generator(device=A ).manual_seed(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'video': video,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'pt',
}
return inputs
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Optional[int] = VideoToVideoSDPipeline(**A )
SCREAMING_SNAKE_CASE : int = sd_pipe.to(A )
sd_pipe.set_progress_bar_config(disable=A )
SCREAMING_SNAKE_CASE : Optional[int] = self.get_dummy_inputs(A )
SCREAMING_SNAKE_CASE : List[str] = 'np'
SCREAMING_SNAKE_CASE : Optional[int] = sd_pipe(**A ).frames
SCREAMING_SNAKE_CASE : Tuple = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
SCREAMING_SNAKE_CASE : Dict = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available(), reason='XFormers attention is only available with CUDA and `xformers` installed', )
def UpperCamelCase_ ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A, expected_max_diff=5E-3 )
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.' )
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class _a ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL', torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
SCREAMING_SNAKE_CASE : Tuple = torch.Generator(device='cpu' ).manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = torch.randn((1, 10, 3, 1_024, 576), generator=A )
SCREAMING_SNAKE_CASE : Dict = video.to('cuda' )
SCREAMING_SNAKE_CASE : Tuple = 'Spiderman is surfing'
SCREAMING_SNAKE_CASE : int = pipe(A, video=A, generator=A, num_inference_steps=3, output_type='pt' ).frames
SCREAMING_SNAKE_CASE : Dict = np.array([-1.0_45_89_84, -1.1_27_92_97, -0.9_66_30_86, -0.91_50_39_06, -0.75_09_76_56] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1E-2
| 28 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
| 60 | 0 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
A_ = logging.get_logger(__name__)
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
def constraint_to_multiple_of(lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__=0 ,lowerCAmelCase__=None ):
lowerCamelCase_ = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
lowerCamelCase_ = math.floor(val / multiple ) * multiple
if x < min_val:
lowerCamelCase_ = math.ceil(val / multiple ) * multiple
return x
lowerCamelCase_ = (output_size, output_size) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else output_size
lowerCamelCase_ , lowerCamelCase_ = get_image_size(lowerCAmelCase__ )
lowerCamelCase_ , lowerCamelCase_ = output_size
# determine new height and width
lowerCamelCase_ = output_height / input_height
lowerCamelCase_ = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
lowerCamelCase_ = scale_width
else:
# fit height
lowerCamelCase_ = scale_height
lowerCamelCase_ = constraint_to_multiple_of(scale_height * input_height ,multiple=lowerCAmelCase__ )
lowerCamelCase_ = constraint_to_multiple_of(scale_width * input_width ,multiple=lowerCAmelCase__ )
return (new_height, new_width)
class __lowerCamelCase ( lowerCAmelCase ):
a__: int = ['pixel_values']
def __init__( self , UpperCAmelCase = True , UpperCAmelCase = None , UpperCAmelCase = PILImageResampling.BILINEAR , UpperCAmelCase = False , UpperCAmelCase = 1 , UpperCAmelCase = True , UpperCAmelCase = 1 / 255 , UpperCAmelCase = True , UpperCAmelCase = None , UpperCAmelCase = None , **UpperCAmelCase , ):
super().__init__(**UpperCAmelCase )
lowerCamelCase_ = size if size is not None else {'''height''': 384, '''width''': 384}
lowerCamelCase_ = get_size_dict(UpperCAmelCase )
lowerCamelCase_ = do_resize
lowerCamelCase_ = size
lowerCamelCase_ = keep_aspect_ratio
lowerCamelCase_ = ensure_multiple_of
lowerCamelCase_ = resample
lowerCamelCase_ = do_rescale
lowerCamelCase_ = rescale_factor
lowerCamelCase_ = do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCamelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = False , UpperCAmelCase = 1 , UpperCAmelCase = PILImageResampling.BICUBIC , UpperCAmelCase = None , **UpperCAmelCase , ):
lowerCamelCase_ = get_size_dict(UpperCAmelCase )
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_ = get_resize_output_image_size(
UpperCAmelCase , output_size=(size['''height'''], size['''width''']) , keep_aspect_ratio=UpperCAmelCase , multiple=UpperCAmelCase , )
return resize(UpperCAmelCase , size=UpperCAmelCase , resample=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = None , **UpperCAmelCase , ):
return rescale(UpperCAmelCase , scale=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = None , **UpperCAmelCase , ):
return normalize(UpperCAmelCase , mean=UpperCAmelCase , std=UpperCAmelCase , data_format=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = ChannelDimension.FIRST , **UpperCAmelCase , ):
lowerCamelCase_ = do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ = size if size is not None else self.size
lowerCamelCase_ = get_size_dict(UpperCAmelCase )
lowerCamelCase_ = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
lowerCamelCase_ = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
lowerCamelCase_ = resample if resample is not None else self.resample
lowerCamelCase_ = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ = image_std if image_std is not None else self.image_std
lowerCamelCase_ = make_list_of_images(UpperCAmelCase )
if not valid_images(UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
lowerCamelCase_ = [to_numpy_array(UpperCAmelCase ) for image in images]
if do_resize:
lowerCamelCase_ = [self.resize(image=UpperCAmelCase , size=UpperCAmelCase , resample=UpperCAmelCase ) for image in images]
if do_rescale:
lowerCamelCase_ = [self.rescale(image=UpperCAmelCase , scale=UpperCAmelCase ) for image in images]
if do_normalize:
lowerCamelCase_ = [self.normalize(image=UpperCAmelCase , mean=UpperCAmelCase , std=UpperCAmelCase ) for image in images]
lowerCamelCase_ = [to_channel_dimension_format(UpperCAmelCase , UpperCAmelCase ) for image in images]
lowerCamelCase_ = {'''pixel_values''': images}
return BatchFeature(data=UpperCAmelCase , tensor_type=UpperCAmelCase )
def UpperCAmelCase__ ( self , UpperCAmelCase , UpperCAmelCase = None ):
lowerCamelCase_ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(UpperCAmelCase ):
lowerCamelCase_ = target_sizes.numpy()
lowerCamelCase_ = []
for idx in range(len(UpperCAmelCase ) ):
lowerCamelCase_ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=UpperCAmelCase )
lowerCamelCase_ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCAmelCase )
else:
lowerCamelCase_ = logits.argmax(dim=1 )
lowerCamelCase_ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 29 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 0 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
__a = None
__a = '<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__a = [
np.dtype('|b1'),
np.dtype('|u1'),
np.dtype('<u2'),
np.dtype('>u2'),
np.dtype('<i2'),
np.dtype('>i2'),
np.dtype('<u4'),
np.dtype('>u4'),
np.dtype('<i4'),
np.dtype('>i4'),
np.dtype('<f4'),
np.dtype('>f4'),
np.dtype('<f8'),
np.dtype('>f8'),
]
@dataclass
class __a:
"""simple docstring"""
lowerCAmelCase = True
lowerCAmelCase = None
# Automatically constructed
lowerCAmelCase = "PIL.Image.Image"
lowerCAmelCase = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
lowerCAmelCase = field(default='''Image''' , init=_a , repr=_a )
def __call__( self ) -> Tuple:
return self.pa_type
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : List[str] = np.array(_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
return {"path": value, "bytes": None}
elif isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
return {"path": None, "bytes": value}
elif isinstance(_SCREAMING_SNAKE_CASE ,np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(_SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE ,PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(_SCREAMING_SNAKE_CASE )
elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get('''path''' )}
elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None ) -> "PIL.Image.Image":
if not self.decode:
raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.''' )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support decoding images, please install \'Pillow\'.''' )
if token_per_repo_id is None:
UpperCAmelCase_ : Dict = {}
UpperCAmelCase_, UpperCAmelCase_ : Union[str, Any] = value['''path'''], value['''bytes''']
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : Tuple = PIL.Image.open(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase_ : Dict = path.split('''::''' )[-1]
try:
UpperCAmelCase_ : Optional[int] = string_to_dict(_SCREAMING_SNAKE_CASE ,config.HUB_DATASETS_URL )['''repo_id''']
UpperCAmelCase_ : Tuple = token_per_repo_id.get(_SCREAMING_SNAKE_CASE )
except ValueError:
UpperCAmelCase_ : Optional[Any] = None
with xopen(_SCREAMING_SNAKE_CASE ,'''rb''' ,use_auth_token=_SCREAMING_SNAKE_CASE ) as f:
UpperCAmelCase_ : List[str] = BytesIO(f.read() )
UpperCAmelCase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
UpperCAmelCase_ : List[Any] = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def a__ ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
return (
self
if self.decode
else {
"bytes": Value('''binary''' ),
"path": Value('''string''' ),
}
)
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
UpperCAmelCase_ : Dict = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) ,type=pa.binary() )
UpperCAmelCase_ : Dict = pa.StructArray.from_arrays([bytes_array, storage] ,['''bytes''', '''path'''] ,mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
UpperCAmelCase_ : List[str] = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) ,type=pa.string() )
UpperCAmelCase_ : Tuple = pa.StructArray.from_arrays([storage, path_array] ,['''bytes''', '''path'''] ,mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('''bytes''' ) >= 0:
UpperCAmelCase_ : Dict = storage.field('''bytes''' )
else:
UpperCAmelCase_ : Any = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) ,type=pa.binary() )
if storage.type.get_field_index('''path''' ) >= 0:
UpperCAmelCase_ : int = storage.field('''path''' )
else:
UpperCAmelCase_ : List[str] = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) ,type=pa.string() )
UpperCAmelCase_ : Optional[Any] = pa.StructArray.from_arrays([bytes_array, path_array] ,['''bytes''', '''path'''] ,mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
UpperCAmelCase_ : Optional[Any] = pa.array(
[encode_np_array(np.array(_SCREAMING_SNAKE_CASE ) )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()] ,type=pa.binary() ,)
UpperCAmelCase_ : Any = pa.array([None] * len(_SCREAMING_SNAKE_CASE ) ,type=pa.string() )
UpperCAmelCase_ : Dict = pa.StructArray.from_arrays(
[bytes_array, path_array] ,['''bytes''', '''path'''] ,mask=bytes_array.is_null() )
return array_cast(_SCREAMING_SNAKE_CASE ,self.pa_type )
def a__ ( self ,_SCREAMING_SNAKE_CASE ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(_SCREAMING_SNAKE_CASE ):
with xopen(_SCREAMING_SNAKE_CASE ,'''rb''' ) as f:
UpperCAmelCase_ : Any = f.read()
return bytes_
UpperCAmelCase_ : Union[str, Any] = pa.array(
[
(path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
for x in storage.to_pylist()
] ,type=pa.binary() ,)
UpperCAmelCase_ : List[str] = pa.array(
[os.path.basename(_SCREAMING_SNAKE_CASE ) if path is not None else None for path in storage.field('''path''' ).to_pylist()] ,type=pa.string() ,)
UpperCAmelCase_ : Union[str, Any] = pa.StructArray.from_arrays([bytes_array, path_array] ,['''bytes''', '''path'''] ,mask=bytes_array.is_null() )
return array_cast(_SCREAMING_SNAKE_CASE ,self.pa_type )
def lowerCamelCase__ ( ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
UpperCAmelCase_ : Optional[int] = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = BytesIO()
if image.format in list_image_compression_formats():
UpperCAmelCase_ : int = image.format
else:
UpperCAmelCase_ : List[Any] = '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
image.save(_lowercase , format=_lowercase )
return buffer.getvalue()
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if hasattr(_lowercase , '''filename''' ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(_lowercase )}
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
UpperCAmelCase_ : Tuple = array.dtype
UpperCAmelCase_ : List[str] = dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
UpperCAmelCase_ : Dict = dtype.kind
UpperCAmelCase_ : Union[str, Any] = dtype.itemsize
UpperCAmelCase_ : Optional[Any] = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
UpperCAmelCase_ : Tuple = np.dtype('''|u1''' )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
UpperCAmelCase_ : Union[str, Any] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
UpperCAmelCase_ : Union[str, Any] = dtype_byteorder + dtype_kind + str(_lowercase )
UpperCAmelCase_ : str = np.dtype(_lowercase )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
UpperCAmelCase_ : Any = PIL.Image.fromarray(array.astype(_lowercase ) )
return {"path": None, "bytes": image_to_bytes(_lowercase )}
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('''To support encoding images, please install \'Pillow\'.''' )
if objs:
UpperCAmelCase_, UpperCAmelCase_ : Tuple = first_non_null_value(_lowercase )
if isinstance(_lowercase , _lowercase ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(_lowercase , np.ndarray ):
UpperCAmelCase_ : Any = no_op_if_value_is_null(_lowercase )
return [obj_to_image_dict_func(_lowercase ) for obj in objs]
elif isinstance(_lowercase , PIL.Image.Image ):
UpperCAmelCase_ : Union[str, Any] = no_op_if_value_is_null(_lowercase )
return [obj_to_image_dict_func(_lowercase ) for obj in objs]
else:
return objs
else:
return objs | 30 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : int ):
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : int , _lowerCAmelCase : Node ):
SCREAMING_SNAKE_CASE_ = tree
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : Node | None ):
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self : Dict ):
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod() | 31 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class __UpperCamelCase ( A__ ):
__A : List[Any] = """roc_bert"""
def __init__( self , _UpperCamelCase=30522 , _UpperCamelCase=768 , _UpperCamelCase=12 , _UpperCamelCase=12 , _UpperCamelCase=3072 , _UpperCamelCase="gelu" , _UpperCamelCase=0.1 , _UpperCamelCase=0.1 , _UpperCamelCase=512 , _UpperCamelCase=2 , _UpperCamelCase=0.02 , _UpperCamelCase=1e-12 , _UpperCamelCase=True , _UpperCamelCase=0 , _UpperCamelCase="absolute" , _UpperCamelCase=None , _UpperCamelCase=True , _UpperCamelCase=True , _UpperCamelCase=768 , _UpperCamelCase=910 , _UpperCamelCase=512 , _UpperCamelCase=24858 , _UpperCamelCase=True , **_UpperCamelCase , ):
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = use_cache
_UpperCAmelCase = enable_pronunciation
_UpperCAmelCase = enable_shape
_UpperCAmelCase = pronunciation_embed_dim
_UpperCAmelCase = pronunciation_vocab_size
_UpperCAmelCase = shape_embed_dim
_UpperCAmelCase = shape_vocab_size
_UpperCAmelCase = concat_input
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = classifier_dropout
super().__init__(pad_token_id=_UpperCamelCase , **_UpperCamelCase ) | 32 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase__ : List[str] = """▁"""
lowerCamelCase__ : Optional[int] = {"""vocab_file""": """sentencepiece.bpe.model"""}
lowerCamelCase__ : Optional[int] = {
"""vocab_file""": {
"""facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
}
}
lowerCamelCase__ : List[Any] = {
"""facebook/xglm-564M""": 2_0_4_8,
}
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : Optional[Any] = VOCAB_FILES_NAMES
__lowercase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowercase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : List[Any] = ['input_ids', 'attention_mask']
def __init__( self:int , _a:List[Any] , _a:List[str]="<s>" , _a:str="</s>" , _a:int="</s>" , _a:Optional[int]="<s>" , _a:Any="<unk>" , _a:int="<pad>" , _a:Optional[Dict[str, Any]] = None , **_a:int , ):
snake_case__ = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
snake_case__ = 7
snake_case__ = [F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
snake_case__ = kwargs.get('''additional_special_tokens''' , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
snake_case__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_a ) )
snake_case__ = 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'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
snake_case__ = 1
# Mimic fairseq token-to-id alignment for the first 4 token
snake_case__ = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
snake_case__ = len(self.sp_model )
snake_case__ = {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(_a )
snake_case__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self:Optional[int] ):
snake_case__ = self.__dict__.copy()
snake_case__ = None
snake_case__ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self:Optional[Any] , _a:Union[str, Any] ):
snake_case__ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
snake_case__ = {}
snake_case__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def SCREAMING_SNAKE_CASE__ ( self:Any , _a:List[int] , _a:Optional[List[int]] = None ):
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
snake_case__ = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def SCREAMING_SNAKE_CASE__ ( self:Dict , _a:List[int] , _a:Optional[List[int]] = None , _a:bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a ))
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a ))
def SCREAMING_SNAKE_CASE__ ( self:List[str] , _a:List[int] , _a:Optional[List[int]] = None ):
snake_case__ = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def SCREAMING_SNAKE_CASE__ ( self:Tuple ):
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def SCREAMING_SNAKE_CASE__ ( self:str ):
snake_case__ = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self:List[str] , _a:str ):
return self.sp_model.encode(_a , out_type=_a )
def SCREAMING_SNAKE_CASE__ ( self:str , _a:Optional[Any] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case__ = self.sp_model.PieceToId(_a )
# 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 SCREAMING_SNAKE_CASE__ ( self:Tuple , _a:List[Any] ):
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 SCREAMING_SNAKE_CASE__ ( self:int , _a:Any ):
snake_case__ = ''''''.join(_a ).replace(_a , ''' ''' ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self:Tuple , _a:str , _a:Optional[str] = None ):
if not os.path.isdir(_a ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case__ = os.path.join(
_a , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , '''wb''' ) as fi:
snake_case__ = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 33 |
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 __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
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 lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
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
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FocalNetForImageClassification',
'FocalNetForMaskedImageModeling',
'FocalNetBackbone',
'FocalNetModel',
'FocalNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 34 |
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_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a_ :int = 16
a_ :Tuple = 32
def a ( A__ , A__ = 1_6 ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
SCREAMING_SNAKE_CASE__ : List[str] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(A__ ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE__ : int = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=A__ , max_length=A__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE__ : Dict = datasets.map(
A__ , batched=A__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE__ : Dict = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(A__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE__ : Any = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE__ : Dict = 1_6
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE__ : Tuple = 8
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
return tokenizer.pad(
A__ , padding='''longest''' , max_length=A__ , pad_to_multiple_of=A__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE__ : Tuple = DataLoader(
tokenized_datasets['''train'''] , shuffle=A__ , collate_fn=A__ , batch_size=A__ )
SCREAMING_SNAKE_CASE__ : str = DataLoader(
tokenized_datasets['''validation'''] , shuffle=A__ , collate_fn=A__ , batch_size=A__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a_ :Union[str, Any] = mocked_dataloaders # noqa: F811
def a ( A__ , A__ ) -> int:
'''simple docstring'''
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , A__ ) == "1":
SCREAMING_SNAKE_CASE__ : Optional[int] = 2
# New Code #
SCREAMING_SNAKE_CASE__ : Union[str, Any] = int(args.gradient_accumulation_steps )
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(args.local_sgd_steps )
# Initialize accelerator
SCREAMING_SNAKE_CASE__ : Any = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=A__ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE__ : Any = config['''lr''']
SCREAMING_SNAKE_CASE__ : Any = int(config['''num_epochs'''] )
SCREAMING_SNAKE_CASE__ : List[str] = int(config['''seed'''] )
SCREAMING_SNAKE_CASE__ : Dict = int(config['''batch_size'''] )
SCREAMING_SNAKE_CASE__ : Tuple = evaluate.load('''glue''' , '''mrpc''' )
set_seed(A__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = get_dataloaders(A__ , A__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=A__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE__ : Dict = AdamW(params=model.parameters() , lr=A__ )
# Instantiate scheduler
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=A__ , num_warmup_steps=1_0_0 , num_training_steps=(len(A__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = accelerator.prepare(
A__ , A__ , A__ , A__ , A__ )
# Now we train the model
for epoch in range(A__ ):
model.train()
with LocalSGD(
accelerator=A__ , model=A__ , local_sgd_steps=A__ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(A__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(A__ ):
SCREAMING_SNAKE_CASE__ : Any = model(**A__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = output.loss
accelerator.backward(A__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(A__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[str] = model(**A__ )
SCREAMING_SNAKE_CASE__ : Tuple = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=A__ , references=A__ , )
SCREAMING_SNAKE_CASE__ : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , A__ )
def a ( ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=A__ , default=A__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' , type=A__ , default=1 , help='''The number of minibatches to be ran before gradients are accumulated.''' , )
parser.add_argument(
'''--local_sgd_steps''' , type=A__ , default=8 , help='''Number of local SGD steps or None to disable local SGD''' )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
SCREAMING_SNAKE_CASE__ : List[str] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Any = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
training_function(A__ , A__ )
if __name__ == "__main__":
main()
| 35 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 0 |
def lowercase ( __A : Tuple , __A : Optional[int] ) -> Optional[int]:
'''simple docstring'''
snake_case : List[Any] = [1]
for i in range(2 , __A ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
snake_case : List[str] = []
snake_case : Optional[Any] = list(range(__A ) )
# Find permutation
while factorials:
snake_case : str = factorials.pop()
snake_case , snake_case : str = divmod(__A , __A )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def UpperCamelCase_ ( __a ) -> Dict:
if is_torch_version("<" , "2.0.0" ) or not hasattr(__a , "_dynamo" ):
return False
return isinstance(__a , torch._dynamo.eval_frame.OptimizedModule )
def UpperCamelCase_ ( __a , __a = True ) -> Tuple:
a__ : Union[str, Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
a__ : List[Any] = is_compiled_module(__a )
if is_compiled:
a__ : Optional[int] = model
a__ : List[str] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(__a , __a ):
a__ : int = model.module
if not keep_fpaa_wrapper:
a__ : Union[str, Any] = getattr(__a , "forward" )
a__ : Union[str, Any] = model.__dict__.pop("_original_forward" , __a )
if original_forward is not None:
while hasattr(__a , "__wrapped__" ):
a__ : int = forward.__wrapped__
if forward == original_forward:
break
a__ : Any = forward
if getattr(__a , "_converted_to_transformer_engine" , __a ):
convert_model(__a , to_transformer_engine=__a )
if is_compiled:
a__ : List[str] = model
a__ : Optional[int] = compiled_model
return model
def UpperCamelCase_ ( ) -> int:
PartialState().wait_for_everyone()
def UpperCamelCase_ ( __a , __a ) -> int:
if PartialState().distributed_type == DistributedType.TPU:
xm.save(__a , __a )
elif PartialState().local_process_index == 0:
torch.save(__a , __a )
@contextmanager
def UpperCamelCase_ ( **__a ) -> Optional[int]:
for key, value in kwargs.items():
a__ : int = str(__a )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def UpperCamelCase_ ( __a ) -> Dict:
if not hasattr(__a , "__qualname__" ) and not hasattr(__a , "__name__" ):
a__ : Union[str, Any] = getattr(__a , "__class__" , __a )
if hasattr(__a , "__qualname__" ):
return obj.__qualname__
if hasattr(__a , "__name__" ):
return obj.__name__
return str(__a )
def UpperCamelCase_ ( __a , __a ) -> str:
for key, value in source.items():
if isinstance(__a , __a ):
a__ : Any = destination.setdefault(__a , {} )
merge_dicts(__a , __a )
else:
a__ : List[str] = value
return destination
def UpperCamelCase_ ( __a = None ) -> bool:
if port is None:
a__ : int = 29_500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 37 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 0 |
'''simple docstring'''
import math
def UpperCamelCase__ ( __magic_name__ : float , __magic_name__ : float ) -> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 3_60:
raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees""" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__magic_name__ ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name="malus_law")
| 38 |
import unittest
import numpy as np
from datasets import load_dataset
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 BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 0 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
'''--original_config_file''',
default=None,
type=str,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--scheduler_type''',
default='''pndm''',
type=str,
help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
)
parser.add_argument(
'''--pipeline_type''',
default=None,
type=str,
help=(
'''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
'''. If `None` pipeline will be automatically inferred.'''
),
)
parser.add_argument(
'''--image_size''',
default=None,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--prediction_type''',
default=None,
type=str,
help=(
'''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
parser.add_argument(
'''--stable_unclip''',
type=str,
default=None,
required=False,
help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
)
parser.add_argument(
'''--stable_unclip_prior''',
type=str,
default=None,
required=False,
help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
)
parser.add_argument(
'''--clip_stats_path''',
type=str,
help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
required=False,
)
parser.add_argument(
'''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
)
parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
parser.add_argument(
'''--vae_path''',
type=str,
default=None,
required=False,
help='''Set to a path, hub id to an already converted vae to not convert it again.''',
)
lowerCAmelCase_ = parser.parse_args()
lowerCAmelCase_ = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) | 39 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@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}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
__UpperCAmelCase = logging.get_logger(__name__)
def UpperCamelCase ( snake_case__ : Optional[int] ) -> List[List[ImageInput]]:
if isinstance(snake_case__ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(snake_case__ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(snake_case__ ):
return [[videos]]
raise ValueError(F"""Could not make batched video from {videos}""" )
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : Tuple = ["pixel_values"]
def __init__( self, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = 1 / 255, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> None:
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = size if size is not None else {'shortest_edge': 256}
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_, default_to_square=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = crop_size if crop_size is not None else {'height': 224, 'width': 224}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_, param_name='crop_size' )
UpperCamelCase : Optional[int] = do_resize
UpperCamelCase : Optional[Any] = size
UpperCamelCase : Any = do_center_crop
UpperCamelCase : Tuple = crop_size
UpperCamelCase : Dict = resample
UpperCamelCase : int = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = offset
UpperCamelCase : int = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : str = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> np.ndarray:
UpperCamelCase : Union[str, Any] = get_size_dict(SCREAMING_SNAKE_CASE_, default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" in size:
UpperCamelCase : List[str] = get_resize_output_image_size(SCREAMING_SNAKE_CASE_, size['shortest_edge'], default_to_square=SCREAMING_SNAKE_CASE_ )
elif "height" in size and "width" in size:
UpperCamelCase : Dict = (size['height'], size['width'])
else:
raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(SCREAMING_SNAKE_CASE_, size=SCREAMING_SNAKE_CASE_, resample=SCREAMING_SNAKE_CASE_, data_format=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> np.ndarray:
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(SCREAMING_SNAKE_CASE_, size=(size['height'], size['width']), data_format=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> Optional[int]:
UpperCamelCase : Tuple = image.astype(np.floataa )
if offset:
UpperCamelCase : Tuple = image - (scale / 2)
return rescale(SCREAMING_SNAKE_CASE_, scale=SCREAMING_SNAKE_CASE_, data_format=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> np.ndarray:
return normalize(SCREAMING_SNAKE_CASE_, mean=SCREAMING_SNAKE_CASE_, std=SCREAMING_SNAKE_CASE_, data_format=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST, ) -> np.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_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
if offset and not do_rescale:
raise ValueError('For offset, do_rescale must also be set to True.' )
# All transformations expect numpy arrays.
UpperCamelCase : Optional[int] = to_numpy_array(SCREAMING_SNAKE_CASE_ )
if do_resize:
UpperCamelCase : Dict = self.resize(image=SCREAMING_SNAKE_CASE_, size=SCREAMING_SNAKE_CASE_, resample=SCREAMING_SNAKE_CASE_ )
if do_center_crop:
UpperCamelCase : Union[str, Any] = self.center_crop(SCREAMING_SNAKE_CASE_, size=SCREAMING_SNAKE_CASE_ )
if do_rescale:
UpperCamelCase : Optional[Any] = self.rescale(image=SCREAMING_SNAKE_CASE_, scale=SCREAMING_SNAKE_CASE_, offset=SCREAMING_SNAKE_CASE_ )
if do_normalize:
UpperCamelCase : int = self.normalize(image=SCREAMING_SNAKE_CASE_, mean=SCREAMING_SNAKE_CASE_, std=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = to_channel_dimension_format(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
return image
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST, **SCREAMING_SNAKE_CASE_, ) -> PIL.Image.Image:
UpperCamelCase : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[str] = resample if resample is not None else self.resample
UpperCamelCase : int = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCamelCase : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : int = offset if offset is not None else self.offset
UpperCamelCase : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Optional[Any] = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : Dict = image_std if image_std is not None else self.image_std
UpperCamelCase : Optional[Any] = size if size is not None else self.size
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_, default_to_square=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = crop_size if crop_size is not None else self.crop_size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_, param_name='crop_size' )
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.' )
UpperCamelCase : Tuple = make_batched(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = [
[
self._preprocess_image(
image=SCREAMING_SNAKE_CASE_, do_resize=SCREAMING_SNAKE_CASE_, size=SCREAMING_SNAKE_CASE_, resample=SCREAMING_SNAKE_CASE_, do_center_crop=SCREAMING_SNAKE_CASE_, crop_size=SCREAMING_SNAKE_CASE_, do_rescale=SCREAMING_SNAKE_CASE_, rescale_factor=SCREAMING_SNAKE_CASE_, offset=SCREAMING_SNAKE_CASE_, do_normalize=SCREAMING_SNAKE_CASE_, image_mean=SCREAMING_SNAKE_CASE_, image_std=SCREAMING_SNAKE_CASE_, data_format=SCREAMING_SNAKE_CASE_, )
for img in video
]
for video in videos
]
UpperCamelCase : str = {'pixel_values': videos}
return BatchFeature(data=SCREAMING_SNAKE_CASE_, tensor_type=SCREAMING_SNAKE_CASE_ )
| 40 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _A ( A__ ):
"""simple docstring"""
if isinstance(A__ , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class lowercase_ :
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : int ,lowercase__ : Optional[Any] ,lowercase__ : Optional[int] ):
pass
def SCREAMING_SNAKE_CASE ( self : str ):
pass
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
pass
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : Dict ,lowercase__ : Optional[Any] ,lowercase__ : Union[str, Any] ,lowercase__ : str ,lowercase__ : Optional[int]=None ,**lowercase__ : str ):
__lowercase = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase__ ,lowercase__ )
__lowercase = TFVisionTextDualEncoderModel(lowercase__ )
__lowercase = model(input_ids=lowercase__ ,pixel_values=lowercase__ ,attention_mask=lowercase__ )
self.assertEqual(output['''text_embeds'''].shape ,(input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape ,(pixel_values.shape[0], config.projection_dim) )
def SCREAMING_SNAKE_CASE ( self : List[str] ,lowercase__ : List[Any] ,lowercase__ : Tuple ,lowercase__ : int ,lowercase__ : List[Any] ,lowercase__ : Optional[int]=None ,**lowercase__ : Any ):
__lowercase , __lowercase = self.get_vision_text_model(lowercase__ ,lowercase__ )
__lowercase = TFVisionTextDualEncoderModel(vision_model=lowercase__ ,text_model=lowercase__ )
__lowercase = model(input_ids=lowercase__ ,pixel_values=lowercase__ ,attention_mask=lowercase__ )
self.assertEqual(output['''text_embeds'''].shape ,(input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape ,(pixel_values.shape[0], model.config.projection_dim) )
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : str ,lowercase__ : Optional[int] ,lowercase__ : int ,lowercase__ : Optional[Any] ,lowercase__ : List[str]=None ,**lowercase__ : Any ):
__lowercase , __lowercase = self.get_vision_text_model(lowercase__ ,lowercase__ )
__lowercase = {'''vision_model''': vision_model, '''text_model''': text_model}
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase__ )
__lowercase = model(input_ids=lowercase__ ,pixel_values=lowercase__ ,attention_mask=lowercase__ )
self.assertEqual(output['''text_embeds'''].shape ,(input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape ,(pixel_values.shape[0], model.config.projection_dim) )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : Dict ,lowercase__ : str ,lowercase__ : List[str] ,lowercase__ : Tuple ,lowercase__ : Any=None ,**lowercase__ : Dict ):
__lowercase , __lowercase = self.get_vision_text_model(lowercase__ ,lowercase__ )
__lowercase = TFVisionTextDualEncoderModel(vision_model=lowercase__ ,text_model=lowercase__ )
__lowercase = model(input_ids=lowercase__ ,pixel_values=lowercase__ ,attention_mask=lowercase__ )
__lowercase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase__ )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(lowercase__ )
__lowercase = model(input_ids=lowercase__ ,pixel_values=lowercase__ ,attention_mask=lowercase__ )
__lowercase = after_output[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase__ ,1e-5 )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ,lowercase__ : int ,lowercase__ : str ,lowercase__ : List[Any] ,lowercase__ : List[str] ,lowercase__ : List[Any]=None ,**lowercase__ : List[Any] ):
__lowercase , __lowercase = self.get_vision_text_model(lowercase__ ,lowercase__ )
__lowercase = TFVisionTextDualEncoderModel(vision_model=lowercase__ ,text_model=lowercase__ )
__lowercase = model(
input_ids=lowercase__ ,pixel_values=lowercase__ ,attention_mask=lowercase__ ,output_attentions=lowercase__ )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(lowercase__ ) ,vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] ,(vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(lowercase__ ) ,text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) ,)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : np.ndarray ,lowercase__ : np.ndarray ,lowercase__ : float ):
__lowercase = np.abs((a - b) ).max()
self.assertLessEqual(lowercase__ ,lowercase__ ,F"Difference between torch and flax is {diff} (>= {tol})." )
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = self.prepare_config_and_inputs()
self.check_save_load(**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**lowercase__ )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase , __lowercase = self.get_pretrained_model_and_inputs()
__lowercase = model_a(**lowercase__ )
__lowercase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(lowercase__ )
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(lowercase__ )
__lowercase = model_a(**lowercase__ )
__lowercase = after_outputs[0].numpy()
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase__ ,1e-5 )
@require_tf
class lowercase_ (lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-vit''' ,'''hf-internal-testing/tiny-random-bert''' )
__lowercase = 1_3
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] ,model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE ( self : str ,lowercase__ : Optional[Any] ,lowercase__ : Union[str, Any] ):
__lowercase = TFViTModel(lowercase__ ,name='''vision_model''' )
__lowercase = TFBertModel(lowercase__ ,name='''text_model''' )
return vision_model, text_model
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = TFViTModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class lowercase_ (lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Any ):
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''Rocketknight1/tiny-random-deit-tf''' ,'''hf-internal-testing/tiny-random-roberta''' )
__lowercase = 1_3
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] ,model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : Optional[int] ,lowercase__ : Tuple ,lowercase__ : str ,lowercase__ : str ,lowercase__ : Optional[Any]=None ,**lowercase__ : str ):
__lowercase , __lowercase = self.get_vision_text_model(lowercase__ ,lowercase__ )
__lowercase = TFVisionTextDualEncoderModel(vision_model=lowercase__ ,text_model=lowercase__ )
__lowercase = model(
input_ids=lowercase__ ,pixel_values=lowercase__ ,attention_mask=lowercase__ ,output_attentions=lowercase__ )
__lowercase = output.vision_model_output.attentions
self.assertEqual(len(lowercase__ ) ,vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
__lowercase = to_atuple(vision_model.config.image_size )
__lowercase = to_atuple(vision_model.config.patch_size )
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowercase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] ,(vision_config.num_attention_heads, seq_len, seq_len) )
__lowercase = output.text_model_output.attentions
self.assertEqual(len(lowercase__ ) ,text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) ,)
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : Dict ,lowercase__ : Tuple ):
__lowercase = TFDeiTModel(lowercase__ ,name='''vision_model''' )
__lowercase = TFRobertaModel(lowercase__ ,name='''text_model''' )
return vision_model, text_model
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = TFDeiTModelTester(self )
__lowercase = TFRobertaModelTester(self )
__lowercase = vit_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class lowercase_ (lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''Rocketknight1/tiny-random-clip-tf''' ,'''hf-internal-testing/tiny-random-bert''' )
__lowercase = 1_3
__lowercase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
__lowercase = ids_tensor([batch_size, 4] ,model.text_model.config.vocab_size )
__lowercase = random_attention_mask([batch_size, 4] )
__lowercase = {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : Optional[Any] ,lowercase__ : Tuple ):
__lowercase = TFCLIPVisionModel(lowercase__ ,name='''vision_model''' )
__lowercase = TFBertModel(lowercase__ ,name='''text_model''' )
return vision_model, text_model
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = TFCLIPVisionModelTester(self )
__lowercase = TFBertModelTester(self )
__lowercase = clip_model_tester.prepare_config_and_inputs()
__lowercase = bert_model_tester.prepare_config_and_inputs()
__lowercase , __lowercase = vision_config_and_inputs
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = TFVisionTextDualEncoderModel.from_pretrained(
'''clip-italian/clip-italian''' ,logit_scale_init_value=1.0 ,from_pt=lowercase__ )
__lowercase = VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''' )
__lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
__lowercase = processor(
text=['''una foto di un gatto''', '''una foto di un cane'''] ,images=lowercase__ ,padding=lowercase__ ,return_tensors='''np''' )
__lowercase = model(**lowercase__ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape ,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape ,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) ,)
__lowercase = np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() ,lowercase__ ,atol=1e-3 ) )
| 41 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
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 lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} 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() ) )
| 60 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
super().__init__()
self.register_modules(unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 2000 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , **SCREAMING_SNAKE_CASE_ , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
lowerCamelCase_ = self.unet.config.sample_size
lowerCamelCase_ = (batch_size, 3, img_size, img_size)
lowerCamelCase_ = self.unet
lowerCamelCase_ = randn_tensor(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ ) * self.scheduler.init_noise_sigma
lowerCamelCase_ = sample.to(self.device )
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE_ )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowerCamelCase_ = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
lowerCamelCase_ = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).sample
lowerCamelCase_ = self.scheduler.step_correct(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ ).prev_sample
# prediction step
lowerCamelCase_ = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).sample
lowerCamelCase_ = self.scheduler.step_pred(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ ,lowerCamelCase_ = output.prev_sample, output.prev_sample_mean
lowerCamelCase_ = sample_mean.clamp(0 , 1 )
lowerCamelCase_ = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
| 42 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class _a ( UpperCamelCase__ ):
def lowerCamelCase_ ( self: Dict ) -> List[str]:
"""simple docstring"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = 5
# Realm tok
lowercase__ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''test''',
'''question''',
'''this''',
'''is''',
'''the''',
'''first''',
'''second''',
'''third''',
'''fourth''',
'''fifth''',
'''record''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
lowercase__ = os.path.join(self.tmpdirname , '''realm_tokenizer''' )
os.makedirs(UpperCamelCase_ , exist_ok=UpperCamelCase_ )
lowercase__ = os.path.join(UpperCamelCase_ , 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] ) )
lowercase__ = os.path.join(self.tmpdirname , '''realm_block_records''' )
os.makedirs(UpperCamelCase_ , exist_ok=UpperCamelCase_ )
def lowerCamelCase_ ( self: str ) -> RealmTokenizer:
"""simple docstring"""
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''realm_tokenizer''' ) )
def lowerCamelCase_ ( self: Optional[int] ) -> Optional[int]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase_ ( self: List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = RealmConfig(num_block_records=self.num_block_records )
return config
def lowerCamelCase_ ( self: str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''question''': ['''foo''', '''bar'''],
'''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
} )
return dataset
def lowerCamelCase_ ( self: int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = np.array(
[
b'''This is the first record''',
b'''This is the second record''',
b'''This is the third record''',
b'''This is the fourth record''',
b'''This is the fifth record''',
b'''This is a longer longer longer record''',
] , dtype=UpperCamelCase_ , )
return block_records
def lowerCamelCase_ ( self: Optional[Any] ) -> int:
"""simple docstring"""
lowercase__ = RealmRetriever(
block_records=self.get_dummy_block_records() , tokenizer=self.get_tokenizer() , )
return retriever
def lowerCamelCase_ ( self: int ) -> Dict:
"""simple docstring"""
lowercase__ = self.get_config()
lowercase__ = self.get_dummy_retriever()
lowercase__ = retriever.tokenizer
lowercase__ = np.array([0, 3] , dtype='''long''' )
lowercase__ = tokenizer(['''Test question'''] ).input_ids
lowercase__ = tokenizer(
['''the fourth'''] , add_special_tokens=UpperCamelCase_ , return_token_type_ids=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , ).input_ids
lowercase__ = config.reader_seq_len
lowercase__ , lowercase__ , lowercase__ , lowercase__ = retriever(
UpperCamelCase_ , UpperCamelCase_ , answer_ids=UpperCamelCase_ , max_length=UpperCamelCase_ , return_tensors='''np''' )
self.assertEqual(len(UpperCamelCase_ ) , 2 )
self.assertEqual(len(UpperCamelCase_ ) , 2 )
self.assertEqual(len(UpperCamelCase_ ) , 2 )
self.assertEqual(concat_inputs.input_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.attention_mask.shape , (2, 10) )
self.assertEqual(concat_inputs.token_type_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.special_tokens_mask.shape , (2, 10) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) , ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]'''] , )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) , ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]'''] , )
def lowerCamelCase_ ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_config()
lowercase__ = self.get_dummy_retriever()
lowercase__ = retriever.tokenizer
lowercase__ = np.array([0, 3, 5] , dtype='''long''' )
lowercase__ = tokenizer(['''Test question'''] ).input_ids
lowercase__ = tokenizer(
['''the fourth''', '''longer longer'''] , add_special_tokens=UpperCamelCase_ , return_token_type_ids=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , ).input_ids
lowercase__ = config.reader_seq_len
lowercase__ , lowercase__ , lowercase__ , lowercase__ = retriever(
UpperCamelCase_ , UpperCamelCase_ , answer_ids=UpperCamelCase_ , max_length=UpperCamelCase_ , return_tensors='''np''' )
self.assertEqual([False, True, True] , UpperCamelCase_ )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] , UpperCamelCase_ )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] , UpperCamelCase_ )
def lowerCamelCase_ ( self: Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname , '''realm_block_records''' ) )
# Test local path
lowercase__ = retriever.from_pretrained(os.path.join(self.tmpdirname , '''realm_block_records''' ) )
self.assertEqual(retriever.block_records[0] , b'''This is the first record''' )
# Test mocked remote path
with patch('''transformers.models.realm.retrieval_realm.hf_hub_download''' ) as mock_hf_hub_download:
lowercase__ = os.path.join(
os.path.join(self.tmpdirname , '''realm_block_records''' ) , _REALM_BLOCK_RECORDS_FILENAME )
lowercase__ = RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa''' )
self.assertEqual(retriever.block_records[0] , b'''This is the first record''' )
| 43 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {
'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = 'xlm-roberta-xl'
def __init__( self : str,__A : Optional[Any]=2_5_0_8_8_0,__A : str=2_5_6_0,__A : Dict=3_6,__A : int=3_2,__A : int=1_0_2_4_0,__A : Union[str, Any]="gelu",__A : Optional[Any]=0.1,__A : Tuple=0.1,__A : Any=5_1_4,__A : int=1,__A : Dict=0.02,__A : Any=1e-05,__A : str=1,__A : Optional[int]=0,__A : Tuple=2,__A : Dict="absolute",__A : Dict=True,__A : str=None,**__A : Any,):
super().__init__(pad_token_id=__A,bos_token_id=__A,eos_token_id=__A,**__A )
_lowerCamelCase : Tuple = vocab_size
_lowerCamelCase : List[Any] = hidden_size
_lowerCamelCase : Union[str, Any] = num_hidden_layers
_lowerCamelCase : Optional[int] = num_attention_heads
_lowerCamelCase : Dict = hidden_act
_lowerCamelCase : Tuple = intermediate_size
_lowerCamelCase : int = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : List[Any] = max_position_embeddings
_lowerCamelCase : List[Any] = type_vocab_size
_lowerCamelCase : Optional[Any] = initializer_range
_lowerCamelCase : str = layer_norm_eps
_lowerCamelCase : Optional[int] = position_embedding_type
_lowerCamelCase : int = use_cache
_lowerCamelCase : str = classifier_dropout
class UpperCAmelCase__ ( A ):
@property
def lowerCamelCase_ ( self : int ):
if self.task == "multiple-choice":
_lowerCamelCase : Optional[int] = {0: "batch", 1: "choice", 2: "sequence"}
else:
_lowerCamelCase : Optional[int] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] ) | 44 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCAmelCase_ ( lowercase , unittest.TestCase ):
"""simple docstring"""
_snake_case : Any = UnCLIPImageVariationPipeline
_snake_case : int = IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
_snake_case : Optional[int] = IMAGE_VARIATION_BATCH_PARAMS
_snake_case : List[Any] = [
"""generator""",
"""return_dict""",
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
_snake_case : Optional[Any] = False
@property
def __a ( self :Optional[int] ):
return 32
@property
def __a ( self :Tuple ):
return 32
@property
def __a ( self :Tuple ):
return self.time_input_dim
@property
def __a ( self :Any ):
return self.time_input_dim * 4
@property
def __a ( self :Any ):
return 1_00
@property
def __a ( self :str ):
UpperCamelCase__ :Any = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def __a ( self :List[Any] ):
torch.manual_seed(0 )
UpperCamelCase__ :List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModelWithProjection(lowerCamelCase__ )
@property
def __a ( self :Tuple ):
torch.manual_seed(0 )
UpperCamelCase__ :List[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(lowerCamelCase__ )
@property
def __a ( self :int ):
torch.manual_seed(0 )
UpperCamelCase__ :List[str] = {
"""clip_embeddings_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""cross_attention_dim""": self.cross_attention_dim,
}
UpperCamelCase__ :Optional[int] = UnCLIPTextProjModel(**lowerCamelCase__ )
return model
@property
def __a ( self :Any ):
torch.manual_seed(0 )
UpperCamelCase__ :Optional[Any] = {
"""sample_size""": 32,
# RGB in channels
"""in_channels""": 3,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 6,
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": """identity""",
}
UpperCamelCase__ :str = UNetaDConditionModel(**lowerCamelCase__ )
return model
@property
def __a ( self :List[str] ):
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def __a ( self :Optional[Any] ):
torch.manual_seed(0 )
UpperCamelCase__ :Union[str, Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def __a ( self :Union[str, Any] ):
# seeded differently to get different unet than `self.dummy_super_res_first`
torch.manual_seed(1 )
UpperCamelCase__ :List[Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def __a ( self :Any ):
UpperCamelCase__ :List[str] = self.dummy_decoder
UpperCamelCase__ :List[Any] = self.dummy_text_proj
UpperCamelCase__ :str = self.dummy_text_encoder
UpperCamelCase__ :Any = self.dummy_tokenizer
UpperCamelCase__ :str = self.dummy_super_res_first
UpperCamelCase__ :Union[str, Any] = self.dummy_super_res_last
UpperCamelCase__ :List[Any] = UnCLIPScheduler(
variance_type="""learned_range""" , prediction_type="""epsilon""" , num_train_timesteps=10_00 , )
UpperCamelCase__ :Any = UnCLIPScheduler(
variance_type="""fixed_small_log""" , prediction_type="""epsilon""" , num_train_timesteps=10_00 , )
UpperCamelCase__ :Tuple = CLIPImageProcessor(crop_size=32 , size=32 )
UpperCamelCase__ :int = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def __a ( self :str , lowerCamelCase__ :Dict , lowerCamelCase__ :Union[str, Any]=0 , lowerCamelCase__ :Tuple=True ):
UpperCamelCase__ :Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
if str(lowerCamelCase__ ).startswith("""mps""" ):
UpperCamelCase__ :List[Any] = torch.manual_seed(lowerCamelCase__ )
else:
UpperCamelCase__ :List[Any] = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
if pil_image:
UpperCamelCase__ :Dict = input_image * 0.5 + 0.5
UpperCamelCase__ :str = input_image.clamp(0 , 1 )
UpperCamelCase__ :Any = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCamelCase__ :int = DiffusionPipeline.numpy_to_pil(lowerCamelCase__ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def __a ( self :int ):
UpperCamelCase__ :List[str] = """cpu"""
UpperCamelCase__ :Tuple = self.get_dummy_components()
UpperCamelCase__ :List[str] = self.pipeline_class(**lowerCamelCase__ )
UpperCamelCase__ :Optional[Any] = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCamelCase__ :Tuple = self.get_dummy_inputs(lowerCamelCase__ , pil_image=lowerCamelCase__ )
UpperCamelCase__ :Optional[Any] = pipe(**lowerCamelCase__ )
UpperCamelCase__ :List[Any] = output.images
UpperCamelCase__ :int = self.get_dummy_inputs(lowerCamelCase__ , pil_image=lowerCamelCase__ )
UpperCamelCase__ :List[str] = pipe(
**lowerCamelCase__ , return_dict=lowerCamelCase__ , )[0]
UpperCamelCase__ :int = image[0, -3:, -3:, -1]
UpperCamelCase__ :Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase__ :Optional[int] = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __a ( self :Union[str, Any] ):
UpperCamelCase__ :Dict = """cpu"""
UpperCamelCase__ :Dict = self.get_dummy_components()
UpperCamelCase__ :Tuple = self.pipeline_class(**lowerCamelCase__ )
UpperCamelCase__ :str = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCamelCase__ :Union[str, Any] = self.get_dummy_inputs(lowerCamelCase__ , pil_image=lowerCamelCase__ )
UpperCamelCase__ :Union[str, Any] = pipe(**lowerCamelCase__ )
UpperCamelCase__ :Dict = output.images
UpperCamelCase__ :Optional[int] = self.get_dummy_inputs(lowerCamelCase__ , pil_image=lowerCamelCase__ )
UpperCamelCase__ :Tuple = pipe(
**lowerCamelCase__ , return_dict=lowerCamelCase__ , )[0]
UpperCamelCase__ :List[str] = image[0, -3:, -3:, -1]
UpperCamelCase__ :Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase__ :Union[str, Any] = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __a ( self :Optional[Any] ):
UpperCamelCase__ :Union[str, Any] = """cpu"""
UpperCamelCase__ :Optional[Any] = self.get_dummy_components()
UpperCamelCase__ :Union[str, Any] = self.pipeline_class(**lowerCamelCase__ )
UpperCamelCase__ :int = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCamelCase__ :List[str] = self.get_dummy_inputs(lowerCamelCase__ , pil_image=lowerCamelCase__ )
UpperCamelCase__ :List[Any] = [
pipeline_inputs["""image"""],
pipeline_inputs["""image"""],
]
UpperCamelCase__ :Dict = pipe(**lowerCamelCase__ )
UpperCamelCase__ :Optional[Any] = output.images
UpperCamelCase__ :Optional[int] = self.get_dummy_inputs(lowerCamelCase__ , pil_image=lowerCamelCase__ )
UpperCamelCase__ :Union[str, Any] = [
tuple_pipeline_inputs["""image"""],
tuple_pipeline_inputs["""image"""],
]
UpperCamelCase__ :Dict = pipe(
**lowerCamelCase__ , return_dict=lowerCamelCase__ , )[0]
UpperCamelCase__ :Dict = image[0, -3:, -3:, -1]
UpperCamelCase__ :Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
UpperCamelCase__ :List[Any] = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __a ( self :Dict ):
UpperCamelCase__ :List[Any] = torch.device("""cpu""" )
class lowerCAmelCase_ :
"""simple docstring"""
_snake_case : int = 1
UpperCamelCase__ :Union[str, Any] = self.get_dummy_components()
UpperCamelCase__ :List[str] = self.pipeline_class(**lowerCamelCase__ )
UpperCamelCase__ :Dict = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCamelCase__ :int = torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
UpperCamelCase__ :List[Any] = pipe.decoder.dtype
UpperCamelCase__ :str = 1
UpperCamelCase__ :List[str] = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
UpperCamelCase__ :int = pipe.prepare_latents(
lowerCamelCase__ , dtype=lowerCamelCase__ , device=lowerCamelCase__ , generator=lowerCamelCase__ , latents=lowerCamelCase__ , scheduler=DummyScheduler() )
UpperCamelCase__ :int = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
UpperCamelCase__ :Optional[int] = pipe.prepare_latents(
lowerCamelCase__ , dtype=lowerCamelCase__ , device=lowerCamelCase__ , generator=lowerCamelCase__ , latents=lowerCamelCase__ , scheduler=DummyScheduler() )
UpperCamelCase__ :Any = self.get_dummy_inputs(lowerCamelCase__ , pil_image=lowerCamelCase__ )
UpperCamelCase__ :int = pipe(
**lowerCamelCase__ , decoder_latents=lowerCamelCase__ , super_res_latents=lowerCamelCase__ ).images
UpperCamelCase__ :int = self.get_dummy_inputs(lowerCamelCase__ , pil_image=lowerCamelCase__ )
# Don't pass image, instead pass embedding
UpperCamelCase__ :List[Any] = pipeline_inputs.pop("""image""" )
UpperCamelCase__ :str = pipe.image_encoder(lowerCamelCase__ ).image_embeds
UpperCamelCase__ :List[Any] = pipe(
**lowerCamelCase__ , decoder_latents=lowerCamelCase__ , super_res_latents=lowerCamelCase__ , image_embeddings=lowerCamelCase__ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1e-4
@skip_mps
def __a ( self :Union[str, Any] ):
UpperCamelCase__ :Dict = torch_device == """cpu"""
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
UpperCamelCase__ :Union[str, Any] = 1e-2
self._test_attention_slicing_forward_pass(
test_max_difference=lowerCamelCase__ , expected_max_diff=lowerCamelCase__ )
@skip_mps
def __a ( self :Union[str, Any] ):
UpperCamelCase__ :Tuple = torch_device == """cpu"""
UpperCamelCase__ :Optional[int] = True
UpperCamelCase__ :Union[str, Any] = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
self._test_inference_batch_single_identical(
test_max_difference=lowerCamelCase__ , relax_max_difference=lowerCamelCase__ , additional_params_copy_to_batched_inputs=lowerCamelCase__ , )
def __a ( self :List[str] ):
UpperCamelCase__ :Any = [
"""decoder_num_inference_steps""",
"""super_res_num_inference_steps""",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
UpperCamelCase__ :List[str] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=lowerCamelCase__ , additional_params_copy_to_batched_inputs=lowerCamelCase__ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=lowerCamelCase__ )
@skip_mps
def __a ( self :List[str] ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def __a ( self :Dict ):
return super().test_save_load_local()
@skip_mps
def __a ( self :Tuple ):
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __a ( self :Optional[int] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self :Tuple ):
UpperCamelCase__ :str = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png""" )
UpperCamelCase__ :List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/unclip/karlo_v1_alpha_cat_variation_fp16.npy""" )
UpperCamelCase__ :int = UnCLIPImageVariationPipeline.from_pretrained(
"""kakaobrain/karlo-v1-alpha-image-variations""" , torch_dtype=torch.floataa )
UpperCamelCase__ :Optional[int] = pipeline.to(lowerCamelCase__ )
pipeline.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCamelCase__ :Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
UpperCamelCase__ :List[str] = pipeline(
lowerCamelCase__ , generator=lowerCamelCase__ , output_type="""np""" , )
UpperCamelCase__ :Union[str, Any] = output.images[0]
assert image.shape == (2_56, 2_56, 3)
assert_mean_pixel_difference(lowerCamelCase__ , lowerCamelCase__ , 15 ) | 45 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
class A_ ( _a ):
def __init__( self: List[Any] ,**__lowerCAmelCase: int ):
'''simple docstring'''
requires_backends(self ,["bs4"] )
super().__init__(**__lowerCAmelCase )
def _lowercase ( self: Optional[Any] ,__lowerCAmelCase: Any ):
'''simple docstring'''
_lowerCamelCase : str = []
_lowerCamelCase : Tuple = []
_lowerCamelCase : Any = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_lowerCamelCase : List[str] = parent.find_all(child.name ,recursive=__lowerCAmelCase )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(__lowerCAmelCase ) else next(i for i, s in enumerate(__lowerCAmelCase ,1 ) if s is child ) )
_lowerCamelCase : List[Any] = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def _lowercase ( self: List[str] ,__lowerCAmelCase: List[str] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = BeautifulSoup(__lowerCAmelCase ,"html.parser" )
_lowerCamelCase : List[Any] = []
_lowerCamelCase : List[str] = []
_lowerCamelCase : List[Any] = []
for element in html_code.descendants:
if type(__lowerCAmelCase ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_lowerCamelCase : List[str] = html.unescape(__lowerCAmelCase ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(__lowerCAmelCase )
_lowerCamelCase, _lowerCamelCase : Optional[Any] = self.xpath_soup(__lowerCAmelCase )
stringaxtag_seq.append(__lowerCAmelCase )
stringaxsubs_seq.append(__lowerCAmelCase )
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
raise ValueError("Number of doc strings and xtags does not correspond" )
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
raise ValueError("Number of doc strings and xsubs does not correspond" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def _lowercase ( self: Tuple ,__lowerCAmelCase: Union[str, Any] ,__lowerCAmelCase: Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : Tuple = ""
for tagname, subs in zip(__lowerCAmelCase ,__lowerCAmelCase ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self: List[str] ,__lowerCAmelCase: List[Any] ):
'''simple docstring'''
_lowerCamelCase : int = False
# Check that strings has a valid type
if isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : Dict = True
elif isinstance(__lowerCAmelCase ,(list, tuple) ):
if len(__lowerCAmelCase ) == 0 or isinstance(html_strings[0] ,__lowerCAmelCase ):
_lowerCamelCase : Tuple = True
if not valid_strings:
raise ValueError(
"HTML strings must of type `str`, `List[str]` (batch of examples), "
F"""but is of type {type(__lowerCAmelCase )}.""" )
_lowerCamelCase : Optional[int] = bool(isinstance(__lowerCAmelCase ,(list, tuple) ) and (isinstance(html_strings[0] ,__lowerCAmelCase )) )
if not is_batched:
_lowerCamelCase : Dict = [html_strings]
# Get nodes + xpaths
_lowerCamelCase : List[Any] = []
_lowerCamelCase : List[Any] = []
for html_string in html_strings:
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : List[Any] = self.get_three_from_single(__lowerCAmelCase )
nodes.append(__lowerCAmelCase )
_lowerCamelCase : Optional[int] = []
for node, tag_list, sub_list in zip(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : Tuple = self.construct_xpath(__lowerCAmelCase ,__lowerCAmelCase )
xpath_strings.append(__lowerCAmelCase )
xpaths.append(__lowerCAmelCase )
# return as Dict
_lowerCamelCase : Optional[Any] = {"nodes": nodes, "xpaths": xpaths}
_lowerCamelCase : Union[str, Any] = BatchFeature(data=__lowerCAmelCase ,tensor_type=__lowerCAmelCase )
return encoded_inputs | 46 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 0 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class _UpperCamelCase( unittest.TestCase ):
def __lowerCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
'''simple docstring'''
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , len(SCREAMING_SNAKE_CASE__ ) )
for a, b in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self.assertAlmostEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , delta=SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Any ):
'''simple docstring'''
__a : List[Any] = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
__a : int = None
ops.enable_eager_execution_internal()
__a : Optional[Any] = tf.config.list_physical_devices('CPU' )
if len(SCREAMING_SNAKE_CASE__ ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
__a : int = tf.config.list_logical_devices(device_type='CPU' )
__a : str = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
__a : List[str] = GradientAccumulator()
__a : Tuple = tf.Variable([4.0, 3.0] )
__a , __a : int = create_optimizer(5e-5 , 1_0 , 5 )
__a : List[Any] = tf.Variable([0.0, 0.0] , trainable=SCREAMING_SNAKE_CASE__ )
def accumulate_on_replica(SCREAMING_SNAKE_CASE__ : Optional[Any] ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple ):
with strategy.scope():
__a : Optional[Any] = strategy.experimental_local_results(SCREAMING_SNAKE_CASE__ )
local_variables[0].assign(SCREAMING_SNAKE_CASE__ )
local_variables[1].assign(SCREAMING_SNAKE_CASE__ )
strategy.run(SCREAMING_SNAKE_CASE__ , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(SCREAMING_SNAKE_CASE__ )
def _check_local_values(SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : int ):
__a : Union[str, Any] = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , SCREAMING_SNAKE_CASE__ , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , SCREAMING_SNAKE_CASE__ , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 47 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 0 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A ( UpperCamelCase_ : List[Any] , UpperCamelCase_ : int , UpperCamelCase_ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase__ = 0
if start < end:
lowerCAmelCase__ = randint(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase__ = a[end]
lowerCAmelCase__ = a[pivot]
lowerCAmelCase__ = temp
lowerCAmelCase__ ,lowerCAmelCase__ = _in_place_partition(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
count += _in_place_quick_sort(UpperCamelCase_ , UpperCamelCase_ , p - 1 )
count += _in_place_quick_sort(UpperCamelCase_ , p + 1 , UpperCamelCase_ )
return count
def A ( UpperCamelCase_ : Tuple , UpperCamelCase_ : List[str] , UpperCamelCase_ : Any ) -> Dict:
'''simple docstring'''
lowerCAmelCase__ = 0
lowerCAmelCase__ = randint(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase__ = a[end]
lowerCAmelCase__ = a[pivot]
lowerCAmelCase__ = temp
lowerCAmelCase__ = start - 1
for index in range(UpperCamelCase_ , UpperCamelCase_ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
lowerCAmelCase__ = new_pivot_index + 1
lowerCAmelCase__ = a[new_pivot_index]
lowerCAmelCase__ = a[index]
lowerCAmelCase__ = temp
lowerCAmelCase__ = a[new_pivot_index + 1]
lowerCAmelCase__ = a[end]
lowerCAmelCase__ = temp
return new_pivot_index + 1, count
UpperCAmelCase__ : Tuple = TemporaryFile()
UpperCAmelCase__ : List[str] = 1_00 # 1000 elements are to be sorted
UpperCAmelCase__ , UpperCAmelCase__ : Dict = 0, 1 # mean and standard deviation
UpperCAmelCase__ : Tuple = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
UpperCAmelCase__ : Optional[Any] = np.load(outfile)
UpperCAmelCase__ : Any = len(M) - 1
UpperCAmelCase__ : Tuple = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| 48 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :dict ):
__UpperCAmelCase = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__UpperCAmelCase = set()
return any(
node not in visited and depth_first_search(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
for node in graph )
def lowercase__ ( snake_case_ :dict , snake_case_ :int , snake_case_ :set , snake_case_ :set ):
visited.add(snake_case_ )
rec_stk.add(snake_case_ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(snake_case_ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 49 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 0 |
'''simple docstring'''
UpperCamelCase : Optional[int] = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCamelCase : Tuple = ['a', 'b', 'c', 'd', 'e']
def A__ ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict ):
lowerCamelCase__ = start
# add current to visited
visited.append(__lowerCAmelCase )
lowerCamelCase__ = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
lowerCamelCase__ = topological_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# if all neighbors visited add current to sort
sort.append(__lowerCAmelCase )
# if all vertices haven't been visited select a new one to visit
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
for vertice in vertices:
if vertice not in visited:
lowerCamelCase__ = topological_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# return sort
return sort
if __name__ == "__main__":
UpperCamelCase : int = topological_sort('a', [], [])
print(sort)
| 50 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
| 60 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , a__ : Optional[Any] , a__ : Any=13 , a__ : str=3 , a__ : List[Any]=224 , a__ : Any=30 , a__ : List[Any]=400 , a__ : Optional[int]=True , a__ : str=None , a__ : Dict=True , a__ : Tuple=[0.5, 0.5, 0.5] , a__ : List[str]=[0.5, 0.5, 0.5] , ):
UpperCAmelCase = size if size is not None else {'''height''': 18, '''width''': 18}
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = num_channels
UpperCAmelCase = image_size
UpperCAmelCase = min_resolution
UpperCAmelCase = max_resolution
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean
UpperCAmelCase = image_std
def __snake_case ( self : List[str] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class lowerCAmelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase =ViTImageProcessor if is_vision_available() else None
def __snake_case ( self : int ):
UpperCAmelCase = EfficientFormerImageProcessorTester(self )
@property
def __snake_case ( self : Optional[int] ):
return self.image_proc_tester.prepare_image_processor_dict()
def __snake_case ( self : Tuple ):
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a__ , '''image_mean''' ) )
self.assertTrue(hasattr(a__ , '''image_std''' ) )
self.assertTrue(hasattr(a__ , '''do_normalize''' ) )
self.assertTrue(hasattr(a__ , '''do_resize''' ) )
self.assertTrue(hasattr(a__ , '''size''' ) )
def __snake_case ( self : Optional[int] ):
pass
def __snake_case ( self : List[str] ):
# Initialize image_processor
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=a__ )
for image in image_inputs:
self.assertIsInstance(a__ , Image.Image )
# Test not batched input
UpperCAmelCase = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
UpperCAmelCase = image_processor(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
def __snake_case ( self : int ):
# Initialize image_processor
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=a__ , numpify=a__ )
for image in image_inputs:
self.assertIsInstance(a__ , np.ndarray )
# Test not batched input
UpperCAmelCase = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
UpperCAmelCase = image_processor(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
def __snake_case ( self : List[Any] ):
# Initialize image_processor
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase = prepare_image_inputs(self.image_proc_tester , equal_resolution=a__ , torchify=a__ )
for image in image_inputs:
self.assertIsInstance(a__ , torch.Tensor )
# Test not batched input
UpperCAmelCase = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
UpperCAmelCase = image_processor(a__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
| 51 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 0 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def __A ( a_ :str) -> List[Any]:
def decorator(a_ :List[Any]):
__a : List[str] = getattr(a_ , '''handle_key''' , [])
handle += [key]
setattr(a_ , '''handle_key''' , a_)
return func
return decorator
def __A ( *a_ :List[str]) -> Optional[int]:
def decorator(a_ :int):
__a : Tuple = getattr(a_ , '''handle_key''' , [])
handle += keys
setattr(a_ , '''handle_key''' , a_)
return func
return decorator
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __new__( cls , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : Tuple = super().__new__(cls , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if not hasattr(_UpperCAmelCase , '''key_handler''' ):
setattr(_UpperCAmelCase , '''key_handler''' , {} )
setattr(_UpperCAmelCase , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
__a : Dict = getattr(_UpperCAmelCase , '''handle_key''' , [] )
for key in handled_keys:
__a : Union[str, Any] = value
return new_cls
@staticmethod
def _lowerCamelCase ( cls ):
__a : Dict = get_character()
if char != KEYMAP["undefined"]:
__a : str = ord(_UpperCAmelCase )
__a : Tuple = cls.key_handler.get(_UpperCAmelCase )
if handler:
__a : Union[str, Any] = char
return handler(cls )
else:
return None
def __A ( cls :Union[str, Any]) -> str:
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy()) | 52 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_snake_case : Any = logging.get_logger(__name__)
_snake_case : int = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_snake_case : Optional[Any] = {
'vocab_file': {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
),
},
}
_snake_case : str = {
'yjernite/retribert-base-uncased': 512,
}
_snake_case : Optional[int] = {
'yjernite/retribert-base-uncased': {'do_lower_case': True},
}
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = PRETRAINED_INIT_CONFIGURATION
a_ = RetriBertTokenizer
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self : Dict , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : str="[UNK]" , lowerCAmelCase_ : Optional[Any]="[SEP]" , lowerCAmelCase_ : List[str]="[PAD]" , lowerCAmelCase_ : Optional[int]="[CLS]" , lowerCAmelCase_ : List[Any]="[MASK]" , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : List[str]=None , **lowerCAmelCase_ : List[Any] , ) -> Dict:
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_ , )
__lowerCAmelCase = 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
):
__lowerCAmelCase = getattr(lowerCAmelCase_ , normalizer_state.pop('type' ) )
__lowerCAmelCase = do_lower_case
__lowerCAmelCase = strip_accents
__lowerCAmelCase = tokenize_chinese_chars
__lowerCAmelCase = normalizer_class(**lowerCAmelCase_ )
__lowerCAmelCase = do_lower_case
def lowercase ( self : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[int]=None ) -> Optional[int]:
__lowerCAmelCase = [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 ) -> List[int]:
__lowerCAmelCase = [self.sep_token_id]
__lowerCAmelCase = [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 : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
__lowerCAmelCase = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ )
return tuple(lowerCAmelCase_ )
| 53 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
__lowercase : Dict ="""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""
__lowercase : List[str] ="""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
'google_bleu': google_bleu score
Examples:
Example 1:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.44
Example 2:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results[\"google_bleu\"], 2))
0.61
Example 3:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results[\"google_bleu\"], 2))
0.53
Example 4:
>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',
... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']
>>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']
>>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
... 'heed', 'the', 'cat', 'commands']
>>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
... 'of', 'the', 'cat']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
... 'interested', 'in', 'world', 'history']
>>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
... 'because', 'he', 'read', 'the', 'book']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric(\"google_bleu\")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results[\"google_bleu\"], 2))
0.4
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
def lowerCAmelCase__ ( self: int ) -> MetricInfo:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , )
def lowerCAmelCase__ ( self: List[str] , _lowerCAmelCase: List[List[List[str]]] , _lowerCAmelCase: List[List[str]] , _lowerCAmelCase: int = 1 , _lowerCAmelCase: int = 4 , ) -> Dict[str, float]:
'''simple docstring'''
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=_lowerCAmelCase , hypotheses=_lowerCAmelCase , min_len=_lowerCAmelCase , max_len=_lowerCAmelCase )
}
| 54 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : Any ):
__A = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(A ,"tf_padding" ) )
self.parent.assertTrue(hasattr(A ,"depth_multiplier" ) )
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ,A : int ,A : List[Any]=13 ,A : int=3 ,A : Optional[Any]=32 ,A : Union[str, Any]=0.25 ,A : Tuple=8 ,A : Optional[int]=True ,A : Union[str, Any]=10_24 ,A : Any=32 ,A : Optional[int]="relu6" ,A : int=0.1 ,A : Optional[Any]=0.02 ,A : Optional[Any]=True ,A : List[str]=True ,A : str=10 ,A : str=None ,):
__A = parent
__A = batch_size
__A = num_channels
__A = image_size
__A = depth_multiplier
__A = min_depth
__A = tf_padding
__A = int(last_hidden_size * depth_multiplier )
__A = output_stride
__A = hidden_act
__A = classifier_dropout_prob
__A = use_labels
__A = is_training
__A = num_labels
__A = initializer_range
__A = scope
def UpperCamelCase_ ( self : Optional[int] ):
__A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A = None
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size] ,self.num_labels )
__A = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
__A = self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCamelCase_ ( self : Any ):
return MobileNetVaConfig(
num_channels=self.num_channels ,image_size=self.image_size ,depth_multiplier=self.depth_multiplier ,min_depth=self.min_depth ,tf_padding=self.tf_padding ,hidden_act=self.hidden_act ,classifier_dropout_prob=self.classifier_dropout_prob ,initializer_range=self.initializer_range ,)
def UpperCamelCase_ ( self : Optional[int] ,A : str ,A : Tuple ,A : Optional[int] ,A : List[str] ):
__A = MobileNetVaModel(config=A )
model.to(A )
model.eval()
__A = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape ,(
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) ,)
def UpperCamelCase_ ( self : List[Any] ,A : Union[str, Any] ,A : List[Any] ,A : int ,A : Union[str, Any] ):
__A = self.num_labels
__A = MobileNetVaForImageClassification(A )
model.to(A )
model.eval()
__A = model(A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self : Tuple ):
__A = self.prepare_config_and_inputs()
__A , __A , __A , __A = config_and_inputs
__A = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
snake_case_ = (
{"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def UpperCamelCase_ ( self : Any ):
__A = MobileNetVaModelTester(self )
__A = MobileNetVaConfigTester(self ,config_class=A ,has_text_modality=A )
def UpperCamelCase_ ( self : str ):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV1 does not use inputs_embeds" )
def UpperCamelCase_ ( self : Union[str, Any] ):
pass
@unittest.skip(reason="MobileNetV1 does not support input and output embeddings" )
def UpperCamelCase_ ( self : Tuple ):
pass
@unittest.skip(reason="MobileNetV1 does not output attentions" )
def UpperCamelCase_ ( self : Any ):
pass
def UpperCamelCase_ ( self : Optional[int] ):
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = model_class(A )
__A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A = [*signature.parameters.keys()]
__A = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,A )
def UpperCamelCase_ ( self : List[Any] ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCamelCase_ ( self : Optional[int] ):
def check_hidden_states_output(A : List[Any] ,A : List[Any] ,A : Optional[int] ):
__A = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
__A = model(**self._prepare_for_class(A ,A ) )
__A = outputs.hidden_states
__A = 26
self.assertEqual(len(A ) ,A )
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = True
check_hidden_states_output(A ,A ,A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A = True
check_hidden_states_output(A ,A ,A )
def UpperCamelCase_ ( self : Tuple ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A = MobileNetVaModel.from_pretrained(A )
self.assertIsNotNone(A )
def UpperCAmelCase ( ) -> str:
"""simple docstring"""
__A = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self : List[str] ):
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224" ) if is_vision_available() else None
)
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
__A = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224" ).to(A )
__A = self.default_image_processor
__A = prepare_img()
__A = image_processor(images=A ,return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
__A = model(**A )
# verify the logits
__A = torch.Size((1, 10_01) )
self.assertEqual(outputs.logits.shape ,A )
__A = torch.tensor([-4.17_39, -1.12_33, 3.12_05] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,A ,atol=1E-4 ) )
| 55 |
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 __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
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 lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
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
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a (lowercase__ : int ) -> bool:
"""simple docstring"""
__snake_case = int(number**0.5 )
return number == sq * sq
def _a (lowercase__ : int , lowercase__ : int , lowercase__ : int , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> tuple[int, int]:
"""simple docstring"""
__snake_case = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
__snake_case = x_den * y_den * z_den
__snake_case = gcd(lowercase__ , lowercase__ )
top //= hcf
bottom //= hcf
return top, bottom
def _a (lowercase__ : int = 3_5 ) -> int:
"""simple docstring"""
__snake_case = set()
__snake_case = 42
__snake_case = Fraction(0 )
__snake_case = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
__snake_case = x_num * y_den + x_den * y_num
__snake_case = x_den * y_den
__snake_case = gcd(lowercase__ , lowercase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__snake_case = add_three(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
unique_s.add(lowercase__ )
# n=2
__snake_case = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
__snake_case = x_den * x_den * y_den * y_den
if is_sq(lowercase__ ) and is_sq(lowercase__ ):
__snake_case = int(sqrt(lowercase__ ) )
__snake_case = int(sqrt(lowercase__ ) )
__snake_case = gcd(lowercase__ , lowercase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__snake_case = add_three(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
unique_s.add(lowercase__ )
# n=-1
__snake_case = x_num * y_num
__snake_case = x_den * y_num + x_num * y_den
__snake_case = gcd(lowercase__ , lowercase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__snake_case = add_three(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
unique_s.add(lowercase__ )
# n=2
__snake_case = x_num * x_num * y_num * y_num
__snake_case = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(lowercase__ ) and is_sq(lowercase__ ):
__snake_case = int(sqrt(lowercase__ ) )
__snake_case = int(sqrt(lowercase__ ) )
__snake_case = gcd(lowercase__ , lowercase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__snake_case = add_three(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
unique_s.add(lowercase__ )
for num, den in unique_s:
total += Fraction(lowercase__ , lowercase__ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f'''{solution() = }''')
| 56 |
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_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 0 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class _lowerCAmelCase( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
a : List[Any] =AutoencoderKL
a : Union[str, Any] ='''sample'''
a : Tuple =1e-2
@property
def _a ( self ):
UpperCamelCase_: Union[str, Any] = 4
UpperCamelCase_: Any = 3
UpperCamelCase_: str = (3_2, 3_2)
UpperCamelCase_: Optional[Any] = floats_tensor((batch_size, num_channels) + sizes ).to(_lowerCamelCase )
return {"sample": image}
@property
def _a ( self ):
return (3, 3_2, 3_2)
@property
def _a ( self ):
return (3, 3_2, 3_2)
def _a ( self ):
UpperCamelCase_: List[Any] = {
'block_out_channels': [3_2, 6_4],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 4,
}
UpperCamelCase_: Optional[Any] = self.dummy_input
return init_dict, inputs_dict
def _a ( self ):
pass
def _a ( self ):
pass
@unittest.skipIf(torch_device == 'mps' , 'Gradient checkpointing skipped on MPS' )
def _a ( self ):
# enable deterministic behavior for gradient checkpointing
UpperCamelCase_ ,UpperCamelCase_: Any = self.prepare_init_args_and_inputs_for_common()
UpperCamelCase_: int = self.model_class(**_lowerCamelCase )
model.to(_lowerCamelCase )
assert not model.is_gradient_checkpointing and model.training
UpperCamelCase_: List[str] = model(**_lowerCamelCase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
UpperCamelCase_: Dict = torch.randn_like(_lowerCamelCase )
UpperCamelCase_: str = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
UpperCamelCase_: Any = self.model_class(**_lowerCamelCase )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(_lowerCamelCase )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
UpperCamelCase_: int = model_a(**_lowerCamelCase ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
UpperCamelCase_: Optional[int] = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
UpperCamelCase_: Optional[Any] = dict(model.named_parameters() )
UpperCamelCase_: List[str] = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def _a ( self ):
UpperCamelCase_ ,UpperCamelCase_: Tuple = AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy' , output_loading_info=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(_lowerCamelCase )
UpperCamelCase_: Any = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def _a ( self ):
UpperCamelCase_: str = AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy' )
UpperCamelCase_: Any = model.to(_lowerCamelCase )
model.eval()
if torch_device == "mps":
UpperCamelCase_: Optional[Any] = torch.manual_seed(0 )
else:
UpperCamelCase_: List[Any] = torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
UpperCamelCase_: Optional[Any] = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
UpperCamelCase_: Dict = image.to(_lowerCamelCase )
with torch.no_grad():
UpperCamelCase_: int = model(_lowerCamelCase , sample_posterior=_lowerCamelCase , generator=_lowerCamelCase ).sample
UpperCamelCase_: Union[str, Any] = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
UpperCamelCase_: List[str] = torch.tensor(
[
-4.0078e-01,
-3.8323e-04,
-1.2681e-01,
-1.1462e-01,
2.0095e-01,
1.0893e-01,
-8.8247e-02,
-3.0361e-01,
-9.8644e-03,
] )
elif torch_device == "cpu":
UpperCamelCase_: List[str] = torch.tensor(
[-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6] )
else:
UpperCamelCase_: int = torch.tensor(
[-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5] )
self.assertTrue(torch_all_close(_lowerCamelCase , _lowerCamelCase , rtol=1e-2 ) )
@slow
class _lowerCAmelCase( unittest.TestCase ):
"""simple docstring"""
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_lowerCamelCase ) for s in shape] )}.npy'''
def _a ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self , _lowerCamelCase=0 , _lowerCamelCase=(4, 3, 5_1_2, 5_1_2) , _lowerCamelCase=False ):
UpperCamelCase_: Optional[Any] = torch.floataa if fpaa else torch.floataa
UpperCamelCase_: Any = torch.from_numpy(load_hf_numpy(self.get_file_format(_lowerCamelCase , _lowerCamelCase ) ) ).to(_lowerCamelCase ).to(_lowerCamelCase )
return image
def _a ( self , _lowerCamelCase="CompVis/stable-diffusion-v1-4" , _lowerCamelCase=False ):
UpperCamelCase_: List[str] = 'fp16' if fpaa else None
UpperCamelCase_: int = torch.floataa if fpaa else torch.floataa
UpperCamelCase_: Optional[Any] = AutoencoderKL.from_pretrained(
_lowerCamelCase , subfolder='vae' , torch_dtype=_lowerCamelCase , revision=_lowerCamelCase , )
model.to(_lowerCamelCase ).eval()
return model
def _a ( self , _lowerCamelCase=0 ):
if torch_device == "mps":
return torch.manual_seed(_lowerCamelCase )
return torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
[4_7, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
# fmt: on
] )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Any = self.get_sd_vae_model()
UpperCamelCase_: str = self.get_sd_image(_lowerCamelCase )
UpperCamelCase_: int = self.get_generator(_lowerCamelCase )
with torch.no_grad():
UpperCamelCase_: List[str] = model(_lowerCamelCase , generator=_lowerCamelCase , sample_posterior=_lowerCamelCase ).sample
assert sample.shape == image.shape
UpperCamelCase_: Tuple = sample[-1, -2:, -2:, :2].flatten().float().cpu()
UpperCamelCase_: List[Any] = torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice )
assert torch_all_close(_lowerCamelCase , _lowerCamelCase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
[4_7, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
# fmt: on
] )
@require_torch_gpu
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Optional[int] = self.get_sd_vae_model(fpaa=_lowerCamelCase )
UpperCamelCase_: str = self.get_sd_image(_lowerCamelCase , fpaa=_lowerCamelCase )
UpperCamelCase_: Union[str, Any] = self.get_generator(_lowerCamelCase )
with torch.no_grad():
UpperCamelCase_: Union[str, Any] = model(_lowerCamelCase , generator=_lowerCamelCase , sample_posterior=_lowerCamelCase ).sample
assert sample.shape == image.shape
UpperCamelCase_: str = sample[-1, -2:, :2, -2:].flatten().float().cpu()
UpperCamelCase_: Any = torch.tensor(_lowerCamelCase )
assert torch_all_close(_lowerCamelCase , _lowerCamelCase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
[4_7, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
# fmt: on
] )
def _a ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Dict = self.get_sd_vae_model()
UpperCamelCase_: Union[str, Any] = self.get_sd_image(_lowerCamelCase )
with torch.no_grad():
UpperCamelCase_: Optional[Any] = model(_lowerCamelCase ).sample
assert sample.shape == image.shape
UpperCamelCase_: List[str] = sample[-1, -2:, -2:, :2].flatten().float().cpu()
UpperCamelCase_: Any = torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice )
assert torch_all_close(_lowerCamelCase , _lowerCamelCase , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[1_3, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
[3_7, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
# fmt: on
] )
@require_torch_gpu
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: List[str] = self.get_sd_vae_model()
UpperCamelCase_: Dict = self.get_sd_image(_lowerCamelCase , shape=(3, 4, 6_4, 6_4) )
with torch.no_grad():
UpperCamelCase_: List[str] = model.decode(_lowerCamelCase ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
UpperCamelCase_: Any = sample[-1, -2:, :2, -2:].flatten().cpu()
UpperCamelCase_: Optional[Any] = torch.tensor(_lowerCamelCase )
assert torch_all_close(_lowerCamelCase , _lowerCamelCase , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[2_7, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
[1_6, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
# fmt: on
] )
@require_torch_gpu
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: List[str] = self.get_sd_vae_model(fpaa=_lowerCamelCase )
UpperCamelCase_: int = self.get_sd_image(_lowerCamelCase , shape=(3, 4, 6_4, 6_4) , fpaa=_lowerCamelCase )
with torch.no_grad():
UpperCamelCase_: Tuple = model.decode(_lowerCamelCase ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
UpperCamelCase_: Any = sample[-1, -2:, :2, -2:].flatten().float().cpu()
UpperCamelCase_: Tuple = torch.tensor(_lowerCamelCase )
assert torch_all_close(_lowerCamelCase , _lowerCamelCase , atol=5e-3 )
@parameterized.expand([(1_3,), (1_6,), (2_7,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='xformers is not required when using PyTorch 2.0.' )
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: List[str] = self.get_sd_vae_model(fpaa=_lowerCamelCase )
UpperCamelCase_: Optional[int] = self.get_sd_image(_lowerCamelCase , shape=(3, 4, 6_4, 6_4) , fpaa=_lowerCamelCase )
with torch.no_grad():
UpperCamelCase_: Optional[int] = model.decode(_lowerCamelCase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
UpperCamelCase_: Tuple = model.decode(_lowerCamelCase ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
assert torch_all_close(_lowerCamelCase , _lowerCamelCase , atol=1e-1 )
@parameterized.expand([(1_3,), (1_6,), (3_7,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='xformers is not required when using PyTorch 2.0.' )
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: List[str] = self.get_sd_vae_model()
UpperCamelCase_: Dict = self.get_sd_image(_lowerCamelCase , shape=(3, 4, 6_4, 6_4) )
with torch.no_grad():
UpperCamelCase_: Optional[int] = model.decode(_lowerCamelCase ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
UpperCamelCase_: int = model.decode(_lowerCamelCase ).sample
assert list(sample.shape ) == [3, 3, 5_1_2, 5_1_2]
assert torch_all_close(_lowerCamelCase , _lowerCamelCase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[3_3, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
[4_7, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
# fmt: on
] )
def _a ( self , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: List[str] = self.get_sd_vae_model()
UpperCamelCase_: int = self.get_sd_image(_lowerCamelCase )
UpperCamelCase_: Dict = self.get_generator(_lowerCamelCase )
with torch.no_grad():
UpperCamelCase_: str = model.encode(_lowerCamelCase ).latent_dist
UpperCamelCase_: Union[str, Any] = dist.sample(generator=_lowerCamelCase )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
UpperCamelCase_: Any = sample[0, -1, -3:, -3:].flatten().cpu()
UpperCamelCase_: Optional[int] = torch.tensor(_lowerCamelCase )
UpperCamelCase_: int = 3e-3 if torch_device != 'mps' else 1e-2
assert torch_all_close(_lowerCamelCase , _lowerCamelCase , atol=_lowerCamelCase ) | 57 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 0 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def __lowerCAmelCase ( __UpperCamelCase : dict , __UpperCamelCase : str , __UpperCamelCase : set , __UpperCamelCase : set , __UpperCamelCase : dict , __UpperCamelCase : dict , __UpperCamelCase : PriorityQueue , __UpperCamelCase : dict , __UpperCamelCase : float | int , ):
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
snake_case_ : List[Any] = cst_fwd.get(__UpperCamelCase , np.inf )
snake_case_ : Optional[int] = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
snake_case_ : List[str] = new_cost_f
snake_case_ : int = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
snake_case_ : Optional[int] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def __lowerCAmelCase ( __UpperCamelCase : str , __UpperCamelCase : str , __UpperCamelCase : dict , __UpperCamelCase : dict ):
'''simple docstring'''
snake_case_ : List[Any] = -1
snake_case_ : List[Any] = set()
snake_case_ : Union[str, Any] = set()
snake_case_ : List[str] = {source: 0}
snake_case_ : Optional[int] = {destination: 0}
snake_case_ : List[Any] = {source: None}
snake_case_ : str = {destination: None}
snake_case_ : PriorityQueue[Any] = PriorityQueue()
snake_case_ : PriorityQueue[Any] = PriorityQueue()
snake_case_ : List[str] = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
snake_case_ , snake_case_ : Dict = queue_forward.get()
visited_forward.add(__UpperCamelCase )
snake_case_ , snake_case_ : Dict = queue_backward.get()
visited_backward.add(__UpperCamelCase )
snake_case_ : Optional[Any] = pass_and_relaxation(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
snake_case_ : int = pass_and_relaxation(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
snake_case_ : List[str] = shortest_distance
return shortest_path_distance
__lowerCAmelCase : Union[str, Any] = {
'''B''': [['''C''', 1]],
'''C''': [['''D''', 1]],
'''D''': [['''F''', 1]],
'''E''': [['''B''', 1], ['''G''', 2]],
'''F''': [],
'''G''': [['''F''', 1]],
}
__lowerCAmelCase : Tuple = {
'''B''': [['''E''', 1]],
'''C''': [['''B''', 1]],
'''D''': [['''C''', 1]],
'''F''': [['''D''', 1], ['''G''', 1]],
'''E''': [[None, np.inf]],
'''G''': [['''E''', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__ = "▁" , __magic_name__ = True , __magic_name__ = "<unk>" , __magic_name__ = "</s>" , __magic_name__ = "<pad>" , ) -> Dict:
'''simple docstring'''
snake_case_ : List[Any] = {
'''pad''': {'''id''': 0, '''token''': pad_token},
'''eos''': {'''id''': 1, '''token''': eos_token},
'''unk''': {'''id''': 2, '''token''': unk_token},
}
snake_case_ : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ : int = token_dict['''token''']
snake_case_ : Optional[int] = Tokenizer(Unigram() )
snake_case_ : int = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ),
normalizers.Lowercase(),
] )
snake_case_ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ ),
pre_tokenizers.Digits(individual_digits=__magic_name__ ),
pre_tokenizers.Punctuation(),
] )
snake_case_ : Tuple = decoders.Metaspace(replacement=__magic_name__ , add_prefix_space=__magic_name__ )
snake_case_ : Optional[Any] = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , )
snake_case_ : Optional[Any] = {
'''model''': '''SentencePieceUnigram''',
'''replacement''': replacement,
'''add_prefix_space''': add_prefix_space,
}
super().__init__(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
if isinstance(__magic_name__ , __magic_name__ ):
snake_case_ : Dict = [files]
self._tokenizer.train(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self , __magic_name__ , __magic_name__ = 8000 , __magic_name__ = True , ) -> int:
'''simple docstring'''
snake_case_ : Any = trainers.UnigramTrainer(
vocab_size=__magic_name__ , special_tokens=self.special_tokens_list , show_progress=__magic_name__ , )
self._tokenizer.train_from_iterator(__magic_name__ , trainer=__magic_name__ )
self.add_unk_id()
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = json.loads(self._tokenizer.to_str() )
snake_case_ : Union[str, Any] = self.special_tokens['''unk''']['''id''']
snake_case_ : Tuple = Tokenizer.from_str(json.dumps(__magic_name__ ) )
| 60 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def lowerCAmelCase_ ( __a , __a , __a , __a ) -> Dict:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =original_name.split("." )[0]
lowerCamelCase__: Any =key.split("." )
lowerCamelCase__: Optional[Any] =int(key_list[key_list.index(__a ) - 2] )
lowerCamelCase__: List[str] =int(key_list[key_list.index(__a ) - 1] )
lowerCamelCase__: Union[str, Any] =orig_block_num - offset
lowerCamelCase__: List[str] =key.replace(F"""{orig_block_num}.{layer_num}.{original_name}""" , F"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def lowerCAmelCase_ ( __a ) -> List[str]:
"""simple docstring"""
lowerCamelCase__: Union[str, Any] =OrderedDict()
lowerCamelCase__ , lowerCamelCase__: int =0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
lowerCamelCase__: Union[str, Any] =key.replace("network" , "poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
lowerCamelCase__: List[Any] =key[: key.find("proj" )]
lowerCamelCase__: Optional[Any] =key.replace(__a , F"""patch_embeddings.{total_embed_found}.""" )
lowerCamelCase__: List[str] =key.replace("proj" , "projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
lowerCamelCase__: Tuple ="poolformer.encoder." + key
if "mlp.fc1" in key:
lowerCamelCase__: Union[str, Any] =replace_key_with_offset(__a , __a , "mlp.fc1" , "output.conv1" )
if "mlp.fc2" in key:
lowerCamelCase__: Optional[int] =replace_key_with_offset(__a , __a , "mlp.fc2" , "output.conv2" )
if "norm1" in key:
lowerCamelCase__: Union[str, Any] =replace_key_with_offset(__a , __a , "norm1" , "before_norm" )
if "norm2" in key:
lowerCamelCase__: List[str] =replace_key_with_offset(__a , __a , "norm2" , "after_norm" )
if "layer_scale_1" in key:
lowerCamelCase__: str =replace_key_with_offset(__a , __a , "layer_scale_1" , "layer_scale_1" )
if "layer_scale_2" in key:
lowerCamelCase__: Any =replace_key_with_offset(__a , __a , "layer_scale_2" , "layer_scale_2" )
if "head" in key:
lowerCamelCase__: int =key.replace("head" , "classifier" )
lowerCamelCase__: List[str] =value
return new_state_dict
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
lowerCamelCase__: Optional[int] ="http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase__: Optional[int] =Image.open(requests.get(__a , stream=__a ).raw )
return image
@torch.no_grad()
def lowerCAmelCase_ ( __a , __a , __a ) -> Any:
"""simple docstring"""
lowerCamelCase__: Any =PoolFormerConfig()
# set attributes based on model_name
lowerCamelCase__: int ="huggingface/label-files"
lowerCamelCase__: Any =model_name[-3:]
lowerCamelCase__: int =1000
lowerCamelCase__: List[Any] ="imagenet-1k-id2label.json"
lowerCamelCase__: Any =(1, 1000)
# set config attributes
lowerCamelCase__: Optional[Any] =json.load(open(hf_hub_download(__a , __a , repo_type="dataset" ) , "r" ) )
lowerCamelCase__: Dict ={int(__a ): v for k, v in idalabel.items()}
lowerCamelCase__: Optional[int] =idalabel
lowerCamelCase__: int ={v: k for k, v in idalabel.items()}
if size == "s12":
lowerCamelCase__: Optional[int] =[2, 2, 6, 2]
lowerCamelCase__: List[Any] =[64, 128, 320, 512]
lowerCamelCase__: Optional[Any] =4.0
lowerCamelCase__: int =0.9
elif size == "s24":
lowerCamelCase__: List[str] =[4, 4, 12, 4]
lowerCamelCase__: str =[64, 128, 320, 512]
lowerCamelCase__: Any =4.0
lowerCamelCase__: str =0.9
elif size == "s36":
lowerCamelCase__: Any =[6, 6, 18, 6]
lowerCamelCase__: Optional[int] =[64, 128, 320, 512]
lowerCamelCase__: int =4.0
lowerCamelCase__: Dict =1e-6
lowerCamelCase__: Any =0.9
elif size == "m36":
lowerCamelCase__: Union[str, Any] =[6, 6, 18, 6]
lowerCamelCase__: Optional[Any] =[96, 192, 384, 768]
lowerCamelCase__: Tuple =4.0
lowerCamelCase__: Union[str, Any] =1e-6
lowerCamelCase__: Optional[int] =0.9_5
elif size == "m48":
lowerCamelCase__: Optional[Any] =[8, 8, 24, 8]
lowerCamelCase__: str =[96, 192, 384, 768]
lowerCamelCase__: Optional[int] =4.0
lowerCamelCase__: Dict =1e-6
lowerCamelCase__: Any =0.9_5
else:
raise ValueError(F"""Size {size} not supported""" )
# load image processor
lowerCamelCase__: str =PoolFormerImageProcessor(crop_pct=__a )
# Prepare image
lowerCamelCase__: Optional[int] =prepare_img()
lowerCamelCase__: Optional[int] =image_processor(images=__a , return_tensors="pt" ).pixel_values
logger.info(F"""Converting model {model_name}...""" )
# load original state dict
lowerCamelCase__: List[str] =torch.load(__a , map_location=torch.device("cpu" ) )
# rename keys
lowerCamelCase__: List[Any] =rename_keys(__a )
# create HuggingFace model and load state dict
lowerCamelCase__: List[str] =PoolFormerForImageClassification(__a )
model.load_state_dict(__a )
model.eval()
# Define image processor
lowerCamelCase__: Optional[int] =PoolFormerImageProcessor(crop_pct=__a )
lowerCamelCase__: Optional[int] =image_processor(images=prepare_img() , return_tensors="pt" ).pixel_values
# forward pass
lowerCamelCase__: List[Any] =model(__a )
lowerCamelCase__: Any =outputs.logits
# define expected logit slices for different models
if size == "s12":
lowerCamelCase__: Optional[int] =torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9] )
elif size == "s24":
lowerCamelCase__: Union[str, Any] =torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5] )
elif size == "s36":
lowerCamelCase__: Dict =torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8] )
elif size == "m36":
lowerCamelCase__: Tuple =torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8] )
elif size == "m48":
lowerCamelCase__: Dict =torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3] )
else:
raise ValueError(F"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , __a , atol=1e-2 )
# finally, save model and image processor
logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(__a ).mkdir(exist_ok=__a )
model.save_pretrained(__a )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__a )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="poolformer_s12",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
__A = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 59 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def lowerCamelCase_ ( _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : List[Any] = [False] * len(_UpperCamelCase )
snake_case_ : int = [-1] * len(_UpperCamelCase )
def dfs(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Dict = True
snake_case_ : Dict = c
for u in graph[v]:
if not visited[u]:
dfs(_UpperCamelCase , 1 - c )
for i in range(len(_UpperCamelCase ) ):
if not visited[i]:
dfs(_UpperCamelCase , 0 )
for i in range(len(_UpperCamelCase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
lowerCAmelCase_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 60 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = OrderedDict(
[
('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
('beit', 'BeitFeatureExtractor'),
('chinese_clip', 'ChineseCLIPFeatureExtractor'),
('clap', 'ClapFeatureExtractor'),
('clip', 'CLIPFeatureExtractor'),
('clipseg', 'ViTFeatureExtractor'),
('conditional_detr', 'ConditionalDetrFeatureExtractor'),
('convnext', 'ConvNextFeatureExtractor'),
('cvt', 'ConvNextFeatureExtractor'),
('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
('data2vec-vision', 'BeitFeatureExtractor'),
('deformable_detr', 'DeformableDetrFeatureExtractor'),
('deit', 'DeiTFeatureExtractor'),
('detr', 'DetrFeatureExtractor'),
('dinat', 'ViTFeatureExtractor'),
('donut-swin', 'DonutFeatureExtractor'),
('dpt', 'DPTFeatureExtractor'),
('encodec', 'EncodecFeatureExtractor'),
('flava', 'FlavaFeatureExtractor'),
('glpn', 'GLPNFeatureExtractor'),
('groupvit', 'CLIPFeatureExtractor'),
('hubert', 'Wav2Vec2FeatureExtractor'),
('imagegpt', 'ImageGPTFeatureExtractor'),
('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
('levit', 'LevitFeatureExtractor'),
('maskformer', 'MaskFormerFeatureExtractor'),
('mctct', 'MCTCTFeatureExtractor'),
('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
('mobilevit', 'MobileViTFeatureExtractor'),
('nat', 'ViTFeatureExtractor'),
('owlvit', 'OwlViTFeatureExtractor'),
('perceiver', 'PerceiverFeatureExtractor'),
('poolformer', 'PoolFormerFeatureExtractor'),
('regnet', 'ConvNextFeatureExtractor'),
('resnet', 'ConvNextFeatureExtractor'),
('segformer', 'SegformerFeatureExtractor'),
('sew', 'Wav2Vec2FeatureExtractor'),
('sew-d', 'Wav2Vec2FeatureExtractor'),
('speech_to_text', 'Speech2TextFeatureExtractor'),
('speecht5', 'SpeechT5FeatureExtractor'),
('swiftformer', 'ViTFeatureExtractor'),
('swin', 'ViTFeatureExtractor'),
('swinv2', 'ViTFeatureExtractor'),
('table-transformer', 'DetrFeatureExtractor'),
('timesformer', 'VideoMAEFeatureExtractor'),
('tvlt', 'TvltFeatureExtractor'),
('unispeech', 'Wav2Vec2FeatureExtractor'),
('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
('van', 'ConvNextFeatureExtractor'),
('videomae', 'VideoMAEFeatureExtractor'),
('vilt', 'ViltFeatureExtractor'),
('vit', 'ViTFeatureExtractor'),
('vit_mae', 'ViTFeatureExtractor'),
('vit_msn', 'ViTFeatureExtractor'),
('wav2vec2', 'Wav2Vec2FeatureExtractor'),
('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
('wavlm', 'Wav2Vec2FeatureExtractor'),
('whisper', 'WhisperFeatureExtractor'),
('xclip', 'CLIPFeatureExtractor'),
('yolos', 'YolosFeatureExtractor'),
]
)
UpperCamelCase = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCAmelCase__ = model_type_to_module_name(lowerCAmelCase_ )
lowerCAmelCase__ = importlib.import_module(F'.{module_name}' , "transformers.models" )
try:
return getattr(lowerCAmelCase_ , lowerCAmelCase_ )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(lowerCAmelCase_ , "__name__" , lowerCAmelCase_ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCAmelCase__ = importlib.import_module("transformers" )
if hasattr(lowerCAmelCase_ , lowerCAmelCase_ ):
return getattr(lowerCAmelCase_ , lowerCAmelCase_ )
return None
def _A ( lowerCAmelCase_ : Union[str, os.PathLike] , lowerCAmelCase_ : Optional[Union[str, os.PathLike]] = None , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Optional[Dict[str, str]] = None , lowerCAmelCase_ : Optional[Union[bool, str]] = None , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : bool = False , **lowerCAmelCase_ : Any , ):
"""simple docstring"""
lowerCAmelCase__ = get_file_from_repo(
lowerCAmelCase_ , lowerCAmelCase_ , cache_dir=lowerCAmelCase_ , force_download=lowerCAmelCase_ , resume_download=lowerCAmelCase_ , proxies=lowerCAmelCase_ , use_auth_token=lowerCAmelCase_ , revision=lowerCAmelCase_ , local_files_only=lowerCAmelCase_ , )
if resolved_config_file is None:
logger.info(
"Could not locate the feature extractor configuration file, will try to use the model config instead." )
return {}
with open(lowerCAmelCase_ , encoding="utf-8" ) as reader:
return json.load(lowerCAmelCase_ )
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : int ) -> int:
raise EnvironmentError(
"AutoFeatureExtractor is designed to be instantiated "
"using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method." )
@classmethod
@replace_list_option_in_docstrings(SCREAMING_SNAKE_CASE__ )
def a ( cls : str , SCREAMING_SNAKE_CASE__ : List[str] , **SCREAMING_SNAKE_CASE__ : List[str] ) -> Any:
lowerCAmelCase__ = kwargs.pop("config" , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = kwargs.pop("trust_remote_code" , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = True
lowerCAmelCase__ , lowerCAmelCase__ = FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = config_dict.get("feature_extractor_type" , SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = None
if "AutoFeatureExtractor" in config_dict.get("auto_map" , {} ):
lowerCAmelCase__ = config_dict["auto_map"]["AutoFeatureExtractor"]
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowerCAmelCase__ = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
# It could be in `config.feature_extractor_type``
lowerCAmelCase__ = getattr(SCREAMING_SNAKE_CASE__ , "feature_extractor_type" , SCREAMING_SNAKE_CASE__ )
if hasattr(SCREAMING_SNAKE_CASE__ , "auto_map" ) and "AutoFeatureExtractor" in config.auto_map:
lowerCAmelCase__ = config.auto_map["AutoFeatureExtractor"]
if feature_extractor_class is not None:
lowerCAmelCase__ = feature_extractor_class_from_name(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = feature_extractor_auto_map is not None
lowerCAmelCase__ = feature_extractor_class is not None or type(SCREAMING_SNAKE_CASE__ ) in FEATURE_EXTRACTOR_MAPPING
lowerCAmelCase__ = resolve_trust_remote_code(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if has_remote_code and trust_remote_code:
lowerCAmelCase__ = get_class_from_dynamic_module(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = kwargs.pop("code_revision" , SCREAMING_SNAKE_CASE__ )
if os.path.isdir(SCREAMING_SNAKE_CASE__ ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(SCREAMING_SNAKE_CASE__ ) in FEATURE_EXTRACTOR_MAPPING:
lowerCAmelCase__ = FEATURE_EXTRACTOR_MAPPING[type(SCREAMING_SNAKE_CASE__ )]
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
raise ValueError(
f'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
f'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}' )
@staticmethod
def a ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str] ) -> Union[str, Any]:
FEATURE_EXTRACTOR_MAPPING.register(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
| 61 |
import unittest
import numpy as np
from datasets import load_dataset
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 BeitImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , __magic_name__ , __magic_name__=7 , __magic_name__=3 , __magic_name__=18 , __magic_name__=30 , __magic_name__=400 , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=None , __magic_name__=True , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=[0.5, 0.5, 0.5] , __magic_name__=False , ) -> int:
'''simple docstring'''
snake_case_ : int = size if size is not None else {'''height''': 20, '''width''': 20}
snake_case_ : int = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
snake_case_ : str = parent
snake_case_ : Optional[int] = batch_size
snake_case_ : Dict = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Union[str, Any] = min_resolution
snake_case_ : Tuple = max_resolution
snake_case_ : str = do_resize
snake_case_ : Tuple = size
snake_case_ : int = do_center_crop
snake_case_ : Tuple = crop_size
snake_case_ : int = do_normalize
snake_case_ : Optional[Any] = image_mean
snake_case_ : List[str] = image_std
snake_case_ : str = do_reduce_labels
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : Any = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Union[str, Any] = Image.open(dataset[0]['''file'''] )
snake_case_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
snake_case_ : str = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
snake_case_ : Optional[Any] = Image.open(ds[0]['''file'''] )
snake_case_ : Optional[Any] = Image.open(ds[1]['''file'''] )
snake_case_ : List[str] = Image.open(ds[2]['''file'''] )
snake_case_ : str = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class __lowerCAmelCase ( _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : int = BeitImageProcessingTester(self )
@property
def lowerCamelCase (self ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__magic_name__ , '''do_resize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''size''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''center_crop''' ) )
self.assertTrue(hasattr(__magic_name__ , '''do_normalize''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_mean''' ) )
self.assertTrue(hasattr(__magic_name__ , '''image_std''' ) )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
snake_case_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=__magic_name__ )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , __magic_name__ )
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , Image.Image )
# Test not batched input
snake_case_ : Optional[int] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , np.ndarray )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : Optional[int] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
# Test not batched input
snake_case_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
snake_case_ : List[str] = image_processing(__magic_name__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ )
snake_case_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(__magic_name__ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
snake_case_ : List[str] = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched
snake_case_ : Any = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test not batched input (PIL images)
snake_case_ , snake_case_ : Optional[int] = prepare_semantic_single_inputs()
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
# Test batched input (PIL images)
snake_case_ , snake_case_ : Dict = prepare_semantic_batch_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
snake_case_ , snake_case_ : Tuple = prepare_semantic_single_inputs()
snake_case_ : Optional[int] = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 150 )
snake_case_ : List[Any] = True
snake_case_ : int = image_processing(__magic_name__ , __magic_name__ , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 255 )
| 60 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""huggingface/informer-tourism-monthly""": (
"""https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Tuple = '''informer'''
UpperCamelCase_ : Tuple = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : Any , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : str = "student_t" , UpperCAmelCase_ : str = "nll" , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : List[int] = None , UpperCAmelCase_ : Optional[Union[str, bool]] = "mean" , UpperCAmelCase_ : int = 0 , UpperCAmelCase_ : int = 0 , UpperCAmelCase_ : int = 0 , UpperCAmelCase_ : int = 0 , UpperCAmelCase_ : Optional[List[int]] = None , UpperCAmelCase_ : Optional[List[int]] = None , UpperCAmelCase_ : int = 64 , UpperCAmelCase_ : int = 32 , UpperCAmelCase_ : int = 32 , UpperCAmelCase_ : int = 2 , UpperCAmelCase_ : int = 2 , UpperCAmelCase_ : int = 2 , UpperCAmelCase_ : int = 2 , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : str = "gelu" , UpperCAmelCase_ : float = 0.05 , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : int = 100 , UpperCAmelCase_ : float = 0.02 , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : str = "prob" , UpperCAmelCase_ : int = 5 , UpperCAmelCase_ : bool = True , **UpperCAmelCase_ : Tuple , ):
# time series specific configuration
SCREAMING_SNAKE_CASE : str = prediction_length
SCREAMING_SNAKE_CASE : List[str] = context_length or prediction_length
SCREAMING_SNAKE_CASE : Optional[Any] = distribution_output
SCREAMING_SNAKE_CASE : Tuple = loss
SCREAMING_SNAKE_CASE : List[Any] = input_size
SCREAMING_SNAKE_CASE : Any = num_time_features
SCREAMING_SNAKE_CASE : List[str] = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
SCREAMING_SNAKE_CASE : List[Any] = scaling
SCREAMING_SNAKE_CASE : List[Any] = num_dynamic_real_features
SCREAMING_SNAKE_CASE : Dict = num_static_real_features
SCREAMING_SNAKE_CASE : Dict = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(UpperCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
SCREAMING_SNAKE_CASE : Any = cardinality
else:
SCREAMING_SNAKE_CASE : Optional[int] = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(UpperCAmelCase_ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
SCREAMING_SNAKE_CASE : Tuple = embedding_dimension
else:
SCREAMING_SNAKE_CASE : List[str] = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
SCREAMING_SNAKE_CASE : Optional[Any] = num_parallel_samples
# Transformer architecture configuration
SCREAMING_SNAKE_CASE : List[Any] = input_size * len(self.lags_sequence ) + self._number_of_features
SCREAMING_SNAKE_CASE : Dict = d_model
SCREAMING_SNAKE_CASE : List[str] = encoder_attention_heads
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_attention_heads
SCREAMING_SNAKE_CASE : str = encoder_ffn_dim
SCREAMING_SNAKE_CASE : Dict = decoder_ffn_dim
SCREAMING_SNAKE_CASE : List[Any] = encoder_layers
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_layers
SCREAMING_SNAKE_CASE : List[str] = dropout
SCREAMING_SNAKE_CASE : Optional[int] = attention_dropout
SCREAMING_SNAKE_CASE : Dict = activation_dropout
SCREAMING_SNAKE_CASE : List[str] = encoder_layerdrop
SCREAMING_SNAKE_CASE : Tuple = decoder_layerdrop
SCREAMING_SNAKE_CASE : str = activation_function
SCREAMING_SNAKE_CASE : Union[str, Any] = init_std
SCREAMING_SNAKE_CASE : Any = use_cache
# Informer
SCREAMING_SNAKE_CASE : Dict = attention_type
SCREAMING_SNAKE_CASE : Dict = sampling_factor
SCREAMING_SNAKE_CASE : Any = distil
super().__init__(is_encoder_decoder=UpperCAmelCase_ , **UpperCAmelCase_ )
@property
def _A ( self : Any ):
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 62 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@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}
}
'''
lowerCAmelCase_ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCAmelCase_ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
] , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('''float''' ) ),
"references": datasets.Sequence(datasets.Value('''float''' ) ),
}
else:
return {
"predictions": datasets.Value('''float''' ),
"references": datasets.Value('''float''' ),
}
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__="uniform_average" , __magic_name__=True ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = mean_squared_error(
__magic_name__ , __magic_name__ , sample_weight=__magic_name__ , multioutput=__magic_name__ , squared=__magic_name__ )
return {"mse": mse}
| 60 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
a : Dict = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
a : int = {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}
a : List[Any] = "zero2"
a : Union[str, Any] = "zero3"
a : Optional[Any] = [ZEROa, ZEROa]
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : int , __lowerCamelCase : int ):
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
__UpperCAmelCase : str = parameterized.to_safe_name("""_""".join(str(__lowerCamelCase ) for x in param.args ) )
return f"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
a : Tuple = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class a ( lowercase__ ):
"""simple docstring"""
@parameterized.expand(__lowercase , name_func=__lowercase )
def UpperCAmelCase ( self : Optional[int] , __lowercase : Any , __lowercase : str ) -> Optional[int]:
self.run_and_check(
stage=__lowercase , model=__lowercase , distributed=__lowercase , fpaa=__lowercase , )
@require_torch_multi_gpu
@parameterized.expand(__lowercase , name_func=__lowercase )
def UpperCAmelCase ( self : List[Any] , __lowercase : Any , __lowercase : Tuple ) -> Optional[int]:
self.run_and_check(
stage=__lowercase , model=__lowercase , distributed=__lowercase , fpaa=__lowercase , )
@parameterized.expand(__lowercase , name_func=__lowercase )
def UpperCAmelCase ( self : Union[str, Any] , __lowercase : str , __lowercase : Union[str, Any] ) -> Optional[Any]:
self.run_and_check(
stage=__lowercase , model=__lowercase , distributed=__lowercase , fpaa=__lowercase , )
@require_torch_multi_gpu
@parameterized.expand(__lowercase , name_func=__lowercase )
def UpperCAmelCase ( self : Optional[Any] , __lowercase : List[Any] , __lowercase : Union[str, Any] ) -> Dict:
self.run_and_check(
stage=__lowercase , model=__lowercase , distributed=__lowercase , fpaa=__lowercase , )
def UpperCAmelCase ( self : List[str] , __lowercase : Optional[Any] ) -> Any:
# XXX: run_asr is premature and doesn't save any results
# so all we check for now is that the process didn't fail
pass
def UpperCAmelCase ( self : Any , __lowercase : str , __lowercase : str , __lowercase : int = 10 , __lowercase : bool = True , __lowercase : bool = True , __lowercase : bool = True , ) -> str:
__UpperCAmelCase : Optional[Any] = models[model]
__UpperCAmelCase : int = self.run_trainer(
stage=__lowercase , model_name=__lowercase , eval_steps=__lowercase , num_train_epochs=1 , distributed=__lowercase , fpaa=__lowercase , )
self.do_checks(__lowercase )
return output_dir
def UpperCAmelCase ( self : List[str] , __lowercase : str , __lowercase : str , __lowercase : int = 10 , __lowercase : int = 1 , __lowercase : bool = True , __lowercase : bool = True , ) -> Dict:
__UpperCAmelCase : Optional[Any] = self.get_auto_remove_tmp_dir("""./xxx""" , after=__lowercase )
__UpperCAmelCase : List[str] = f"""
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(__lowercase )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
""".split()
if fpaa:
args.extend(["""--fp16"""] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
__UpperCAmelCase : Tuple = f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
__UpperCAmelCase : Union[str, Any] = [f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
__UpperCAmelCase : List[Any] = self.get_launcher(__lowercase )
__UpperCAmelCase : Tuple = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(__lowercase , env=self.get_env() )
return output_dir
def UpperCAmelCase ( self : Tuple , __lowercase : List[Any]=False ) -> Tuple:
# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
# - it won't be able to handle that
# 2. for now testing with just 2 gpus max (since some quality tests may give different
# results with mode gpus because we use very little data)
__UpperCAmelCase : Optional[int] = min(2 , get_gpu_count() ) if distributed else 1
return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()
| 63 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowerCamelCase :
__a = 42
__a = None
__a = None
def A__ ( snake_case_ : TreeNode | None ):
# Validation
def is_valid_tree(snake_case_ : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(snake_case_ , snake_case_ ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(snake_case_ ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
snake_case_ : TreeNode | None , snake_case_ : float , snake_case_ : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , snake_case_ , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , snake_case_ )
)
return is_binary_search_tree_recursive_check(snake_case_ , -float('''inf''' ) , float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 64 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
lowerCamelCase_ : str
lowerCamelCase_ : str = None
@staticmethod
def lowerCamelCase () -> Any:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
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 lowerCamelCase (cls ) -> List[Any]:
'''simple docstring'''
return F'''`pip install {cls.pip_package or cls.name}`'''
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[int] = '''optuna'''
@staticmethod
def lowerCamelCase () -> Union[str, Any]:
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return run_hp_search_optuna(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_optuna(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Any = '''ray'''
lowerCamelCase_ : List[str] = '''\'ray[tune]\''''
@staticmethod
def lowerCamelCase () -> List[Any]:
'''simple docstring'''
return is_ray_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_ray(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
return default_hp_space_ray(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''sigopt'''
@staticmethod
def lowerCamelCase () -> Optional[int]:
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> List[str]:
'''simple docstring'''
return run_hp_search_sigopt(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> int:
'''simple docstring'''
return default_hp_space_sigopt(__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''wandb'''
@staticmethod
def lowerCamelCase () -> Dict:
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return run_hp_search_wandb(__magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
def lowerCamelCase (self , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
return default_hp_space_wandb(__magic_name__ )
lowerCAmelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Optional[int] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_UpperCamelCase ) > 0:
snake_case_ : Dict = available_backends[0].name
if len(_UpperCamelCase ) > 1:
logger.info(
f'''{len(_UpperCamelCase )} 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() ) )
| 60 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowercase :
def __init__( self : List[str] ,A : List[Any] ,A : List[str]=13 ,A : Any=32 ,A : List[str]=3 ,A : Optional[int]=4 ,A : Optional[int]=[10, 20, 30, 40] ,A : str=[2, 2, 3, 2] ,A : Optional[Any]=True ,A : Dict=True ,A : Tuple=37 ,A : List[str]="gelu" ,A : Optional[int]=10 ,A : List[Any]=0.0_2 ,A : Optional[int]=["stage2", "stage3", "stage4"] ,A : List[Any]=[2, 3, 4] ,A : List[Any]=None ,):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = parent
UpperCAmelCase__ : str = batch_size
UpperCAmelCase__ : Union[str, Any] = image_size
UpperCAmelCase__ : Any = num_channels
UpperCAmelCase__ : Optional[int] = num_stages
UpperCAmelCase__ : str = hidden_sizes
UpperCAmelCase__ : List[Any] = depths
UpperCAmelCase__ : str = is_training
UpperCAmelCase__ : Dict = use_labels
UpperCAmelCase__ : List[str] = intermediate_size
UpperCAmelCase__ : List[Any] = hidden_act
UpperCAmelCase__ : Optional[Any] = num_labels
UpperCAmelCase__ : Union[str, Any] = initializer_range
UpperCAmelCase__ : List[Any] = out_features
UpperCAmelCase__ : Optional[Any] = out_indices
UpperCAmelCase__ : Any = scope
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Tuple = None
if self.use_labels:
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size] ,self.num_labels )
UpperCAmelCase__ : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def __lowercase ( self : int ):
'''simple docstring'''
return ConvNextVaConfig(
num_channels=self.num_channels ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,num_stages=self.num_stages ,hidden_act=self.hidden_act ,is_decoder=A ,initializer_range=self.initializer_range ,out_features=self.out_features ,out_indices=self.out_indices ,num_labels=self.num_labels ,)
def __lowercase ( self : str ,A : List[Any] ,A : Union[str, Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = ConvNextVaModel(config=A )
model.to(A )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def __lowercase ( self : Union[str, Any] ,A : Union[str, Any] ,A : Optional[Any] ,A : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = ConvNextVaForImageClassification(A )
model.to(A )
model.eval()
UpperCAmelCase__ : Optional[int] = model(A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def __lowercase ( self : int ,A : Optional[int] ,A : Optional[int] ,A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = ConvNextVaBackbone(config=A )
model.to(A )
model.eval()
UpperCAmelCase__ : Tuple = model(A )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) ,len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,len(config.out_features ) )
self.parent.assertListEqual(model.channels ,config.hidden_sizes[1:] )
# verify backbone works with out_features=None
UpperCAmelCase__ : List[Any] = None
UpperCAmelCase__ : str = ConvNextVaBackbone(config=A )
model.to(A )
model.eval()
UpperCAmelCase__ : str = model(A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,1 )
self.parent.assertListEqual(model.channels ,[config.hidden_sizes[-1]] )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = config_and_inputs
UpperCAmelCase__ : List[Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : int = config_and_inputs
UpperCAmelCase__ : Dict = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class __lowercase ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
snake_case_ = (
{"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = ConvNextVaModelTester(self )
UpperCAmelCase__ : Any = ConfigTester(self ,config_class=A ,has_text_modality=A ,hidden_size=37 )
def __lowercase ( self : List[str] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowercase ( self : List[str] ):
'''simple docstring'''
return
@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds""" )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings""" )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking""" )
def __lowercase ( self : str ):
'''simple docstring'''
pass
def __lowercase ( self : List[Any] ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_with_labels()
UpperCAmelCase__ : int = True
if model_class.__name__ in [
*get_values(A ),
*get_values(A ),
]:
continue
UpperCAmelCase__ : Tuple = model_class(A )
model.to(A )
model.train()
UpperCAmelCase__ : List[Any] = self._prepare_for_class(A ,A ,return_labels=A )
UpperCAmelCase__ : Optional[int] = model(**A ).loss
loss.backward()
def __lowercase ( self : Tuple ):
'''simple docstring'''
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_with_labels()
UpperCAmelCase__ : int = False
UpperCAmelCase__ : List[Any] = True
if (
model_class.__name__
in [*get_values(A ), *get_values(A )]
or not model_class.supports_gradient_checkpointing
):
continue
UpperCAmelCase__ : Dict = model_class(A )
model.to(A )
model.gradient_checkpointing_enable()
model.train()
UpperCAmelCase__ : Tuple = self._prepare_for_class(A ,A ,return_labels=A )
UpperCAmelCase__ : Optional[Any] = model(**A ).loss
loss.backward()
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(A )
UpperCAmelCase__ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Optional[Any] = [*signature.parameters.keys()]
UpperCAmelCase__ : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,A )
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def __lowercase ( self : Any ):
'''simple docstring'''
def check_hidden_states_output(A : Optional[Any] ,A : Union[str, Any] ,A : str ):
UpperCAmelCase__ : List[str] = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
UpperCAmelCase__ : int = model(**self._prepare_for_class(A ,A ) )
UpperCAmelCase__ : str = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase__ : List[str] = self.model_tester.num_stages
self.assertEqual(len(A ) ,expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)
UpperCAmelCase__ , UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Dict = True
check_hidden_states_output(A ,A ,A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : Tuple = True
check_hidden_states_output(A ,A ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Tuple = ConvNextVaModel.from_pretrained(A )
self.assertIsNotNone(A )
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __lowercase ( unittest.TestCase ):
@cached_property
def __lowercase ( self : int ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ) if is_vision_available() else None
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ).to(A )
UpperCAmelCase__ : Any = self.default_image_processor
UpperCAmelCase__ : str = prepare_img()
UpperCAmelCase__ : List[Any] = preprocessor(images=A ,return_tensors="""pt""" ).to(A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Optional[int] = model(**A )
# verify the logits
UpperCAmelCase__ : List[Any] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape ,A )
UpperCAmelCase__ : Optional[Any] = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,A ,atol=1e-4 ) )
| 65 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Union[str, Any] = [[0] * n for i in range(_UpperCamelCase )]
for i in range(_UpperCamelCase ):
snake_case_ : Any = y_points[i]
for i in range(2 , _UpperCamelCase ):
for j in range(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 0 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> List[Any]:
_lowercase : int = model.config
_lowercase : Tuple = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
_lowercase : Any = MBartConfig(
is_decoder=SCREAMING_SNAKE_CASE , is_encoder_decoder=SCREAMING_SNAKE_CASE , add_cross_attention=SCREAMING_SNAKE_CASE , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=SCREAMING_SNAKE_CASE , add_final_layer_norm=SCREAMING_SNAKE_CASE , )
return encoder_config, decoder_config
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> int:
if "encoder.model" in name:
_lowercase : Any = name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
_lowercase : str = name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
_lowercase : List[str] = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
_lowercase : Optional[Any] = name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
_lowercase : int = 'encoder.' + name
if "attn.proj" in name:
_lowercase : List[str] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
_lowercase : Optional[int] = name.replace('attn' , 'attention.self' )
if "norm1" in name:
_lowercase : str = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
_lowercase : Optional[int] = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
_lowercase : int = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
_lowercase : List[Any] = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
_lowercase : Optional[int] = 'encoder.layernorm.weight'
if name == "encoder.norm.bias":
_lowercase : str = 'encoder.layernorm.bias'
return name
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
for key in orig_state_dict.copy().keys():
_lowercase : str = orig_state_dict.pop(SCREAMING_SNAKE_CASE )
if "qkv" in key:
_lowercase : Tuple = key.split('.' )
_lowercase : Tuple = int(key_split[3] )
_lowercase : Any = int(key_split[5] )
_lowercase : str = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_lowercase : Optional[Any] = val[:dim, :]
_lowercase : List[str] = val[dim : dim * 2, :]
_lowercase : Tuple = val[-dim:, :]
else:
_lowercase : List[Any] = val[:dim]
_lowercase : List[Any] = val[dim : dim * 2]
_lowercase : int = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
_lowercase : List[str] = val
return orig_state_dict
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=False ) -> Dict:
# load original model
_lowercase : str = DonutModel.from_pretrained(SCREAMING_SNAKE_CASE ).eval()
# load HuggingFace model
_lowercase , _lowercase : Any = get_configs(SCREAMING_SNAKE_CASE )
_lowercase : Any = DonutSwinModel(SCREAMING_SNAKE_CASE )
_lowercase : Optional[Any] = MBartForCausalLM(SCREAMING_SNAKE_CASE )
_lowercase : Tuple = VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE , decoder=SCREAMING_SNAKE_CASE )
model.eval()
_lowercase : List[str] = original_model.state_dict()
_lowercase : str = convert_state_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
model.load_state_dict(SCREAMING_SNAKE_CASE )
# verify results on scanned document
_lowercase : List[str] = load_dataset('hf-internal-testing/example-documents' )
_lowercase : Optional[int] = dataset['test'][0]['image'].convert('RGB' )
_lowercase : Optional[Any] = XLMRobertaTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE , from_slow=SCREAMING_SNAKE_CASE )
_lowercase : Tuple = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
_lowercase : str = DonutProcessor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
_lowercase : Optional[Any] = processor(SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
_lowercase : Tuple = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
_lowercase : int = 'When is the coffee break?'
_lowercase : Optional[int] = task_prompt.replace('{user_input}' , SCREAMING_SNAKE_CASE )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
_lowercase : Any = '<s_rvlcdip>'
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
_lowercase : List[Any] = '<s_cord>'
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
_lowercase : Optional[Any] = 's_cord-v2>'
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
_lowercase : List[str] = '<s_zhtrainticket>'
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
_lowercase : Tuple = 'hello world'
else:
raise ValueError('Model name not supported' )
_lowercase : Dict = original_model.decoder.tokenizer(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE , return_tensors='pt' )[
'input_ids'
]
_lowercase : Any = original_model.encoder.model.patch_embed(SCREAMING_SNAKE_CASE )
_lowercase , _lowercase : int = model.encoder.embeddings(SCREAMING_SNAKE_CASE )
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 )
# verify encoder hidden states
_lowercase : str = original_model.encoder(SCREAMING_SNAKE_CASE )
_lowercase : List[str] = model.encoder(SCREAMING_SNAKE_CASE ).last_hidden_state
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-2 )
# verify decoder hidden states
_lowercase : List[Any] = original_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).logits
_lowercase : str = model(SCREAMING_SNAKE_CASE , decoder_input_ids=SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(SCREAMING_SNAKE_CASE )
processor.save_pretrained(SCREAMING_SNAKE_CASE )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ix/donut-base-finetuned-docvqa",
required=False,
type=str,
help="Name of the original model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
required=False,
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 and processor to the 🤗 hub.",
)
UpperCamelCase = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 66 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
'''XmodOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XmodForCausalLM''',
'''XmodForMaskedLM''',
'''XmodForMultipleChoice''',
'''XmodForQuestionAnswering''',
'''XmodForSequenceClassification''',
'''XmodForTokenClassification''',
'''XmodModel''',
'''XmodPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 0 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")
# TF training parameters
snake_case = False
snake_case = False
def SCREAMING_SNAKE_CASE__ ( snake_case__ :Namespace ) -> Tuple:
return TrainCommand(snake_case__ )
class A_ ( UpperCAmelCase ):
"""simple docstring"""
@staticmethod
def __UpperCAmelCase ( __A : ArgumentParser ) -> List[Any]:
_lowercase = parser.add_parser('train' ,help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' ,type=__A ,required=__A ,help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' ,)
train_parser.add_argument(
'--column_label' ,type=__A ,default=0 ,help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' ,type=__A ,default=1 ,help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' ,type=__A ,default=2 ,help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' ,action='store_true' ,help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' ,type=__A ,default='' ,help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' ,type=__A ,default=0.1 ,help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' ,)
train_parser.add_argument('--output' ,type=__A ,default='./' ,help='path to saved the trained model.' )
train_parser.add_argument(
'--task' ,type=__A ,default='text_classification' ,help='Task to train the model on.' )
train_parser.add_argument(
'--model' ,type=__A ,default='bert-base-uncased' ,help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' ,type=__A ,default=32 ,help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' ,type=__A ,default=64 ,help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' ,type=__A ,default=3e-5 ,help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' ,type=__A ,default=1e-08 ,help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=__A )
def __init__( self : Optional[Any] ,__A : Namespace ) -> Tuple:
_lowercase = logging.get_logger('transformers-cli/training' )
_lowercase = 'tf' if is_tf_available() else 'torch'
os.makedirs(args.output ,exist_ok=__A )
_lowercase = args.output
_lowercase = args.column_label
_lowercase = args.column_text
_lowercase = args.column_id
self.logger.info(F"""Loading {args.task} pipeline for {args.model}""" )
if args.task == "text_classification":
_lowercase = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F"""Loading dataset from {args.train_data}""" )
_lowercase = Processor.create_from_csv(
args.train_data ,column_label=args.column_label ,column_text=args.column_text ,column_id=args.column_id ,skip_first_row=args.skip_first_row ,)
_lowercase = None
if args.validation_data:
self.logger.info(F"""Loading validation dataset from {args.validation_data}""" )
_lowercase = Processor.create_from_csv(
args.validation_data ,column_label=args.column_label ,column_text=args.column_text ,column_id=args.column_id ,skip_first_row=args.skip_first_row ,)
_lowercase = args.validation_split
_lowercase = args.train_batch_size
_lowercase = args.valid_batch_size
_lowercase = args.learning_rate
_lowercase = args.adam_epsilon
def __UpperCAmelCase ( self : Optional[Any] ) -> str:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def __UpperCAmelCase ( self : Tuple ) -> List[Any]:
raise NotImplementedError
def __UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
self.pipeline.fit(
self.train_dataset ,validation_data=self.valid_dataset ,validation_split=self.validation_split ,learning_rate=self.learning_rate ,adam_epsilon=self.adam_epsilon ,train_batch_size=self.train_batch_size ,valid_batch_size=self.valid_batch_size ,)
# Save trained pipeline
self.pipeline.save_pretrained(self.output ) | 67 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase_ ( _UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
return getitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
return setitem, k, v
def lowerCamelCase_ ( _UpperCamelCase ) -> Tuple:
"""simple docstring"""
return delitem, k
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , *_UpperCamelCase ) -> str:
"""simple docstring"""
try:
return fun(_UpperCamelCase , *_UpperCamelCase ), None
except Exception as e:
return None, e
lowerCAmelCase_ = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowerCAmelCase_ = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowerCAmelCase_ = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Any = HashMap(initial_block_size=4 )
snake_case_ : Union[str, Any] = {}
for _, (fun, *args) in enumerate(_UpperCamelCase ):
snake_case_ , snake_case_ : str = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = _run_operation(_UpperCamelCase , _UpperCamelCase , *_UpperCamelCase )
assert my_res == py_res
assert str(_UpperCamelCase ) == str(_UpperCamelCase )
assert set(_UpperCamelCase ) == set(_UpperCamelCase )
assert len(_UpperCamelCase ) == len(_UpperCamelCase )
assert set(my.items() ) == set(py.items() )
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
def is_public(_UpperCamelCase ) -> bool:
return not name.startswith('''_''' )
snake_case_ : str = {name for name in dir({} ) if is_public(_UpperCamelCase )}
snake_case_ : str = {name for name in dir(HashMap() ) if is_public(_UpperCamelCase )}
assert dict_public_names > hash_public_names
| 60 | 0 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class _A ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[int] = VQModel
lowerCamelCase : Dict = 'sample'
@property
def _a ( self : List[Any] , __SCREAMING_SNAKE_CASE : Tuple=(32, 32) ) -> str:
__UpperCAmelCase =4
__UpperCAmelCase =3
__UpperCAmelCase =floats_tensor((batch_size, num_channels) + sizes ).to(__SCREAMING_SNAKE_CASE )
return {"sample": image}
@property
def _a ( self : Any ) -> List[str]:
return (3, 32, 32)
@property
def _a ( self : Optional[int] ) -> Tuple:
return (3, 32, 32)
def _a ( self : int ) -> int:
__UpperCAmelCase ={
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 3,
}
__UpperCAmelCase =self.dummy_input
return init_dict, inputs_dict
def _a ( self : str ) -> Dict:
pass
def _a ( self : str ) -> Any:
pass
def _a ( self : Any ) -> str:
__UpperCAmelCase , __UpperCAmelCase =VQModel.from_pretrained("""fusing/vqgan-dummy""" , output_loading_info=__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
self.assertEqual(len(loading_info["""missing_keys"""] ) , 0 )
model.to(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def _a ( self : Tuple ) -> str:
__UpperCAmelCase =VQModel.from_pretrained("""fusing/vqgan-dummy""" )
model.to(__SCREAMING_SNAKE_CASE ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
__UpperCAmelCase =torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
__UpperCAmelCase =image.to(__SCREAMING_SNAKE_CASE )
with torch.no_grad():
__UpperCAmelCase =model(__SCREAMING_SNAKE_CASE ).sample
__UpperCAmelCase =output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
__UpperCAmelCase =torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143] )
# fmt: on
self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
| 68 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase )
snake_case_ : List[str] = int(max_value - min_value ) + 1
snake_case_ : list[list] = [[] for _ in range(_UpperCamelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCamelCase )
return [v for bucket in buckets for v in sorted(_UpperCamelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]
| 60 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , a_ : int , a_ : MutableSequence[float] ):
"""simple docstring"""
if len(a_ ) != degree + 1:
raise ValueError(
"The number of coefficients should be equal to the degree + 1." )
__snake_case = list(a_ )
__snake_case = degree
def __add__( self : List[str] , a_ : Polynomial ):
"""simple docstring"""
if self.degree > polynomial_a.degree:
__snake_case = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , a_ )
else:
__snake_case = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , a_ )
def __sub__( self : Any , a_ : Polynomial ):
"""simple docstring"""
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : Union[str, Any] ):
"""simple docstring"""
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Optional[int] , a_ : Polynomial ):
"""simple docstring"""
__snake_case = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , a_ )
def A ( self : str , a_ : int | float ):
"""simple docstring"""
__snake_case = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : str ):
"""simple docstring"""
__snake_case = ""
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(a_ )
return polynomial
def __repr__( self : str ):
"""simple docstring"""
return self.__str__()
def A ( self : Tuple ):
"""simple docstring"""
__snake_case = [0] * self.degree
for i in range(self.degree ):
__snake_case = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , a_ )
def A ( self : List[Any] , a_ : int | float = 0 ):
"""simple docstring"""
__snake_case = [0] * (self.degree + 2)
__snake_case = constant
for i in range(self.degree + 1 ):
__snake_case = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , a_ )
def __eq__( self : Union[str, Any] , a_ : object ):
"""simple docstring"""
if not isinstance(a_ , a_ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : int , a_ : object ):
"""simple docstring"""
return not self.__eq__(a_ )
| 69 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 | 0 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 50 ):
'''simple docstring'''
lowerCamelCase_ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 70 |
import requests
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : Tuple = {'''Content-Type''': '''application/json'''}
snake_case_ : Any = requests.post(_UpperCamelCase , json={'''text''': message_body} , headers=_UpperCamelCase )
if response.status_code != 200:
snake_case_ : List[Any] = (
'''Request to slack returned an error '''
f'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(_UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 60 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class _snake_case (__SCREAMING_SNAKE_CASE):
__A : torch.FloatTensor
__A : torch.FloatTensor
__A : Optional[torch.FloatTensor] =None
class _snake_case (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
__A : List[Any] =2
@register_to_config
def __init__( self ,_snake_case = 0.02 ,_snake_case = 1_00 ,_snake_case = 1.007 ,_snake_case = 80 ,_snake_case = 0.05 ,_snake_case = 50 ,):
# standard deviation of the initial noise distribution
UpperCAmelCase_ : List[str] = sigma_max
# setable values
UpperCAmelCase_ : int = None
UpperCAmelCase_ : np.IntTensor = None
UpperCAmelCase_ : torch.FloatTensor = None # sigma(t_i)
def UpperCamelCase__ ( self ,_snake_case ,_snake_case = None ):
return sample
def UpperCamelCase__ ( self ,_snake_case ,_snake_case = None ):
UpperCAmelCase_ : Optional[Any] = num_inference_steps
UpperCAmelCase_ : Any = np.arange(0 ,self.num_inference_steps )[::-1].copy()
UpperCAmelCase_ : int = torch.from_numpy(_snake_case ).to(_snake_case )
UpperCAmelCase_ : str = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
UpperCAmelCase_ : List[Any] = torch.tensor(_snake_case ,dtype=torch.floataa ,device=_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case = None ):
if self.config.s_min <= sigma <= self.config.s_max:
UpperCAmelCase_ : List[Any] = min(self.config.s_churn / self.num_inference_steps ,2**0.5 - 1 )
else:
UpperCAmelCase_ : Any = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCAmelCase_ : int = self.config.s_noise * randn_tensor(sample.shape ,generator=_snake_case ).to(sample.device )
UpperCAmelCase_ : str = sigma + gamma * sigma
UpperCAmelCase_ : Dict = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case = True ,):
UpperCAmelCase_ : Optional[int] = sample_hat + sigma_hat * model_output
UpperCAmelCase_ : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCAmelCase_ : Union[str, Any] = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=_snake_case ,derivative=_snake_case ,pred_original_sample=_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case = True ,):
UpperCAmelCase_ : List[Any] = sample_prev + sigma_prev * model_output
UpperCAmelCase_ : Union[str, Any] = (sample_prev - pred_original_sample) / sigma_prev
UpperCAmelCase_ : Optional[int] = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=_snake_case ,derivative=_snake_case ,pred_original_sample=_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ):
raise NotImplementedError()
| 71 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60 | 0 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
_UpperCAmelCase : Optional[int] = re.compile(r'''\s+''')
def UpperCamelCase ( lowercase_ : Dict ) -> Any:
'''simple docstring'''
return {"hash": hashlib.mda(re.sub(lowercase_ , '''''' , example['''content'''] ).encode('''utf-8''' ) ).hexdigest()}
def UpperCamelCase ( lowercase_ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowercase =[len(lowercase_ ) for line in example['''content'''].splitlines()]
return {"line_mean": np.mean(lowercase_ ), "line_max": max(lowercase_ )}
def UpperCamelCase ( lowercase_ : Union[str, Any] ) -> str:
'''simple docstring'''
lowercase =np.mean([c.isalnum() for c in example['''content''']] )
return {"alpha_frac": alpha_frac}
def UpperCamelCase ( lowercase_ : List[str] , lowercase_ : int ) -> Optional[Any]:
'''simple docstring'''
if example["hash"] in uniques:
uniques.remove(example['''hash'''] )
return True
else:
return False
def UpperCamelCase ( lowercase_ : Optional[int] , lowercase_ : Optional[Any]=5 ) -> int:
'''simple docstring'''
lowercase =['''auto-generated''', '''autogenerated''', '''automatically generated''']
lowercase =example['''content'''].splitlines()
for _, line in zip(range(lowercase_ ) , lowercase_ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def UpperCamelCase ( lowercase_ : Any , lowercase_ : Optional[Any]=5 , lowercase_ : List[Any]=0.0_5 ) -> Optional[Any]:
'''simple docstring'''
lowercase =['''unit tests''', '''test file''', '''configuration file''']
lowercase =example['''content'''].splitlines()
lowercase =0
lowercase =0
# first test
for _, line in zip(range(lowercase_ ) , lowercase_ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
lowercase =example['''content'''].count('''\n''' )
lowercase =int(coeff * nlines )
for line in lines:
count_config += line.lower().count('''config''' )
count_test += line.lower().count('''test''' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def UpperCamelCase ( lowercase_ : str ) -> List[str]:
'''simple docstring'''
lowercase =['''def ''', '''class ''', '''for ''', '''while ''']
lowercase =example['''content'''].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : str=4 ) -> int:
'''simple docstring'''
lowercase =example['''content'''].splitlines()
lowercase =0
for line in lines:
counter += line.lower().count('''=''' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def UpperCamelCase ( lowercase_ : Union[str, Any] ) -> int:
'''simple docstring'''
lowercase =tokenizer(example['''content'''] , truncation=lowercase_ )['''input_ids''']
lowercase =len(example['''content'''] ) / len(lowercase_ )
return {"ratio": ratio}
def UpperCamelCase ( lowercase_ : List[Any] ) -> int:
'''simple docstring'''
lowercase ={}
results.update(get_hash(lowercase_ ) )
results.update(line_stats(lowercase_ ) )
results.update(alpha_stats(lowercase_ ) )
results.update(char_token_ratio(lowercase_ ) )
results.update(is_autogenerated(lowercase_ ) )
results.update(is_config_or_test(lowercase_ ) )
results.update(has_no_keywords(lowercase_ ) )
results.update(has_few_assignments(lowercase_ ) )
return results
def UpperCamelCase ( lowercase_ : Any , lowercase_ : Any , lowercase_ : Dict ) -> Optional[int]:
'''simple docstring'''
if not check_uniques(lowercase_ , lowercase_ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def UpperCamelCase ( lowercase_ : str ) -> List[str]:
'''simple docstring'''
with open(lowercase_ , '''rb''' ) as f_in:
with gzip.open(str(lowercase_ ) + '''.gz''' , '''wb''' , compresslevel=6 ) as f_out:
shutil.copyfileobj(lowercase_ , lowercase_ )
os.unlink(lowercase_ )
# Settings
_UpperCAmelCase : Any = HfArgumentParser(PreprocessingArguments)
_UpperCAmelCase : Tuple = parser.parse_args()
if args.num_workers is None:
_UpperCAmelCase : str = multiprocessing.cpu_count()
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
_UpperCAmelCase : int = time.time()
_UpperCAmelCase : Optional[Any] = load_dataset(args.dataset_name, split='''train''')
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
_UpperCAmelCase : int = time.time()
_UpperCAmelCase : Optional[Any] = ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
_UpperCAmelCase : Tuple = set(ds.unique('''hash'''))
_UpperCAmelCase : Optional[int] = len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
_UpperCAmelCase : Dict = time.time()
_UpperCAmelCase : Tuple = ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(F"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
_UpperCAmelCase : str = time.time()
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(F"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
_UpperCAmelCase : Tuple = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
json.dump(duplicate_clusters, f)
_UpperCAmelCase : List[str] = output_dir / '''data'''
data_dir.mkdir(exist_ok=True)
_UpperCAmelCase : List[str] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
_UpperCAmelCase : Dict = str(data_dir / F"""file-{file_number+1:012}.json""")
_UpperCAmelCase : List[str] = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"""Time to save dataset: {time.time()-t_start:.2f}""")
| 72 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Tuple = '''owlvit_text_model'''
def __init__(self , __magic_name__=4_9408 , __magic_name__=512 , __magic_name__=2048 , __magic_name__=12 , __magic_name__=8 , __magic_name__=16 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , __magic_name__=0 , __magic_name__=4_9406 , __magic_name__=4_9407 , **__magic_name__ , ) -> str:
'''simple docstring'''
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
snake_case_ : int = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[Any] = intermediate_size
snake_case_ : str = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Optional[Any] = max_position_embeddings
snake_case_ : str = hidden_act
snake_case_ : Union[str, Any] = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : int = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : str = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit_vision_model'''
def __init__(self , __magic_name__=768 , __magic_name__=3072 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3 , __magic_name__=768 , __magic_name__=32 , __magic_name__="quick_gelu" , __magic_name__=1e-5 , __magic_name__=0.0 , __magic_name__=0.02 , __magic_name__=1.0 , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Union[str, Any] = intermediate_size
snake_case_ : Union[str, Any] = num_hidden_layers
snake_case_ : Tuple = num_attention_heads
snake_case_ : List[Any] = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = patch_size
snake_case_ : List[Any] = hidden_act
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Dict = attention_dropout
snake_case_ : List[str] = initializer_range
snake_case_ : List[Any] = initializer_factor
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : int = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
snake_case_ : str = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = '''owlvit'''
lowerCamelCase_ : Optional[int] = True
def __init__(self , __magic_name__=None , __magic_name__=None , __magic_name__=512 , __magic_name__=2.6_592 , __magic_name__=True , **__magic_name__ , ) -> int:
'''simple docstring'''
super().__init__(**__magic_name__ )
if text_config is None:
snake_case_ : Tuple = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
snake_case_ : str = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
snake_case_ : str = OwlViTTextConfig(**__magic_name__ )
snake_case_ : Union[str, Any] = OwlViTVisionConfig(**__magic_name__ )
snake_case_ : Any = projection_dim
snake_case_ : Union[str, Any] = logit_scale_init_value
snake_case_ : str = return_dict
snake_case_ : Any = 1.0
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__magic_name__ )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(__magic_name__ , **__magic_name__ )
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = {}
snake_case_ : Union[str, Any] = text_config
snake_case_ : Optional[Any] = vision_config
return cls.from_dict(__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Dict = copy.deepcopy(self.__dict__ )
snake_case_ : List[Any] = self.text_config.to_dict()
snake_case_ : List[Any] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
class __lowerCAmelCase ( _a ):
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-4
def lowerCamelCase (self , __magic_name__ , __magic_name__ = -1 , __magic_name__ = -1 , __magic_name__ = None , ) -> Mapping[str, Any]:
'''simple docstring'''
snake_case_ : Dict = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__magic_name__ , seq_length=__magic_name__ , framework=__magic_name__ )
snake_case_ : List[str] = super().generate_dummy_inputs(
processor.image_processor , batch_size=__magic_name__ , framework=__magic_name__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase (self ) -> int:
'''simple docstring'''
return 14
| 60 | 0 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a_ : List[Any] = False
class _snake_case ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREAMING_SNAKE_CASE = VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion')
pipe.to(a)
pipe.set_progress_bar_config(disable=a)
SCREAMING_SNAKE_CASE = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')
SCREAMING_SNAKE_CASE = torch.manual_seed(0)
SCREAMING_SNAKE_CASE = pipe(
image=a , generator=a , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' , ).images
SCREAMING_SNAKE_CASE = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE = np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
| 73 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
| 60 | 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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
lowercase_ = {
"""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"""
),
},
}
lowercase_ = {
"""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"""
),
},
}
lowercase_ = {
"""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"""
),
},
}
lowercase_ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": 512,
"""facebook/dpr-ctx_encoder-multiset-base""": 512,
}
lowercase_ = {
"""facebook/dpr-question_encoder-single-nq-base""": 512,
"""facebook/dpr-question_encoder-multiset-base""": 512,
}
lowercase_ = {
"""facebook/dpr-reader-single-nq-base""": 512,
"""facebook/dpr-reader-multiset-base""": 512,
}
lowercase_ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowercase_ = {
"""facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowercase_ = {
"""facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase_ = DPRContextEncoderTokenizer
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase_ = DPRQuestionEncoderTokenizer
lowercase_ = collections.namedtuple(
"""DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)
lowercase_ = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])
lowercase_ = 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(lowerCAmelCase__ )
class __UpperCamelCase :
"""simple docstring"""
def __call__( self : List[str] , _A : Tuple , _A : Optional[str] = None , _A : Optional[str] = None , _A : Union[bool, str] = False , _A : Union[bool, str] = False , _A : Optional[int] = None , _A : Optional[Union[str, TensorType]] = None , _A : Optional[bool] = None , **_A : Dict , ):
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
_A , padding=_A , truncation=_A , max_length=_A , return_tensors=_A , return_attention_mask=_A , **_A , )
elif titles is None or texts is None:
__SCREAMING_SNAKE_CASE : Tuple = titles if texts is None else texts
return super().__call__(
_A , _A , padding=_A , truncation=_A , max_length=_A , return_tensors=_A , return_attention_mask=_A , **_A , )
__SCREAMING_SNAKE_CASE : List[Any] = titles if not isinstance(_A , _A ) else [titles]
__SCREAMING_SNAKE_CASE : Any = texts if not isinstance(_A , _A ) else [texts]
__SCREAMING_SNAKE_CASE : List[Any] = len(_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = questions if not isinstance(_A , _A ) else [questions] * n_passages
assert len(_A ) == len(
_A ), F'''There should be as many titles than texts but got {len(_A )} titles and {len(_A )} texts.'''
__SCREAMING_SNAKE_CASE : Tuple = super().__call__(_A , _A , padding=_A , truncation=_A )['''input_ids''']
__SCREAMING_SNAKE_CASE : Any = super().__call__(_A , add_special_tokens=_A , padding=_A , truncation=_A )['''input_ids''']
__SCREAMING_SNAKE_CASE : str = {
'''input_ids''': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(_A , _A )
]
}
if return_attention_mask is not False:
__SCREAMING_SNAKE_CASE : Union[str, Any] = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
__SCREAMING_SNAKE_CASE : str = attention_mask
return self.pad(_A , padding=_A , max_length=_A , return_tensors=_A )
def UpperCAmelCase__ ( self : int , _A : BatchEncoding , _A : DPRReaderOutput , _A : int = 16 , _A : int = 64 , _A : int = 4 , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = reader_input['''input_ids''']
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : List[Any] = reader_output[:3]
__SCREAMING_SNAKE_CASE : int = len(_A )
__SCREAMING_SNAKE_CASE : str = sorted(range(_A ) , reverse=_A , key=relevance_logits.__getitem__ )
__SCREAMING_SNAKE_CASE : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
__SCREAMING_SNAKE_CASE : Optional[int] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
__SCREAMING_SNAKE_CASE : Optional[int] = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
__SCREAMING_SNAKE_CASE : int = sequence_ids.index(self.pad_token_id )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = len(_A )
__SCREAMING_SNAKE_CASE : 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=_A , top_spans=_A , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=_A , start_index=_A , end_index=_A , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(_A ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def UpperCAmelCase__ ( self : List[str] , _A : List[int] , _A : List[int] , _A : int , _A : int , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = []
for start_index, start_score in enumerate(_A ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
__SCREAMING_SNAKE_CASE : Optional[Any] = sorted(_A , key=lambda _A : x[1] , reverse=_A )
__SCREAMING_SNAKE_CASE : int = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
__SCREAMING_SNAKE_CASE : Dict = end_index - start_index + 1
assert length <= max_answer_length, F'''Span is too long: {length} > {max_answer_length}'''
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(_A ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(lowerCAmelCase__ )
class __UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = READER_PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = READER_PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase_ = ['''input_ids''', '''attention_mask''']
lowerCAmelCase_ = DPRReaderTokenizer
| 74 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
def __init__(self , __magic_name__=None , **__magic_name__ ) -> Dict:
'''simple docstring'''
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , __magic_name__ , )
super().__init__(args=__magic_name__ , **__magic_name__ )
| 60 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ ( __a ):
def __init__( self : List[str] , _A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = data
def __iter__( self : List[Any] ):
'''simple docstring'''
for element in self.data:
yield element
def a__ ( lowerCAmelCase__=True ) -> str:
UpperCAmelCase__ : Optional[int] = Accelerator(even_batches=lowerCAmelCase__ )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = False ) -> Optional[int]:
if iterable:
UpperCAmelCase__ : int = DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase__ ) ) )
else:
UpperCAmelCase__ : str = TensorDataset(torch.as_tensor(range(lowerCAmelCase__ ) ) )
UpperCAmelCase__ : int = DataLoader(lowerCAmelCase__ , batch_size=lowerCAmelCase__ )
UpperCAmelCase__ : Optional[Any] = accelerator.prepare(lowerCAmelCase__ )
return dl
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> Union[str, Any]:
UpperCAmelCase__ : List[str] = create_dataloader(accelerator=lowerCAmelCase__ , dataset_size=lowerCAmelCase__ , batch_size=lowerCAmelCase__ )
UpperCAmelCase__ : str = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def a__ ( ) -> Tuple:
UpperCAmelCase__ : Tuple = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
lowerCAmelCase__ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
lowerCAmelCase__ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def a__ ( ) -> List[Any]:
UpperCAmelCase__ : Any = create_accelerator(even_batches=lowerCAmelCase__ )
verify_dataloader_batch_sizes(
lowerCAmelCase__ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
lowerCAmelCase__ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def a__ ( ) -> Optional[Any]:
UpperCAmelCase__ : List[str] = create_accelerator(even_batches=lowerCAmelCase__ )
UpperCAmelCase__ : str = torch.nn.Linear(1 , 1 )
UpperCAmelCase__ : Union[str, Any] = accelerator.prepare(lowerCAmelCase__ )
UpperCAmelCase__ : Tuple = create_dataloader(lowerCAmelCase__ , dataset_size=3 , batch_size=1 )
UpperCAmelCase__ : Optional[int] = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(lowerCAmelCase__ ):
UpperCAmelCase__ : Any = ddp_model(batch[0].float() )
UpperCAmelCase__ : Dict = output.sum()
loss.backward()
batch_idxs.append(lowerCAmelCase__ )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def a__ ( lowerCAmelCase__ ) -> Tuple:
with warnings.catch_warnings(record=lowerCAmelCase__ ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , lowerCAmelCase__ )
assert "only supported for multi-GPU" in str(w[-1].message )
def a__ ( ) -> Optional[int]:
UpperCAmelCase__ : Optional[Any] = True
UpperCAmelCase__ : int = False
UpperCAmelCase__ : Tuple = create_accelerator(even_batches=lowerCAmelCase__ )
UpperCAmelCase__ : List[Any] = torch.nn.Linear(1 , 1 )
UpperCAmelCase__ : List[Any] = accelerator.prepare(lowerCAmelCase__ )
UpperCAmelCase__ : Tuple = create_dataloader(lowerCAmelCase__ , dataset_size=3 , batch_size=1 )
UpperCAmelCase__ : Optional[int] = create_dataloader(lowerCAmelCase__ , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=lowerCAmelCase__ ):
UpperCAmelCase__ : Optional[int] = train_dl.batch_sampler.even_batches
UpperCAmelCase__ : Optional[Any] = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def a__ ( ) -> str:
UpperCAmelCase__ : List[str] = True
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = create_accelerator(even_batches=lowerCAmelCase__ )
UpperCAmelCase__ : List[str] = torch.nn.Linear(1 , 1 )
UpperCAmelCase__ : str = accelerator.prepare(lowerCAmelCase__ )
create_dataloader(lowerCAmelCase__ , dataset_size=3 , batch_size=1 , iterable=lowerCAmelCase__ )
UpperCAmelCase__ : Tuple = create_dataloader(lowerCAmelCase__ , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings('''ignore''' )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=lowerCAmelCase__ ):
UpperCAmelCase__ : Union[str, Any] = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def a__ ( ) -> int:
UpperCAmelCase__ : Union[str, Any] = create_accelerator()
UpperCAmelCase__ : List[str] = torch.nn.Linear(1 , 1 )
UpperCAmelCase__ : int = accelerator.prepare(lowerCAmelCase__ )
create_dataloader(lowerCAmelCase__ , dataset_size=3 , batch_size=1 , iterable=lowerCAmelCase__ )
with warnings.catch_warnings(record=lowerCAmelCase__ ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=lowerCAmelCase__ ):
pass
assert issubclass(w[-1].category , lowerCAmelCase__ )
assert "only supported for map-style datasets" in str(w[-1].message )
def a__ ( ) -> Optional[Any]:
UpperCAmelCase__ : Union[str, Any] = create_accelerator()
accelerator.print('''Test that even_batches variable ensures uniform batches across processes''' )
test_default_ensures_even_batch_sizes()
accelerator.print('''Run tests with even_batches disabled''' )
test_can_disable_even_batches()
accelerator.print('''Test joining uneven inputs''' )
test_can_join_uneven_inputs()
accelerator.print('''Test overriding even_batches when joining uneven inputs''' )
test_join_can_override_even_batches()
accelerator.print('''Test overriding even_batches for mixed dataloader types''' )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders''' )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print('''Test join with non DDP distributed raises warning''' )
UpperCAmelCase__ : Tuple = accelerator.state.distributed_type
UpperCAmelCase__ : Dict = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase__ )
UpperCAmelCase__ : int = original_state
if __name__ == "__main__":
main()
| 75 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
snake_case_ : str = '''mock-s3-bucket'''
snake_case_ : str = f'''s3://{mock_bucket}'''
snake_case_ : Any = extract_path_from_uri(_UpperCamelCase )
assert dataset_path.startswith('''s3://''' ) is False
snake_case_ : Optional[Any] = '''./local/path'''
snake_case_ : List[str] = extract_path_from_uri(_UpperCamelCase )
assert dataset_path == new_dataset_path
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : Union[str, Any] = is_remote_filesystem(_UpperCamelCase )
assert is_remote is True
snake_case_ : Union[str, Any] = fsspec.filesystem('''file''' )
snake_case_ : int = is_remote_filesystem(_UpperCamelCase )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : Optional[Any] = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
snake_case_ : Optional[Any] = input_paths[compression_fs_class.protocol]
if input_path is None:
snake_case_ : List[Any] = f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_UpperCamelCase )
snake_case_ : Dict = fsspec.filesystem(compression_fs_class.protocol , fo=_UpperCamelCase )
assert isinstance(_UpperCamelCase , _UpperCamelCase )
snake_case_ : int = os.path.basename(_UpperCamelCase )
snake_case_ : Any = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(_UpperCamelCase , '''r''' , encoding='''utf-8''' ) as f, open(_UpperCamelCase , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
snake_case_ : Union[str, Any] = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
snake_case_ : Any = compressed_file_paths[protocol]
snake_case_ : Any = '''dataset.jsonl'''
snake_case_ : Dict = f'''{protocol}://{member_file_path}::{compressed_file_path}'''
snake_case_ , *snake_case_ : Optional[Any] = fsspec.get_fs_token_paths(_UpperCamelCase )
assert fs.isfile(_UpperCamelCase )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : Optional[int] = hf_api.dataset_info(_UpperCamelCase , token=_UpperCamelCase )
snake_case_ : List[str] = HfFileSystem(repo_info=_UpperCamelCase , token=_UpperCamelCase )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(_UpperCamelCase ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def lowerCamelCase_ ( ) -> Any:
"""simple docstring"""
snake_case_ : Tuple = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(_UpperCamelCase , _UpperCamelCase , clobber=_UpperCamelCase )
with pytest.warns(_UpperCamelCase ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(_UpperCamelCase ) == 1
assert (
str(warning_info[0].message )
== f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
)
| 60 | 0 |
"""simple docstring"""
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 __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if isinstance(__UpperCamelCase , __UpperCamelCase ):
__lowercase : Optional[int] = np.full((len(__UpperCamelCase ), sequence_length, 2) , __UpperCamelCase )
else:
__lowercase : Optional[Any] = np.full((len(__UpperCamelCase ), sequence_length) , __UpperCamelCase )
for i, tensor in enumerate(__UpperCamelCase ):
if padding_side == "right":
if isinstance(__UpperCamelCase , __UpperCamelCase ):
__lowercase : int = tensor[:sequence_length]
else:
__lowercase : Optional[Any] = tensor[:sequence_length]
else:
if isinstance(__UpperCamelCase , __UpperCamelCase ):
__lowercase : str = tensor[:sequence_length]
else:
__lowercase : Union[str, Any] = tensor[:sequence_length]
return out_tensor.tolist()
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Dict = ord(__UpperCamelCase )
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
__lowercase : List[str] = unicodedata.category(__UpperCamelCase )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase =42
UpperCamelCase =True
UpperCamelCase =None
UpperCamelCase =None
UpperCamelCase =-1_00
UpperCamelCase ="pt"
def _lowerCamelCase ( self , UpperCamelCase_ ) -> List[str]:
import torch
__lowercase : int = '''label''' if '''label''' in features[0].keys() else '''labels'''
__lowercase : int = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__lowercase : List[Any] = 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
__lowercase : Union[str, Any] = torch.tensor(batch['''entity_ids'''] ).shape[1]
__lowercase : Optional[int] = self.tokenizer.padding_side
if padding_side == "right":
__lowercase : Tuple = [
list(UpperCamelCase_ ) + [self.label_pad_token_id] * (sequence_length - len(UpperCamelCase_ )) for label in labels
]
else:
__lowercase : int = [
[self.label_pad_token_id] * (sequence_length - len(UpperCamelCase_ )) + list(UpperCamelCase_ ) for label in labels
]
__lowercase : int = [feature['''ner_tags'''] for feature in features]
__lowercase : Any = padding_tensor(UpperCamelCase_ , -1 , UpperCamelCase_ , UpperCamelCase_ )
__lowercase : Dict = [feature['''original_entity_spans'''] for feature in features]
__lowercase : Dict = padding_tensor(UpperCamelCase_ , (-1, -1) , UpperCamelCase_ , UpperCamelCase_ )
__lowercase : Dict = {k: torch.tensor(UpperCamelCase_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 76 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
def __init__(self , **__magic_name__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__magic_name__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
snake_case_ : Any = kwargs.pop('''encoder''' )
snake_case_ : Tuple = encoder_config.pop('''model_type''' )
snake_case_ : Union[str, Any] = kwargs.pop('''decoder''' )
snake_case_ : Union[str, Any] = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
snake_case_ : Optional[int] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : List[str] = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
snake_case_ : Any = True
@classmethod
def lowerCamelCase (cls , __magic_name__ , __magic_name__ , **__magic_name__ ) -> PretrainedConfig:
'''simple docstring'''
logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
snake_case_ : Tuple = True
snake_case_ : Optional[Any] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : str = copy.deepcopy(self.__dict__ )
snake_case_ : Any = self.encoder.to_dict()
snake_case_ : Dict = self.decoder.to_dict()
snake_case_ : Union[str, Any] = self.__class__.model_type
return output
| 60 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A = logging.get_logger(__name__)
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase : Any = WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase , config=UpperCamelCase )
__UpperCAmelCase : int = downstream_dict["projector.weight"]
__UpperCAmelCase : List[Any] = downstream_dict["projector.bias"]
__UpperCAmelCase : Optional[Any] = downstream_dict["model.post_net.linear.weight"]
__UpperCAmelCase : List[Any] = downstream_dict["model.post_net.linear.bias"]
return model
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase , config=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = downstream_dict["model.linear.weight"]
__UpperCAmelCase : Union[str, Any] = downstream_dict["model.linear.bias"]
return model
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : List[str] = WavaVecaForXVector.from_pretrained(UpperCamelCase , config=UpperCamelCase )
__UpperCAmelCase : Tuple = downstream_dict["connector.weight"]
__UpperCAmelCase : str = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
__UpperCAmelCase : int = downstream_dict[
f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
]
__UpperCAmelCase : Union[str, Any] = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
__UpperCAmelCase : Tuple = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
__UpperCAmelCase : Union[str, Any] = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
__UpperCAmelCase : Dict = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
__UpperCAmelCase : Union[str, Any] = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
__UpperCAmelCase : int = downstream_dict["objective.W"]
return model
@torch.no_grad()
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = torch.load(UpperCamelCase , map_location="cpu" )
__UpperCAmelCase : Optional[Any] = checkpoint["Downstream"]
__UpperCAmelCase : int = WavaVecaConfig.from_pretrained(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = WavaVecaFeatureExtractor.from_pretrained(
UpperCamelCase , return_attention_mask=UpperCamelCase , do_normalize=UpperCamelCase )
__UpperCAmelCase : Dict = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
__UpperCAmelCase : List[Any] = convert_classification(UpperCamelCase , UpperCamelCase , UpperCamelCase )
elif arch.endswith("ForAudioFrameClassification" ):
__UpperCAmelCase : List[str] = convert_diarization(UpperCamelCase , UpperCamelCase , UpperCamelCase )
elif arch.endswith("ForXVector" ):
__UpperCAmelCase : str = convert_xvector(UpperCamelCase , UpperCamelCase , UpperCamelCase )
else:
raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}" )
if hf_config.use_weighted_layer_sum:
__UpperCAmelCase : Optional[Any] = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(UpperCamelCase )
hf_model.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
A = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
)
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
A = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 77 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ ) -> List[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = question_encoder
snake_case_ : Optional[int] = generator
snake_case_ : Optional[Any] = self.question_encoder
def lowerCamelCase (self , __magic_name__ ) -> Dict:
'''simple docstring'''
if os.path.isfile(__magic_name__ ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
snake_case_ : str = os.path.join(__magic_name__ , '''question_encoder_tokenizer''' )
snake_case_ : List[Any] = os.path.join(__magic_name__ , '''generator_tokenizer''' )
self.question_encoder.save_pretrained(__magic_name__ )
self.generator.save_pretrained(__magic_name__ )
@classmethod
def lowerCamelCase (cls , __magic_name__ , **__magic_name__ ) -> Any:
'''simple docstring'''
from ..auto.tokenization_auto import AutoTokenizer
snake_case_ : List[str] = kwargs.pop('''config''' , __magic_name__ )
if config is None:
snake_case_ : int = RagConfig.from_pretrained(__magic_name__ )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.question_encoder , subfolder='''question_encoder_tokenizer''' )
snake_case_ : Dict = AutoTokenizer.from_pretrained(
__magic_name__ , config=config.generator , subfolder='''generator_tokenizer''' )
return cls(question_encoder=__magic_name__ , generator=__magic_name__ )
def __call__(self , *__magic_name__ , **__magic_name__ ) -> Tuple:
'''simple docstring'''
return self.current_tokenizer(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
return self.generator.batch_decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self , *__magic_name__ , **__magic_name__ ) -> int:
'''simple docstring'''
return self.generator.decode(*__magic_name__ , **__magic_name__ )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Any = self.question_encoder
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.generator
def lowerCamelCase (self , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "longest" , __magic_name__ = None , __magic_name__ = True , **__magic_name__ , ) -> BatchEncoding:
'''simple docstring'''
warnings.warn(
'''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
'''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
'''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
'''details''' , __magic_name__ , )
if max_length is None:
snake_case_ : Dict = self.current_tokenizer.model_max_length
snake_case_ : List[str] = self(
__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , max_length=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
snake_case_ : Optional[int] = self.current_tokenizer.model_max_length
snake_case_ : Union[str, Any] = self(
text_target=__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , padding=__magic_name__ , max_length=__magic_name__ , truncation=__magic_name__ , **__magic_name__ , )
snake_case_ : str = labels['''input_ids''']
return model_inputs
| 60 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : Dict ) -> Tuple:
'''simple docstring'''
UpperCAmelCase_ = int(snake_case_ )
assert noofclusters < len(snake_case_ )
# Find out the dimensionality
UpperCAmelCase_ = len(vectors[0] )
# Will help select random centroids from among the available vectors
UpperCAmelCase_ = list(range(len(snake_case_ ) ) )
shuffle(snake_case_ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
UpperCAmelCase_ = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
UpperCAmelCase_ = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
UpperCAmelCase_ = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(snake_case_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
UpperCAmelCase_ = tf.placeholder("float64" , [dim] )
UpperCAmelCase_ = []
for centroid in centroids:
cent_assigns.append(tf.assign(snake_case_ , snake_case_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
UpperCAmelCase_ = [tf.Variable(0 ) for i in range(len(snake_case_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
UpperCAmelCase_ = tf.placeholder("int32" )
UpperCAmelCase_ = []
for assignment in assignments:
cluster_assigns.append(tf.assign(snake_case_ , snake_case_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
UpperCAmelCase_ = tf.placeholder("float" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
UpperCAmelCase_ = tf.reduce_mean(snake_case_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
UpperCAmelCase_ = tf.placeholder("float" , [dim] )
UpperCAmelCase_ = tf.placeholder("float" , [dim] )
UpperCAmelCase_ = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(snake_case_ , snake_case_ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
UpperCAmelCase_ = tf.placeholder("float" , [noofclusters] )
UpperCAmelCase_ = tf.argmin(snake_case_ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
UpperCAmelCase_ = tf.initialize_all_variables()
# Initialize all variables
sess.run(snake_case_ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
UpperCAmelCase_ = 1_00
for _ in range(snake_case_ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(snake_case_ ) ):
UpperCAmelCase_ = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
UpperCAmelCase_ = [
sess.run(snake_case_ , feed_dict={va: vect, va: sess.run(snake_case_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
UpperCAmelCase_ = sess.run(
snake_case_ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(snake_case_ ):
# Collect all the vectors assigned to this cluster
UpperCAmelCase_ = [
vectors[i]
for i in range(len(snake_case_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
UpperCAmelCase_ = sess.run(
snake_case_ , feed_dict={mean_input: array(snake_case_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
UpperCAmelCase_ = sess.run(snake_case_ )
UpperCAmelCase_ = sess.run(snake_case_ )
return centroids, assignments
| 78 |
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 __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__=13 , __magic_name__=30 , __magic_name__=2 , __magic_name__=3 , __magic_name__=True , __magic_name__=True , __magic_name__=32 , __magic_name__=5 , __magic_name__=4 , __magic_name__=37 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=10 , __magic_name__=0.02 , __magic_name__=None , ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : Optional[Any] = is_training
snake_case_ : List[Any] = use_labels
snake_case_ : Optional[int] = hidden_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : Optional[Any] = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : List[str] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : List[Any] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case_ : Any = (image_size // patch_size) ** 2
snake_case_ : int = num_patches + 1
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : List[Any] = None
if self.use_labels:
snake_case_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : int = self.get_config()
return config, pixel_values, labels
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
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 lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = ViTMSNModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : List[str] = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
snake_case_ : int = self.type_sequence_label_size
snake_case_ : Tuple = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : Any = model(__magic_name__ , labels=__magic_name__ )
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
snake_case_ : Optional[int] = 1
snake_case_ : List[str] = ViTMSNForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
snake_case_ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ : Any = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ : Optional[int] = config_and_inputs
snake_case_ : Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( _a, _a, unittest.TestCase ):
lowerCamelCase_ : List[Any] = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCamelCase_ : Optional[int] = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
lowerCamelCase_ : int = False
lowerCamelCase_ : Optional[int] = False
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = ViTMSNModelTester(self )
snake_case_ : Optional[Any] = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
pass
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Any = model_class(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ : Optional[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ , snake_case_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(__magic_name__ )
snake_case_ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[int] = [*signature.parameters.keys()]
snake_case_ : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __magic_name__ )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def lowerCamelCase (self ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = ViTMSNModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
torch.manual_seed(2 )
snake_case_ : List[str] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(__magic_name__ )
snake_case_ : str = self.default_image_processor
snake_case_ : str = prepare_img()
snake_case_ : int = image_processor(images=__magic_name__ , return_tensors='''pt''' ).to(__magic_name__ )
# forward pass
with torch.no_grad():
snake_case_ : Optional[int] = model(**__magic_name__ )
# verify the logits
snake_case_ : Optional[int] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __magic_name__ )
snake_case_ : List[Any] = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(__magic_name__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 60 | 0 |
from collections import deque
from .hash_table import HashTable
class UpperCAmelCase_ ( __lowerCamelCase ):
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ):
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(_lowerCAmelCase )
UpperCAmelCase__ : List[str] = self.values[key]
def __UpperCAmelCase ( self ):
return (
sum(self.charge_factor - len(_lowerCAmelCase ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def __UpperCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase=None ):
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(_lowerCAmelCase ) == 0
):
return key
return super()._collision_resolution(_lowerCAmelCase , _lowerCAmelCase )
| 79 |
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_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : List[Any] = '''efficientnet'''
def __init__(self , __magic_name__ = 3 , __magic_name__ = 600 , __magic_name__ = 2.0 , __magic_name__ = 3.1 , __magic_name__ = 8 , __magic_name__ = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ = [] , __magic_name__ = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ = 0.25 , __magic_name__ = "swish" , __magic_name__ = 2560 , __magic_name__ = "mean" , __magic_name__ = 0.02 , __magic_name__ = 0.001 , __magic_name__ = 0.99 , __magic_name__ = 0.5 , __magic_name__ = 0.2 , **__magic_name__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[str] = num_channels
snake_case_ : Tuple = image_size
snake_case_ : Union[str, Any] = width_coefficient
snake_case_ : Tuple = depth_coefficient
snake_case_ : Optional[Any] = depth_divisor
snake_case_ : Optional[int] = kernel_sizes
snake_case_ : str = in_channels
snake_case_ : Optional[Any] = out_channels
snake_case_ : int = depthwise_padding
snake_case_ : Optional[Any] = strides
snake_case_ : Any = num_block_repeats
snake_case_ : Optional[Any] = expand_ratios
snake_case_ : Union[str, Any] = squeeze_expansion_ratio
snake_case_ : Union[str, Any] = hidden_act
snake_case_ : Union[str, Any] = hidden_dim
snake_case_ : Any = pooling_type
snake_case_ : List[str] = initializer_range
snake_case_ : str = batch_norm_eps
snake_case_ : Optional[int] = batch_norm_momentum
snake_case_ : Optional[Any] = dropout_rate
snake_case_ : List[str] = drop_connect_rate
snake_case_ : Union[str, Any] = sum(__magic_name__ ) * 4
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Union[str, Any] = version.parse('''1.11''' )
@property
def lowerCamelCase (self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCamelCase (self ) -> float:
'''simple docstring'''
return 1e-5
| 60 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {
"""bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
"""bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
"""bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
"""bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
"""bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
"""bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
"""bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
"""bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
"""bert-large-uncased-whole-word-masking""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
),
"""bert-large-cased-whole-word-masking""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
),
"""bert-large-uncased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
),
"""bert-large-cased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
),
"""bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
"""bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
"""bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
"""cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
"""cl-tohoku/bert-base-japanese-whole-word-masking""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
),
"""cl-tohoku/bert-base-japanese-char""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
),
"""cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
),
"""TurkuNLP/bert-base-finnish-cased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
),
"""TurkuNLP/bert-base-finnish-uncased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
),
"""wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
# See all BERT models at https://huggingface.co/models?filter=bert
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :int = 'bert'
def __init__( self : Union[str, Any] , _lowerCAmelCase : Union[str, Any]=3_0522 , _lowerCAmelCase : List[str]=768 , _lowerCAmelCase : List[Any]=12 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : List[str]=3072 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : str=0.1 , _lowerCAmelCase : str=0.1 , _lowerCAmelCase : Optional[Any]=512 , _lowerCAmelCase : Dict=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : Dict=1e-12 , _lowerCAmelCase : Optional[int]=0 , _lowerCAmelCase : Optional[Any]="absolute" , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : Optional[Any] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(pad_token_id=_lowerCAmelCase , **_lowerCAmelCase )
__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 __UpperCamelCase ( _lowerCAmelCase ):
@property
def _a ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
"""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),
("""token_type_ids""", dynamic_axis),
] )
| 80 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
lowerCAmelCase_ = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowerCAmelCase_ = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowerCAmelCase_ = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowerCAmelCase_ = [0] * args.vocab_size
for k, v in counter.items():
lowerCAmelCase_ = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 60 | 0 |
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