code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__lowerCAmelCase : Any = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[Any] = ['DPTFeatureExtractor']
__lowerCAmelCase : Union[str, Any] = ['DPTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Union[str, Any] = [
'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DPTForDepthEstimation',
'DPTForSemanticSegmentation',
'DPTModel',
'DPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 107 |
"""simple docstring"""
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
a : Optional[int] = logging.get_logger(__name__)
# General docstring
a : Union[str, Any] = '''MobileNetV1Config'''
# Base docstring
a : str = '''google/mobilenet_v1_1.0_224'''
a : str = [1, 1024, 7, 7]
# Image classification docstring
a : Optional[Any] = '''google/mobilenet_v1_1.0_224'''
a : Optional[int] = '''tabby, tabby cat'''
a : List[str] = [
'''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 _SCREAMING_SNAKE_CASE ( _lowercase : List[Any] , _lowercase : str , _lowercase : int=None ) ->int:
'''simple docstring'''
a : List[Any] = {}
if isinstance(_lowercase , _lowercase ):
a : Union[str, Any] = model.mobilenet_va
else:
a : List[str] = model
a : Dict = "MobilenetV1/Conv2d_0/"
a : Tuple = backbone.conv_stem.convolution.weight
a : Dict = backbone.conv_stem.normalization.bias
a : Optional[Any] = backbone.conv_stem.normalization.weight
a : Optional[Any] = backbone.conv_stem.normalization.running_mean
a : Tuple = backbone.conv_stem.normalization.running_var
for i in range(13 ):
a : List[str] = i + 1
a : Dict = i * 2
a : int = backbone.layer[pt_index]
a : List[str] = F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
a : int = pointer.convolution.weight
a : Union[str, Any] = pointer.normalization.bias
a : Union[str, Any] = pointer.normalization.weight
a : Optional[Any] = pointer.normalization.running_mean
a : Dict = pointer.normalization.running_var
a : List[Any] = backbone.layer[pt_index + 1]
a : Union[str, Any] = F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
a : Dict = pointer.convolution.weight
a : Optional[Any] = pointer.normalization.bias
a : Dict = pointer.normalization.weight
a : Optional[Any] = pointer.normalization.running_mean
a : Optional[Any] = pointer.normalization.running_var
if isinstance(_lowercase , _lowercase ):
a : Dict = "MobilenetV1/Logits/Conv2d_1c_1x1/"
a : Tuple = model.classifier.weight
a : Optional[int] = model.classifier.bias
return tf_to_pt_map
def _SCREAMING_SNAKE_CASE ( _lowercase : Any , _lowercase : List[Any] , _lowercase : Tuple ) ->int:
'''simple docstring'''
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 : List[Any] = tf.train.list_variables(_lowercase )
a : Optional[int] = {}
for name, shape in init_vars:
logger.info(F"""Loading TF weight {name} with shape {shape}""" )
a : Union[str, Any] = tf.train.load_variable(_lowercase , _lowercase )
a : Optional[Any] = array
# Build TF to PyTorch weights loading map
a : Tuple = _build_tf_to_pytorch_map(_lowercase , _lowercase , _lowercase )
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 : List[str] = tf_weights[name]
if "depthwise_weights" in name:
logger.info("Transposing depthwise" )
a : List[Any] = np.transpose(_lowercase , (2, 3, 0, 1) )
elif "weights" in name:
logger.info("Transposing" )
if len(pointer.shape ) == 2: # copying into linear layer
a : Union[str, Any] = array.squeeze().transpose()
else:
a : Any = np.transpose(_lowercase , (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 : str = torch.from_numpy(_lowercase )
tf_weights.pop(_lowercase , _lowercase )
tf_weights.pop(name + "/RMSProp" , _lowercase )
tf_weights.pop(name + "/RMSProp_1" , _lowercase )
tf_weights.pop(name + "/ExponentialMovingAverage" , _lowercase )
logger.info(F"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}""" )
return model
def _SCREAMING_SNAKE_CASE ( _lowercase : torch.Tensor , _lowercase : nn.Convad ) ->torch.Tensor:
'''simple docstring'''
a, a : Any = features.shape[-2:]
a, a : Dict = conv_layer.stride
a, a : int = conv_layer.kernel_size
if in_height % stride_height == 0:
a : Tuple = max(kernel_height - stride_height , 0 )
else:
a : Optional[Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
a : Optional[Any] = max(kernel_width - stride_width , 0 )
else:
a : str = max(kernel_width - (in_width % stride_width) , 0 )
a : Any = pad_along_width // 2
a : List[str] = pad_along_width - pad_left
a : List[str] = pad_along_height // 2
a : List[Any] = pad_along_height - pad_top
a : int = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(_lowercase , _lowercase , "constant" , 0.0 )
class __UpperCamelCase ( nn.Module ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 1 , lowerCAmelCase__ = False , lowerCAmelCase__ = True , lowerCAmelCase__ = True , ) -> None:
super().__init__()
a : str = 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 : Optional[int] = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
a : Tuple = nn.Convad(
in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , kernel_size=lowerCAmelCase__ , stride=lowerCAmelCase__ , padding=lowerCAmelCase__ , groups=lowerCAmelCase__ , bias=lowerCAmelCase__ , padding_mode="zeros" , )
if use_normalization:
a : Optional[int] = nn.BatchNormad(
num_features=lowerCAmelCase__ , eps=config.layer_norm_eps , momentum=0.9_997 , affine=lowerCAmelCase__ , track_running_stats=lowerCAmelCase__ , )
else:
a : int = None
if use_activation:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
a : Optional[int] = ACTaFN[use_activation]
elif isinstance(config.hidden_act , lowerCAmelCase__ ):
a : Dict = ACTaFN[config.hidden_act]
else:
a : Union[str, Any] = config.hidden_act
else:
a : int = None
def __a ( self , lowerCAmelCase__ ) -> torch.Tensor:
if self.config.tf_padding:
a : Union[str, Any] = apply_tf_padding(lowerCAmelCase__ , self.convolution )
a : List[str] = self.convolution(lowerCAmelCase__ )
if self.normalization is not None:
a : int = self.normalization(lowerCAmelCase__ )
if self.activation is not None:
a : Dict = self.activation(lowerCAmelCase__ )
return features
class __UpperCamelCase ( a__ ):
lowerCamelCase : List[str] =MobileNetVaConfig
lowerCamelCase : str =load_tf_weights_in_mobilenet_va
lowerCamelCase : List[str] ="""mobilenet_v1"""
lowerCamelCase : Tuple ="""pixel_values"""
lowerCamelCase : Optional[Any] =False
def __a ( self , lowerCAmelCase__ ) -> None:
if isinstance(lowerCAmelCase__ , (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(lowerCAmelCase__ , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
a : Optional[Any] = 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.
'''
a : List[str] = 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.""" , a__ , )
class __UpperCamelCase ( a__ ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ = True ) -> List[str]:
super().__init__(lowerCAmelCase__ )
a : Tuple = config
a : Dict = 32
a : Optional[int] = max(int(depth * config.depth_multiplier ) , config.min_depth )
a : Dict = MobileNetVaConvLayer(
lowerCAmelCase__ , in_channels=config.num_channels , out_channels=lowerCAmelCase__ , kernel_size=3 , stride=2 , )
a : Dict = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
a : int = nn.ModuleList()
for i in range(13 ):
a : Optional[Any] = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
a : List[str] = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
lowerCAmelCase__ , in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , kernel_size=3 , stride=strides[i] , groups=lowerCAmelCase__ , ) )
self.layer.append(
MobileNetVaConvLayer(
lowerCAmelCase__ , in_channels=lowerCAmelCase__ , out_channels=lowerCAmelCase__ , kernel_size=1 , ) )
a : Tuple = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def __a ( self , lowerCAmelCase__ ) -> Optional[Any]:
raise NotImplementedError
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __a ( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
a : Dict = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
a : Optional[int] = 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 : List[str] = self.conv_stem(lowerCAmelCase__ )
a : Dict = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
a : List[Any] = layer_module(lowerCAmelCase__ )
if output_hidden_states:
a : Optional[Any] = all_hidden_states + (hidden_states,)
a : Any = hidden_states
if self.pooler is not None:
a : Union[str, Any] = torch.flatten(self.pooler(lowerCAmelCase__ ) , start_dim=1 )
else:
a : List[Any] = 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=lowerCAmelCase__ , pooler_output=lowerCAmelCase__ , hidden_states=lowerCAmelCase__ , )
@add_start_docstrings(
"""
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , a__ , )
class __UpperCamelCase ( a__ ):
def __init__( self , lowerCAmelCase__ ) -> None:
super().__init__(lowerCAmelCase__ )
a : int = config.num_labels
a : List[Any] = MobileNetVaModel(lowerCAmelCase__ )
a : List[str] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
a : Union[str, Any] = nn.Dropout(config.classifier_dropout_prob , inplace=lowerCAmelCase__ )
a : str = nn.Linear(lowerCAmelCase__ , 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(lowerCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __a ( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
a : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
a : Any = self.mobilenet_va(lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ )
a : Optional[int] = outputs.pooler_output if return_dict else outputs[1]
a : Tuple = self.classifier(self.dropout(lowerCAmelCase__ ) )
a : Tuple = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
a : List[Any] = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
a : Any = "single_label_classification"
else:
a : int = "multi_label_classification"
if self.config.problem_type == "regression":
a : Tuple = MSELoss()
if self.num_labels == 1:
a : Dict = loss_fct(logits.squeeze() , labels.squeeze() )
else:
a : str = loss_fct(lowerCAmelCase__ , lowerCAmelCase__ )
elif self.config.problem_type == "single_label_classification":
a : List[Any] = CrossEntropyLoss()
a : Any = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
a : int = BCEWithLogitsLoss()
a : Optional[int] = loss_fct(lowerCAmelCase__ , lowerCAmelCase__ )
if not return_dict:
a : Optional[Any] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=lowerCAmelCase__ , logits=lowerCAmelCase__ , hidden_states=outputs.hidden_states , )
| 105 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( a_ : Optional[int] , a_ : Any , a_ : Optional[Any] , a_ : Tuple ) -> str:
# Initialise PyTorch model
__SCREAMING_SNAKE_CASE :Union[str, Any] = FunnelConfig.from_json_file(a_ )
print(f'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE :str = FunnelBaseModel(a_ ) if base_model else FunnelModel(a_ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(a_ , a_ , a_ )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , a_ )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
)
lowerCamelCase_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
) | 239 |
"""simple docstring"""
from __future__ import annotations
import math
def __lowerCamelCase ( a_ : int , a_ : int , a_ : bool , a_ : list[int] , a_ : float ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(a_ ) == 0:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , a_ , a_ , a_ ) , minimax(depth + 1 , node_index * 2 + 1 , a_ , a_ , a_ ) , )
return min(
minimax(depth + 1 , node_index * 2 , a_ , a_ , a_ ) , minimax(depth + 1 , node_index * 2 + 1 , a_ , a_ , a_ ) , )
def __lowerCamelCase ( ) -> None:
__SCREAMING_SNAKE_CASE :Dict = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
__SCREAMING_SNAKE_CASE :Optional[int] = math.log(len(a_ ) , 2 )
print('''Optimal value : ''' , end='''''' )
print(minimax(0 , 0 , a_ , a_ , a_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 239 | 1 |
from manim import *
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : List[Any] = Rectangle(height=0.5 , width=0.5)
a__ : str = Rectangle(height=0.46 , width=0.46).set_stroke(width=0)
a__ : Any = Rectangle(height=0.25 , width=0.25)
a__ : List[Any] = [mem.copy() for i in range(6)]
a__ : Optional[Any] = [mem.copy() for i in range(6)]
a__ : Tuple = VGroup(*lowercase).arrange(lowercase , buff=0)
a__ : Dict = VGroup(*lowercase).arrange(lowercase , buff=0)
a__ : List[Any] = VGroup(lowercase , lowercase).arrange(lowercase , buff=0)
a__ : List[str] = Text('CPU' , font_size=24)
a__ : Optional[Any] = Group(lowercase , lowercase).arrange(lowercase , buff=0.5 , aligned_edge=lowercase)
cpu.move_to([-2.5, -0.5, 0])
self.add(lowercase)
a__ : Dict = [mem.copy() for i in range(4)]
a__ : Optional[Any] = VGroup(*lowercase).arrange(lowercase , buff=0)
a__ : str = Text('GPU' , font_size=24)
a__ : Any = Group(lowercase , lowercase).arrange(lowercase , buff=0.5 , aligned_edge=lowercase)
gpu.move_to([-1, -1, 0])
self.add(lowercase)
a__ : Any = [mem.copy() for i in range(6)]
a__ : List[str] = VGroup(*lowercase).arrange(lowercase , buff=0)
a__ : int = Text('Model' , font_size=24)
a__ : List[Any] = Group(lowercase , lowercase).arrange(lowercase , buff=0.5 , aligned_edge=lowercase)
model.move_to([3, -1.0, 0])
self.add(lowercase)
a__ : Dict = []
a__ : str = []
for i, rect in enumerate(lowercase):
a__ : Tuple = fill.copy().set_fill(lowercase , opacity=0.8)
target.move_to(lowercase)
model_arr.append(lowercase)
a__ : int = Rectangle(height=0.46 , width=0.46).set_stroke(width=0.0).set_fill(lowercase , opacity=0.8)
cpu_target.move_to(cpu_left_col_base[i])
model_cpu_arr.append(lowercase)
self.add(*lowercase , *lowercase)
a__ : Dict = [meta_mem.copy() for i in range(6)]
a__ : List[Any] = [meta_mem.copy() for i in range(6)]
a__ : Union[str, Any] = VGroup(*lowercase).arrange(lowercase , buff=0)
a__ : Dict = VGroup(*lowercase).arrange(lowercase , buff=0)
a__ : List[Any] = VGroup(lowercase , lowercase).arrange(lowercase , buff=0)
a__ : Dict = Text('Disk' , font_size=24)
a__ : Optional[int] = Group(lowercase , lowercase).arrange(lowercase , buff=0.5 , aligned_edge=lowercase)
disk.move_to([-4, -1.25, 0])
self.add(lowercase , lowercase)
a__ : List[str] = Square(side_length=2.2)
key.move_to([-5, 2, 0])
a__ : List[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(lowercase , lowercase)
a__ : Dict = MarkupText(
F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(lowercase , DOWN * 2.4 , aligned_edge=key_text.get_left())
self.add(lowercase)
a__ : Dict = MarkupText(
F'Now watch as an input is passed through the model\nand how the memory is utilized and handled.' , font_size=24 , )
step_a.move_to([2, 2, 0])
self.play(Write(lowercase))
a__ : int = Square(0.3)
input.set_fill(lowercase , opacity=1.0)
input.set_stroke(width=0.0)
input.next_to(model_base[0] , lowercase , buff=0.5)
self.play(Write(lowercase))
input.generate_target()
input.target.next_to(model_arr[0] , direction=lowercase , buff=0.02)
self.play(MoveToTarget(lowercase))
self.play(FadeOut(lowercase))
a__ : str = Arrow(start=lowercase , end=lowercase , color=lowercase , buff=0.5)
a.next_to(model_arr[0].get_left() , lowercase , buff=0.2)
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0])
a__ : Union[str, Any] = MarkupText(
F'As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.' , font_size=24 , )
step_a.move_to([2, 2, 0])
self.play(Write(lowercase , run_time=3))
a__ : Union[str, Any] = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(lowercase) , Circumscribe(model_arr[0] , color=lowercase , **lowercase) , Circumscribe(model_cpu_arr[0] , color=lowercase , **lowercase) , Circumscribe(gpu_rect[0] , color=lowercase , **lowercase) , )
self.play(MoveToTarget(model_cpu_arr[0]))
a__ : List[Any] = a.copy()
for i in range(6):
a_c.next_to(model_arr[i].get_right() + 0.02 , lowercase , buff=0.2)
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02)
a__ : Any = AnimationGroup(
FadeOut(lowercase , run_time=0.5) , MoveToTarget(lowercase , run_time=0.5) , FadeIn(lowercase , run_time=0.5) , lag_ratio=0.2)
self.play(lowercase)
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i])
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0])
if i >= 1:
a__ : Tuple = 0.7
self.play(
Circumscribe(model_arr[i] , **lowercase) , Circumscribe(cpu_left_col_base[i] , **lowercase) , Circumscribe(cpu_left_col_base[i + 1] , color=lowercase , **lowercase) , Circumscribe(gpu_rect[0] , color=lowercase , **lowercase) , Circumscribe(model_arr[i + 1] , color=lowercase , **lowercase) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i]) , MoveToTarget(model_cpu_arr[i + 1]) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1])
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2)
self.play(
Circumscribe(model_arr[-1] , color=lowercase , **lowercase) , Circumscribe(cpu_left_col_base[-1] , color=lowercase , **lowercase) , Circumscribe(gpu_rect[0] , color=lowercase , **lowercase) , )
self.play(MoveToTarget(model_cpu_arr[i]))
a__ : Optional[int] = a_c
a__ : str = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5)
self.play(
FadeOut(lowercase) , FadeOut(lowercase , run_time=0.5) , )
a__ : List[Any] = MarkupText(F'Inference on a model too large for GPU memory\nis successfully completed.' , font_size=24)
step_a.move_to([2, 2, 0])
self.play(Write(lowercase , run_time=3) , MoveToTarget(lowercase))
self.wait()
| 99 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''label_embs_concat''': '''label_embeddings_concat''',
'''mask_emb''': '''masked_spec_embed''',
'''spk_proj''': '''speaker_proj''',
}
lowerCAmelCase__ = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''label_embeddings_concat''',
'''speaker_proj''',
'''layer_norm_for_extract''',
]
def _A ( A__ , A__ , A__ , A__ , A__ ):
"""simple docstring"""
for attribute in key.split('''.''' ):
__lowercase = getattr(A__ , A__ )
if weight_type is not None:
__lowercase = getattr(A__ , A__ ).shape
else:
__lowercase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}" )
if weight_type == "weight":
__lowercase = value
elif weight_type == "weight_g":
__lowercase = value
elif weight_type == "weight_v":
__lowercase = value
elif weight_type == "bias":
__lowercase = value
else:
__lowercase = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def _A ( A__ , A__ ):
"""simple docstring"""
__lowercase = []
__lowercase = fairseq_model.state_dict()
__lowercase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowercase = False
if "conv_layers" in name:
load_conv_layer(
A__ , A__ , A__ , A__ , hf_model.config.feat_extract_norm == '''group''' , )
__lowercase = True
else:
for key, mapped_key in MAPPING.items():
__lowercase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
# special case since naming is very similar
continue
__lowercase = True
if "*" in mapped_key:
__lowercase = name.split(A__ )[0].split('''.''' )[-2]
__lowercase = mapped_key.replace('''*''' , A__ )
if "weight_g" in name:
__lowercase = '''weight_g'''
elif "weight_v" in name:
__lowercase = '''weight_v'''
elif "bias" in name:
__lowercase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowercase = '''weight'''
else:
__lowercase = None
set_recursively(A__ , A__ , A__ , A__ , A__ )
continue
if not is_used:
unused_weights.append(A__ )
logger.warning(F"Unused weights: {unused_weights}" )
def _A ( A__ , A__ , A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = full_name.split('''conv_layers.''' )[-1]
__lowercase = name.split('''.''' )
__lowercase = int(items[0] )
__lowercase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
__lowercase = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
__lowercase = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found." )
__lowercase = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"{full_name} has size {value.shape}, but"
F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found." )
__lowercase = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(A__ )
@torch.no_grad()
def _A ( A__ , A__ , A__=None , A__=None , A__=True ):
"""simple docstring"""
if config_path is not None:
__lowercase = UniSpeechSatConfig.from_pretrained(A__ )
else:
__lowercase = UniSpeechSatConfig()
__lowercase = ''''''
if is_finetuned:
__lowercase = UniSpeechSatForCTC(A__ )
else:
__lowercase = UniSpeechSatForPreTraining(A__ )
__lowercase , __lowercase , __lowercase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
__lowercase = model[0].eval()
recursively_load_weights(A__ , A__ )
hf_wavavec.save_pretrained(A__ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
lowerCAmelCase__ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 104 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCamelCase ( lowerCAmelCase : Any ):
"""simple docstring"""
__magic_name__ : Dict = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(lowerCAmelCase , lowerCAmelCase )
def lowerCamelCase ( lowerCAmelCase : List[str] ):
"""simple docstring"""
__magic_name__ , __magic_name__ : Union[str, Any] = emb.weight.shape
__magic_name__ : Any = nn.Linear(lowerCAmelCase , lowerCAmelCase , bias=lowerCAmelCase )
__magic_name__ : Optional[Any] = emb.weight.data
return lin_layer
def lowerCamelCase ( lowerCAmelCase : List[str] , lowerCAmelCase : Union[str, Any]=None ):
"""simple docstring"""
__magic_name__ : Dict = {}
for old_key in state_dict.keys():
__magic_name__ : Any = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
__magic_name__ : Optional[Any] = key.replace('moe_layer.experts.0' , f'ffn.experts.expert_{expert_idx}' )
else:
__magic_name__ : int = key.replace('moe_layer.experts.' , 'ffn.experts.expert_' )
if "gate" in key:
__magic_name__ : List[str] = key.replace('.moe_layer.gate.wg' , '.ffn.router.classifier' )
if "fc2" and "experts" not in key:
__magic_name__ : Union[str, Any] = key.replace('.fc2.' , '.ffn.fc2.' )
if "fc1" and "experts" not in key:
__magic_name__ : Tuple = key.replace('.fc1.' , '.ffn.fc1.' )
if ".encoder_attn." in key:
__magic_name__ : Tuple = key.replace('.encoder_attn.' , '.cross_attention.' )
if "encoder_attn_layer_norm" in key:
__magic_name__ : Dict = key.replace('encoder_attn_layer_norm' , 'cross_attention_layer_norm' )
if "final_layer_norm" in key:
__magic_name__ : Any = key.replace('final_layer_norm' , 'ff_layer_norm' )
__magic_name__ : Optional[Any] = state_dict[old_key]
return new_dict
def lowerCamelCase ( lowerCAmelCase : str , lowerCAmelCase : Tuple , lowerCAmelCase : str , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : str = WEIGHTS_NAME ):
"""simple docstring"""
__magic_name__ : List[str] = []
__magic_name__ : Union[str, Any] = 0
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
for expert in range(lowerCAmelCase ):
__magic_name__ : List[str] = switch_checkpoint_path + f'-rank-{expert}.pt'
if os.path.isfile(lowerCAmelCase ):
__magic_name__ : int = torch.load(lowerCAmelCase )['model']
remove_ignore_keys_(lowerCAmelCase )
__magic_name__ : Optional[Any] = rename_fairseq_keys(lowerCAmelCase , lowerCAmelCase )
__magic_name__ : Dict = os.path.join(
lowerCAmelCase , weights_name.replace('.bin' , f'-{len(lowerCAmelCase )+1:05d}-of-???.bin' ) )
torch.save(lowerCAmelCase , lowerCAmelCase )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(lowerCAmelCase )[0]].dtype )
# Add the last block
__magic_name__ : Dict = os.path.join(lowerCAmelCase , weights_name.replace('.bin' , f'-{len(lowerCAmelCase )+1:05d}-of-???.bin' ) )
__magic_name__ : Optional[Any] = torch.load(switch_checkpoint_path + '-shared.pt' )['model']
remove_ignore_keys_(lowerCAmelCase )
__magic_name__ : Tuple = rename_fairseq_keys(lowerCAmelCase , lowerCAmelCase )
__magic_name__ : Tuple = shared_weights['decoder.embed_tokens.weight']
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(lowerCAmelCase ) == 1:
__magic_name__ : Dict = os.path.join(lowerCAmelCase , lowerCAmelCase )
torch.save(lowerCAmelCase , lowerCAmelCase )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(lowerCAmelCase , lowerCAmelCase )
# Otherwise, let's build the index
__magic_name__ : List[str] = {}
for idx, shard in enumerate(lowerCAmelCase ):
__magic_name__ : Union[str, Any] = weights_name.replace('.bin' , f'-{idx+1:05d}-of-{len(lowerCAmelCase ):05d}.bin' )
__magic_name__ : List[str] = os.path.join(lowerCAmelCase , weights_name.replace('.bin' , f'-{idx+1:05d}-of-???.bin' ) )
os.rename(lowerCAmelCase , os.path.join(lowerCAmelCase , lowerCAmelCase ) )
for key in shard:
__magic_name__ : int = shard_file
# Add the metadata
__magic_name__ : str = {'total_size': total_size}
__magic_name__ : Union[str, Any] = {'metadata': metadata, 'weight_map': weight_map}
with open(os.path.join(lowerCAmelCase , lowerCAmelCase ) , 'w' , encoding='utf-8' ) as f:
__magic_name__ : Union[str, Any] = json.dumps(lowerCAmelCase , indent=2 , sort_keys=lowerCAmelCase ) + '\n'
f.write(lowerCAmelCase )
return metadata, index
if __name__ == "__main__":
lowerCAmelCase :Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--nllb_moe_checkpoint_path''',
default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
type=str,
required=False,
help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
)
parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
type=str,
required=False,
help='''Path to the output pytorch model.''',
)
lowerCAmelCase :List[str] = parser.parse_args()
lowerCAmelCase , lowerCAmelCase :str = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
1_2_8,
args.dtype,
)
lowerCAmelCase :Dict = NllbMoeConfig.from_pretrained(
'''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
)
config.save_pretrained(args.pytorch_dump_folder_path)
lowerCAmelCase :List[str] = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('''Done''')
model.save_pretrained(args.pytorch_dump_folder_path) | 275 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : Any = """Salesforce/blip-image-captioning-base"""
A_ : Any = (
"""This is a tool that generates a description of an image. It takes an input named `image` which should be the """
"""image to caption, and returns a text that contains the description in English."""
)
A_ : List[str] = """image_captioner"""
A_ : Optional[Any] = AutoModelForVisionaSeq
A_ : Any = ["""image"""]
A_ : Union[str, Any] = ["""text"""]
def __init__( self : Dict , *_A : Dict , **_A : Any ) -> Dict:
requires_backends(self , ['vision'] )
super().__init__(*_A , **_A )
def __lowerCAmelCase ( self : Tuple , _A : "Image" ) -> Union[str, Any]:
return self.pre_processor(images=_A , return_tensors='pt' )
def __lowerCAmelCase ( self : Optional[Any] , _A : str ) -> List[Any]:
return self.model.generate(**_A )
def __lowerCAmelCase ( self : int , _A : Optional[int] ) -> Union[str, Any]:
return self.pre_processor.batch_decode(_A , skip_special_tokens=_A )[0].strip() | 275 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
if index == number_of_items:
return 0
__UpperCamelCase : Dict = 0
__UpperCamelCase : Union[str, Any] = 0
__UpperCamelCase : Tuple = knapsack(snake_case__ , snake_case__ , snake_case__ , snake_case__ , index + 1 )
if weights[index] <= max_weight:
__UpperCamelCase : Dict = values[index] + knapsack(
snake_case__ , snake_case__ , snake_case__ , max_weight - weights[index] , index + 1 )
return max(snake_case__ , snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 298 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
_lowerCAmelCase = logging.getLogger()
def __lowerCAmelCase ( ):
__UpperCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument("-f" )
__UpperCamelCase : Any = parser.parse_args()
return args.f
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Dict = {}
__UpperCamelCase : Dict = os.path.join(snake_case__ , "all_results.json" )
if os.path.exists(snake_case__ ):
with open(snake_case__ , "r" ) as f:
__UpperCamelCase : Any = json.load(snake_case__ )
else:
raise ValueError(F"can't find {path}" )
return results
def __lowerCAmelCase ( ):
__UpperCamelCase : Any = torch.cuda.is_available() and torch_device == "cuda"
return is_using_cuda and is_apex_available()
_lowerCAmelCase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@classmethod
def a_ (cls ) -> Union[str, Any]:
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__UpperCamelCase : Optional[Any] = tempfile.mkdtemp()
__UpperCamelCase : List[str] = os.path.join(cls.tmpdir , "default_config.yml" )
write_basic_config(save_location=cls.configPath )
__UpperCamelCase : Optional[Any] = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def a_ (cls ) -> Union[str, Any]:
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Optional[int]:
__UpperCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"\n {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --seed=42\n --checkpointing_steps epoch\n --with_tracking\n ".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
__UpperCamelCase : Tuple = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "glue_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Dict:
__UpperCamelCase : Optional[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"\n {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --block_size 128\n --per_device_train_batch_size 5\n --per_device_eval_batch_size 5\n --num_train_epochs 2\n --output_dir {tmp_dir}\n --checkpointing_steps epoch\n --with_tracking\n ".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__UpperCamelCase : int = get_results(_UpperCAmelCase )
self.assertLess(result["perplexity"] , 1_0_0 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "clm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Any:
__UpperCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Optional[Any] = f"\n {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --num_train_epochs=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[Any] = get_results(_UpperCAmelCase )
self.assertLess(result["perplexity"] , 4_2 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "mlm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> int:
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__UpperCamelCase : int = 7 if get_gpu_count() > 1 else 2
__UpperCamelCase : int = self.get_auto_remove_tmp_dir()
__UpperCamelCase : str = f"\n {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : List[Any] = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
self.assertLess(result["train_loss"] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "ner_no_trainer" ) ) )
@unittest.skip(reason="Fix me @muellerzr" )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Any:
__UpperCamelCase : Tuple = self.get_auto_remove_tmp_dir()
__UpperCamelCase : str = f"\n {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --seed=42\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[int] = get_results(_UpperCAmelCase )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result["eval_f1"] , 2_8 )
self.assertGreaterEqual(result["eval_exact"] , 2_8 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "qa_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Dict:
__UpperCamelCase : Tuple = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[str] = f"\n {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/swag/sample.json\n --validation_file tests/fixtures/tests_samples/swag/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=20\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Tuple = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "swag_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Union[str, Any]:
__UpperCamelCase : str = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Dict = f"\n {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Dict = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_rouge1"] , 1_0 )
self.assertGreaterEqual(result["eval_rouge2"] , 2 )
self.assertGreaterEqual(result["eval_rougeL"] , 7 )
self.assertGreaterEqual(result["eval_rougeLsum"] , 7 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "summarization_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Tuple:
__UpperCamelCase : Optional[int] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"\n {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n --model_name_or_path sshleifer/student_marian_en_ro_6_1\n --source_lang en\n --target_lang ro\n --train_file tests/fixtures/tests_samples/wmt16/sample.json\n --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --num_beams=6\n --learning_rate=3e-3\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --source_lang en_XX\n --target_lang ro_RO\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : List[Any] = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_bleu"] , 3_0 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "translation_no_trainer" ) ) )
@slow
def a_ (self ) -> List[Any]:
__UpperCamelCase : Tuple = logging.StreamHandler(sys.stdout )
logger.addHandler(_UpperCAmelCase )
__UpperCamelCase : Dict = self.get_auto_remove_tmp_dir()
__UpperCamelCase : List[Any] = f"\n {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n --dataset_name huggingface/semantic-segmentation-test-sample\n --output_dir {tmp_dir}\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n ".split()
run_command(self._launch_args + testargs )
__UpperCamelCase : Optional[int] = get_results(_UpperCAmelCase )
self.assertGreaterEqual(result["eval_overall_accuracy"] , 0.10 )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def a_ (self ) -> Tuple:
__UpperCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
__UpperCamelCase : Optional[Any] = f"\n {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n --model_name_or_path google/vit-base-patch16-224-in21k\n --dataset_name hf-internal-testing/cats_vs_dogs_sample\n --learning_rate 1e-4\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 1\n --max_train_steps 2\n --train_val_split 0.1\n --seed 42\n --output_dir {tmp_dir}\n --with_tracking\n --checkpointing_steps 1\n ".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
__UpperCamelCase : str = get_results(_UpperCAmelCase )
# The base model scores a 25%
self.assertGreaterEqual(result["eval_accuracy"] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "step_1" ) ) )
self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase , "image_classification_no_trainer" ) ) )
| 298 | 1 |
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ = get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class A_ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = AlbertTokenizer
__UpperCamelCase = AlbertTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = True
__UpperCamelCase = True
def UpperCAmelCase__ ( self :Tuple ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase = AlbertTokenizer(lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[Any] ) -> List[Any]:
UpperCAmelCase = 'this is a test'
UpperCAmelCase = 'this is a test'
return input_text, output_text
def UpperCAmelCase__ ( self :List[str] ) -> List[Any]:
UpperCAmelCase = '<pad>'
UpperCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def UpperCAmelCase__ ( self :List[Any] ) -> Optional[Any]:
UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '▁eloquent' )
self.assertEqual(len(lowercase_ ) , 3_00_00 )
def UpperCAmelCase__ ( self :int ) -> int:
self.assertEqual(self.get_tokenizer().vocab_size , 3_00_00 )
def UpperCAmelCase__ ( self :str ) -> str:
if not self.test_rust_tokenizer:
return
UpperCAmelCase = self.get_tokenizer()
UpperCAmelCase = self.get_rust_tokenizer()
UpperCAmelCase = 'I was born in 92000, and this is falsé.'
UpperCAmelCase = tokenizer.tokenize(lowercase_ )
UpperCAmelCase = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
UpperCAmelCase = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase = self.get_rust_tokenizer()
UpperCAmelCase = tokenizer.encode(lowercase_ )
UpperCAmelCase = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Tuple ) -> Union[str, Any]:
UpperCAmelCase = AlbertTokenizer(lowercase_ , keep_accents=lowercase_ )
UpperCAmelCase = tokenizer.tokenize('This is a test' )
self.assertListEqual(lowercase_ , ['▁this', '▁is', '▁a', '▁test'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , [48, 25, 21, 12_89] )
UpperCAmelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowercase_ , ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.'] )
UpperCAmelCase = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(lowercase_ , [31, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9] )
UpperCAmelCase = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.'] , )
def UpperCAmelCase__ ( self :Optional[Any] ) -> List[str]:
UpperCAmelCase = AlbertTokenizer(lowercase_ )
UpperCAmelCase = tokenizer.encode('sequence builders' )
UpperCAmelCase = tokenizer.encode('multi-sequence build' )
UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(lowercase_ )
UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def UpperCAmelCase__ ( self :int ) -> List[str]:
UpperCAmelCase = {'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 2_19_70, 13, 5, 60_92, 1_67, 28, 71_03, 21_53, 6_73, 8, 70_28, 1_20_51, 18, 17, 71_03, 21_53, 6_73, 8, 35_15, 1_86_84, 8, 44_61, 6, 19_27, 2_97, 8, 1_20_60, 26_07, 18, 13, 5, 44_61, 15, 1_05_38, 38, 8, 1_35, 15, 8_22, 58, 15, 9_93, 1_03_63, 15, 14_60, 80_05, 44_61, 15, 9_93, 2_55, 23_28, 9, 9, 9, 6, 26, 11_12, 8_16, 32_60, 13, 5, 1_03, 23_77, 6, 17, 11_12, 8_16, 27_82, 13, 5, 1_03, 1_06_41, 6, 29, 84, 25_12, 24_30, 7_82, 1_86_84, 27_61, 19, 8_08, 24_30, 25_56, 17, 8_55, 14_80, 94_77, 40_91, 1_28, 1_17_12, 15, 71_03, 21_53, 6_73, 17, 2_48_83, 99_90, 9, 3], [2, 1_15_02, 25, 10_06, 20, 7_82, 8, 1_18_09, 8_55, 17_32, 1_93_93, 1_86_67, 37, 3_67, 2_10_18, 69, 18_54, 34, 1_18_60, 1_91_24, 27, 1_56, 2_25, 17, 1_93, 41_41, 19, 65, 91_24, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 22_31, 8_86, 23_85, 1_76_59, 84, 14, 1_67_92, 19_52, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='albert-base-v2' , revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e' , )
| 369 |
"""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
snake_case_ = logging.get_logger(__name__)
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = R'\w+[.]\d+'
UpperCAmelCase = re.findall(lowercase_ , lowercase_ )
for pat in pats:
UpperCAmelCase = key.replace(lowercase_ , '_'.join(pat.split('.' ) ) )
return key
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = 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)
):
UpperCAmelCase = 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:
UpperCAmelCase = 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:
UpperCAmelCase = pt_tuple_key[:-1] + ('embedding',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCAmelCase = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
UpperCAmelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCAmelCase = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight":
UpperCAmelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCAmelCase = pt_tuple_key[:-1] + ('weight',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCAmelCase = 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 _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_=42 ):
# Step 1: Convert pytorch tensor to numpy
UpperCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
UpperCAmelCase = flax_model.init_weights(PRNGKey(lowercase_ ) )
UpperCAmelCase = flatten_dict(lowercase_ )
UpperCAmelCase = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCAmelCase = rename_key(lowercase_ )
UpperCAmelCase = tuple(renamed_pt_key.split('.' ) )
# Correctly rename weight parameters
UpperCAmelCase , UpperCAmelCase = rename_key_and_reshape_tensor(lowercase_ , lowercase_ , lowercase_ )
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
UpperCAmelCase = jnp.asarray(lowercase_ )
return unflatten_dict(lowercase_ )
| 181 | 0 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 64 | def __lowercase ( lowerCamelCase : str , lowerCamelCase : str ):
def get_matched_characters(lowerCamelCase : str , lowerCamelCase : str ) -> str:
UpperCamelCase_ : Tuple = []
UpperCamelCase_ : List[Any] = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCamelCase_ : int = int(max(0 , i - limit ) )
UpperCamelCase_ : Dict = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCamelCase )
UpperCamelCase_ : Dict = F"{_stra[0:_stra.index(lowerCamelCase )]} {_stra[_stra.index(lowerCamelCase ) + 1:]}"
return "".join(lowerCamelCase )
# matching characters
UpperCamelCase_ : str = get_matched_characters(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : str = get_matched_characters(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : Union[str, Any] = len(lowerCamelCase )
# transposition
UpperCamelCase_ : int = (
len([(ca, ca) for ca, ca in zip(lowerCamelCase , lowerCamelCase ) if ca != ca] ) // 2
)
if not match_count:
UpperCamelCase_ : Union[str, Any] = 0.0
else:
UpperCamelCase_ : str = (
1
/ 3
* (
match_count / len(lowerCamelCase )
+ match_count / len(lowerCamelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCamelCase_ : Dict = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 175 | 0 |
"""simple docstring"""
import numpy as np
__magic_name__ = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self):
__SCREAMING_SNAKE_CASE = np.array(lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = np.where(letter == self.SQUARE)
__SCREAMING_SNAKE_CASE = np.concatenate([indexa + 1, indexa + 1])
return indexes
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = self.SQUARE[indexa - 1, indexa - 1]
return letter
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = message.lower()
__SCREAMING_SNAKE_CASE = message.replace(""" """ , """""")
__SCREAMING_SNAKE_CASE = message.replace("""j""" , """i""")
__SCREAMING_SNAKE_CASE = np.empty((2, len(lowerCAmelCase__)))
for letter_index in range(len(lowerCAmelCase__)):
__SCREAMING_SNAKE_CASE = self.letter_to_numbers(message[letter_index])
__SCREAMING_SNAKE_CASE = numbers[0]
__SCREAMING_SNAKE_CASE = numbers[1]
__SCREAMING_SNAKE_CASE = first_step.reshape(2 * len(lowerCAmelCase__))
__SCREAMING_SNAKE_CASE = """"""
for numbers_index in range(len(lowerCAmelCase__)):
__SCREAMING_SNAKE_CASE = int(second_step[numbers_index * 2])
__SCREAMING_SNAKE_CASE = int(second_step[(numbers_index * 2) + 1])
__SCREAMING_SNAKE_CASE = self.numbers_to_letter(lowerCAmelCase__ , lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = encoded_message + letter
return encoded_message
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = message.lower()
message.replace(""" """ , """""")
__SCREAMING_SNAKE_CASE = np.empty(2 * len(lowerCAmelCase__))
for letter_index in range(len(lowerCAmelCase__)):
__SCREAMING_SNAKE_CASE = self.letter_to_numbers(message[letter_index])
__SCREAMING_SNAKE_CASE = numbers[0]
__SCREAMING_SNAKE_CASE = numbers[1]
__SCREAMING_SNAKE_CASE = first_step.reshape((2, len(lowerCAmelCase__)))
__SCREAMING_SNAKE_CASE = """"""
for numbers_index in range(len(lowerCAmelCase__)):
__SCREAMING_SNAKE_CASE = int(second_step[0, numbers_index])
__SCREAMING_SNAKE_CASE = int(second_step[1, numbers_index])
__SCREAMING_SNAKE_CASE = self.numbers_to_letter(lowerCAmelCase__ , lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = decoded_message + letter
return decoded_message
| 255 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
__magic_name__ = False
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def snake_case_ ( self):
return 1_2
@property
def snake_case_ ( self):
return 1_2
@property
def snake_case_ ( self):
return 3_2
@property
def snake_case_ ( self):
torch.manual_seed(0)
__SCREAMING_SNAKE_CASE = VQModel(
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=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
return tokenizer
@property
def snake_case_ ( self):
torch.manual_seed(0)
__SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(lowerCAmelCase__)
@property
def snake_case_ ( self):
torch.manual_seed(0)
__SCREAMING_SNAKE_CASE = 1_2
__SCREAMING_SNAKE_CASE = 1_2
__SCREAMING_SNAKE_CASE = {
"""attention_bias""": True,
"""cross_attention_dim""": 3_2,
"""attention_head_dim""": height * width,
"""num_attention_heads""": 1,
"""num_vector_embeds""": self.num_embed,
"""num_embeds_ada_norm""": self.num_embeds_ada_norm,
"""norm_num_groups""": 3_2,
"""sample_size""": width,
"""activation_fn""": """geglu-approximate""",
}
__SCREAMING_SNAKE_CASE = TransformeraDModel(**lowerCAmelCase__)
return model
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = """cpu"""
__SCREAMING_SNAKE_CASE = self.dummy_vqvae
__SCREAMING_SNAKE_CASE = self.dummy_text_encoder
__SCREAMING_SNAKE_CASE = self.dummy_tokenizer
__SCREAMING_SNAKE_CASE = self.dummy_transformer
__SCREAMING_SNAKE_CASE = VQDiffusionScheduler(self.num_embed)
__SCREAMING_SNAKE_CASE = LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = VQDiffusionPipeline(
vqvae=lowerCAmelCase__ , text_encoder=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , transformer=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , learned_classifier_free_sampling_embeddings=lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__)
pipe.set_progress_bar_config(disable=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = """teddy bear playing in the pool"""
__SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__).manual_seed(0)
__SCREAMING_SNAKE_CASE = pipe([prompt] , generator=lowerCAmelCase__ , num_inference_steps=2 , output_type="""np""")
__SCREAMING_SNAKE_CASE = output.images
__SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__).manual_seed(0)
__SCREAMING_SNAKE_CASE = pipe(
[prompt] , generator=lowerCAmelCase__ , output_type="""np""" , return_dict=lowerCAmelCase__ , num_inference_steps=2)[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
__SCREAMING_SNAKE_CASE = np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92])
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 snake_case_ ( self):
__SCREAMING_SNAKE_CASE = """cpu"""
__SCREAMING_SNAKE_CASE = self.dummy_vqvae
__SCREAMING_SNAKE_CASE = self.dummy_text_encoder
__SCREAMING_SNAKE_CASE = self.dummy_tokenizer
__SCREAMING_SNAKE_CASE = self.dummy_transformer
__SCREAMING_SNAKE_CASE = VQDiffusionScheduler(self.num_embed)
__SCREAMING_SNAKE_CASE = LearnedClassifierFreeSamplingEmbeddings(
learnable=lowerCAmelCase__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length)
__SCREAMING_SNAKE_CASE = VQDiffusionPipeline(
vqvae=lowerCAmelCase__ , text_encoder=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , transformer=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , learned_classifier_free_sampling_embeddings=lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__)
pipe.set_progress_bar_config(disable=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = """teddy bear playing in the pool"""
__SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__).manual_seed(0)
__SCREAMING_SNAKE_CASE = pipe([prompt] , generator=lowerCAmelCase__ , num_inference_steps=2 , output_type="""np""")
__SCREAMING_SNAKE_CASE = output.images
__SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__).manual_seed(0)
__SCREAMING_SNAKE_CASE = pipe(
[prompt] , generator=lowerCAmelCase__ , output_type="""np""" , return_dict=lowerCAmelCase__ , num_inference_steps=2)[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
__SCREAMING_SNAKE_CASE = np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88])
assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy""")
__SCREAMING_SNAKE_CASE = VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq""")
__SCREAMING_SNAKE_CASE = pipeline.to(lowerCAmelCase__)
pipeline.set_progress_bar_config(disable=lowerCAmelCase__)
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__).manual_seed(0)
__SCREAMING_SNAKE_CASE = pipeline(
"""teddy bear playing in the pool""" , num_images_per_prompt=1 , generator=lowerCAmelCase__ , output_type="""np""" , )
__SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert np.abs(expected_image - image).max() < 2.0
| 255 | 1 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowercase_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
__UpperCAmelCase : Optional[datasets.Features] = None
__UpperCAmelCase : str = "utf-8"
__UpperCAmelCase : Optional[str] = None
__UpperCAmelCase : Optional[str] = None
__UpperCAmelCase : bool = True # deprecated
__UpperCAmelCase : Optional[int] = None # deprecated
__UpperCAmelCase : int = 1_0 << 2_0 # 10MB
__UpperCAmelCase : Optional[bool] = None
class __lowerCAmelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = JsonConfig
def __UpperCAmelCase ( self ):
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
__a = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def __UpperCAmelCase ( self , _a ):
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
__a = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_a , (str, list, tuple) ):
__a = data_files
if isinstance(_a , _a ):
__a = [files]
__a = [dl_manager.iter_files(_a ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
__a = []
for split_name, files in data_files.items():
if isinstance(_a , _a ):
__a = [files]
__a = [dl_manager.iter_files(_a ) for file in files]
splits.append(datasets.SplitGenerator(name=_a , gen_kwargs={'''files''': files} ) )
return splits
def __UpperCAmelCase ( self , _a ):
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
__a = self.config.features.arrow_schema.field(_a ).type
__a = pa_table.append_column(_a , pa.array([None] * len(_a ) , type=_a ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
__a = table_cast(_a , self.config.features.arrow_schema )
return pa_table
def __UpperCAmelCase ( self , _a ):
for file_idx, file in enumerate(itertools.chain.from_iterable(_a ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(_a , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__a = json.load(_a )
# We keep only the field we are interested in
__a = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(_a , (list, tuple) ):
__a = set().union(*[row.keys() for row in dataset] )
__a = {col: [row.get(_a ) for row in dataset] for col in keys}
else:
__a = dataset
__a = pa.Table.from_pydict(_a )
yield file_idx, self._cast_table(_a )
# If the file has one json object per line
else:
with open(_a , '''rb''' ) as f:
__a = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
__a = max(self.config.chunksize // 32 , 16 << 10 )
__a = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
__a = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(_a )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
__a = batch.decode(self.config.encoding , errors=_a ).encode('''utf-8''' )
try:
while True:
try:
__a = paj.read_json(
io.BytesIO(_a ) , read_options=paj.ReadOptions(block_size=_a ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(_a , pa.ArrowInvalid )
and "straddling" not in str(_a )
or block_size > len(_a )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'''Batch of {len(_a )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
_a , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__a = json.load(_a )
except json.JSONDecodeError:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {e}''' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(_a , _a ): # list is the only sequence type supported in JSON
try:
__a = set().union(*[row.keys() for row in dataset] )
__a = {col: [row.get(_a ) for row in dataset] for col in keys}
__a = pa.Table.from_pydict(_a )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {e}''' )
raise ValueError(f'''Not able to read records in the JSON file at {file}.''' ) from None
yield file_idx, self._cast_table(_a )
break
else:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {e}''' )
raise ValueError(
f'''Not able to read records in the JSON file at {file}. '''
f'''You should probably indicate the field of the JSON file containing your records. '''
f'''This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '''
f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_a )
batch_idx += 1
| 45 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""" , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :int = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :List[Any] = jax.device_count()
lowerCAmelCase_ :Optional[Any] = num_samples * [prompt]
lowerCAmelCase_ :int = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Optional[Any] = replicate(__A )
lowerCAmelCase_ :Union[str, Any] = shard(__A )
lowerCAmelCase_ :Optional[Any] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :Tuple = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Union[str, Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Optional[int] = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Union[str, Any] = """stabilityai/stable-diffusion-2"""
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = FlaxDPMSolverMultistepScheduler.from_pretrained(__A , subfolder="""scheduler""" )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = FlaxStableDiffusionPipeline.from_pretrained(
__A , scheduler=__A , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :Optional[int] = scheduler_params
lowerCAmelCase_ :List[Any] = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :Tuple = jax.device_count()
lowerCAmelCase_ :str = num_samples * [prompt]
lowerCAmelCase_ :Union[str, Any] = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Tuple = replicate(__A )
lowerCAmelCase_ :Optional[int] = shard(__A )
lowerCAmelCase_ :List[str] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :List[Any] = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Optional[Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Dict = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 84 | 0 |
'''simple docstring'''
import pprint
import requests
a : Any = 'https://zenquotes.io/api'
def __magic_name__ ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def __magic_name__ ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
a : Optional[Any] = random_quotes()
pprint.pprint(response)
| 363 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
a : Any = get_logger(__name__)
a : Union[str, Any] = Path(__file__).parent / 'model_card_template.md'
a : List[Any] = uuida().hex
a : List[str] = os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
a : str = os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
a : Optional[Any] = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'
def __magic_name__ ( __UpperCAmelCase = None ) -> str:
'''simple docstring'''
snake_case_ = F"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += F"; torch/{_torch_version}"
if is_flax_available():
ua += F"; jax/{_jax_version}"
ua += F"; flax/{_flax_version}"
if is_onnx_available():
ua += F"; onnxruntime/{_onnxruntime_version}"
# CI will set this value to True
if os.environ.get('''DIFFUSERS_IS_CI''', '''''' ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(__UpperCAmelCase, __UpperCAmelCase ):
ua += "; " + "; ".join(F"{k}/{v}" for k, v in user_agent.items() )
elif isinstance(__UpperCAmelCase, __UpperCAmelCase ):
ua += "; " + user_agent
return ua
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None ) -> Optional[Any]:
'''simple docstring'''
if token is None:
snake_case_ = HfFolder.get_token()
if organization is None:
snake_case_ = whoami(__UpperCAmelCase )['''name''']
return F"{username}/{model_id}"
else:
return F"{organization}/{model_id}"
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
if not is_jinja_available():
raise ValueError(
'''Modelcard rendering is based on Jinja templates.'''
''' Please make sure to have `jinja` installed before using `create_model_card`.'''
''' To install it, please run `pip install Jinja2`.''' )
if hasattr(__UpperCAmelCase, '''local_rank''' ) and args.local_rank not in [-1, 0]:
return
snake_case_ = args.hub_token if hasattr(__UpperCAmelCase, '''hub_token''' ) else None
snake_case_ = get_full_repo_name(__UpperCAmelCase, token=__UpperCAmelCase )
snake_case_ = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language='''en''', license='''apache-2.0''', library_name='''diffusers''', tags=[], datasets=args.dataset_name, metrics=[], ), template_path=__UpperCAmelCase, model_name=__UpperCAmelCase, repo_name=__UpperCAmelCase, dataset_name=args.dataset_name if hasattr(__UpperCAmelCase, '''dataset_name''' ) else None, learning_rate=args.learning_rate, train_batch_size=args.train_batch_size, eval_batch_size=args.eval_batch_size, gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(__UpperCAmelCase, '''gradient_accumulation_steps''' ) else None
), adam_betaa=args.adam_betaa if hasattr(__UpperCAmelCase, '''adam_beta1''' ) else None, adam_betaa=args.adam_betaa if hasattr(__UpperCAmelCase, '''adam_beta2''' ) else None, adam_weight_decay=args.adam_weight_decay if hasattr(__UpperCAmelCase, '''adam_weight_decay''' ) else None, adam_epsilon=args.adam_epsilon if hasattr(__UpperCAmelCase, '''adam_epsilon''' ) else None, lr_scheduler=args.lr_scheduler if hasattr(__UpperCAmelCase, '''lr_scheduler''' ) else None, lr_warmup_steps=args.lr_warmup_steps if hasattr(__UpperCAmelCase, '''lr_warmup_steps''' ) else None, ema_inv_gamma=args.ema_inv_gamma if hasattr(__UpperCAmelCase, '''ema_inv_gamma''' ) else None, ema_power=args.ema_power if hasattr(__UpperCAmelCase, '''ema_power''' ) else None, ema_max_decay=args.ema_max_decay if hasattr(__UpperCAmelCase, '''ema_max_decay''' ) else None, mixed_precision=args.mixed_precision, )
snake_case_ = os.path.join(args.output_dir, '''README.md''' )
model_card.save(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = None ) -> Optional[Any]:
'''simple docstring'''
if resolved_file is None or commit_hash is not None:
return commit_hash
snake_case_ = str(Path(__UpperCAmelCase ).as_posix() )
snake_case_ = re.search(r'''snapshots/([^/]+)/''', __UpperCAmelCase )
if search is None:
return None
snake_case_ = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(__UpperCAmelCase ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
a : str = os.path.expanduser(
os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
a : Optional[Any] = os.path.join(hf_cache_home, 'diffusers')
def __magic_name__ ( __UpperCAmelCase = None, __UpperCAmelCase = None ) -> None:
'''simple docstring'''
if new_cache_dir is None:
snake_case_ = DIFFUSERS_CACHE
if old_cache_dir is None:
snake_case_ = old_diffusers_cache
snake_case_ = Path(__UpperCAmelCase ).expanduser()
snake_case_ = Path(__UpperCAmelCase ).expanduser()
for old_blob_path in old_cache_dir.glob('''**/blobs/*''' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
snake_case_ = new_cache_dir / old_blob_path.relative_to(__UpperCAmelCase )
new_blob_path.parent.mkdir(parents=__UpperCAmelCase, exist_ok=__UpperCAmelCase )
os.replace(__UpperCAmelCase, __UpperCAmelCase )
try:
os.symlink(__UpperCAmelCase, __UpperCAmelCase )
except OSError:
logger.warning(
'''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.''' )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
a : Tuple = os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
a : Tuple = 0
else:
with open(cache_version_file) as f:
try:
a : Optional[Any] = int(f.read())
except ValueError:
a : List[str] = 0
if cache_version < 1:
a : Tuple = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
'existing cached models. This is a one-time operation, you can interrupt it or run it '
'later by calling `diffusers.utils.hub_utils.move_cache()`.'
)
try:
move_cache()
except Exception as e:
a : str = '\n'.join(traceback.format_tb(e.__traceback__))
logger.error(
f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
'message and we will do our best to help.'
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, 'w') as f:
f.write('1')
except Exception:
logger.warning(
f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
'the directory exists and can be written to.'
)
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = None ) -> str:
'''simple docstring'''
if variant is not None:
snake_case_ = weights_name.split('''.''' )
snake_case_ = splits[:-1] + [variant] + splits[-1:]
snake_case_ = '''.'''.join(__UpperCAmelCase )
return weights_name
def __magic_name__ ( __UpperCAmelCase, *,
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=None, ) -> int:
'''simple docstring'''
snake_case_ = str(__UpperCAmelCase )
if os.path.isfile(__UpperCAmelCase ):
return pretrained_model_name_or_path
elif os.path.isdir(__UpperCAmelCase ):
if os.path.isfile(os.path.join(__UpperCAmelCase, __UpperCAmelCase ) ):
# Load from a PyTorch checkpoint
snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) ):
snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase )
return model_file
else:
raise EnvironmentError(
F"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}." )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(__UpperCAmelCase ).base_version ) >= version.parse('''0.20.0''' )
):
try:
snake_case_ = hf_hub_download(
__UpperCAmelCase, filename=_add_variant(__UpperCAmelCase, __UpperCAmelCase ), cache_dir=__UpperCAmelCase, force_download=__UpperCAmelCase, proxies=__UpperCAmelCase, resume_download=__UpperCAmelCase, local_files_only=__UpperCAmelCase, use_auth_token=__UpperCAmelCase, user_agent=__UpperCAmelCase, subfolder=__UpperCAmelCase, revision=revision or commit_hash, )
warnings.warn(
F"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.", __UpperCAmelCase, )
return model_file
except: # noqa: E722
warnings.warn(
F"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__UpperCAmelCase, __UpperCAmelCase )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(__UpperCAmelCase, __UpperCAmelCase )}' so that the correct variant file can be added.", __UpperCAmelCase, )
try:
# 2. Load model file as usual
snake_case_ = hf_hub_download(
__UpperCAmelCase, filename=__UpperCAmelCase, cache_dir=__UpperCAmelCase, force_download=__UpperCAmelCase, proxies=__UpperCAmelCase, resume_download=__UpperCAmelCase, local_files_only=__UpperCAmelCase, use_auth_token=__UpperCAmelCase, user_agent=__UpperCAmelCase, subfolder=__UpperCAmelCase, revision=revision or commit_hash, )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
F"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
'''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
'''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
'''login`.''' )
except RevisionNotFoundError:
raise EnvironmentError(
F"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
'''this model name. Check the model page at '''
F"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." )
except EntryNotFoundError:
raise EnvironmentError(
F"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}." )
except HTTPError as err:
raise EnvironmentError(
F"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" )
except ValueError:
raise EnvironmentError(
F"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
F" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
F" directory containing a file named {weights_name} or"
''' \nCheckout your internet connection or see how to run the library in'''
''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.''' )
except EnvironmentError:
raise EnvironmentError(
F"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
'''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
F"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
F"containing a file named {weights_name}" )
| 72 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class UpperCAmelCase__ ( lowercase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ShapEImgaImgPipeline
__UpperCAmelCase : Any = ['''image''']
__UpperCAmelCase : Optional[Any] = ['''image''']
__UpperCAmelCase : List[str] = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
__UpperCAmelCase : List[str] = False
@property
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowercase ( self : Tuple ):
'''simple docstring'''
return 8
@property
def __lowercase ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
_a : Optional[int] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size ,image_size=64 ,projection_dim=self.text_embedder_hidden_size ,intermediate_size=37 ,num_attention_heads=4 ,num_channels=3 ,num_hidden_layers=5 ,patch_size=1 ,)
_a : int = CLIPVisionModel(_a )
return model
@property
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
_a : Dict = CLIPImageProcessor(
crop_size=224 ,do_center_crop=_a ,do_normalize=_a ,do_resize=_a ,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] ,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] ,resample=3 ,size=224 ,)
return image_processor
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
_a : str = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
_a : Tuple = PriorTransformer(**_a )
return model
@property
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
_a : Tuple = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
_a : List[str] = ShapERenderer(**_a )
return model
def __lowercase ( self : List[str] ):
'''simple docstring'''
_a : int = self.dummy_prior
_a : Optional[int] = self.dummy_image_encoder
_a : Dict = self.dummy_image_processor
_a : List[Any] = self.dummy_renderer
_a : Union[str, Any] = HeunDiscreteScheduler(
beta_schedule='exp' ,num_train_timesteps=1024 ,prediction_type='sample' ,use_karras_sigmas=_a ,clip_sample=_a ,clip_sample_range=1.0 ,)
_a : Optional[int] = {
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __lowercase ( self : Optional[int] ,_a : Tuple ,_a : str=0 ):
'''simple docstring'''
_a : int = floats_tensor((1, 3, 64, 64) ,rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith('mps' ):
_a : Optional[Any] = torch.manual_seed(_a )
else:
_a : str = torch.Generator(device=_a ).manual_seed(_a )
_a : str = {
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def __lowercase ( self : Tuple ):
'''simple docstring'''
_a : str = 'cpu'
_a : List[str] = self.get_dummy_components()
_a : int = self.pipeline_class(**_a )
_a : int = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : List[str] = pipe(**self.get_dummy_inputs(_a ) )
_a : List[Any] = output.images[0]
_a : Any = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_a : Any = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __lowercase ( self : Tuple ):
'''simple docstring'''
_a : int = torch_device == 'cpu'
_a : Optional[Any] = True
self._test_inference_batch_single_identical(
batch_size=2 ,test_max_difference=_a ,relax_max_difference=_a ,)
def __lowercase ( self : Any ):
'''simple docstring'''
_a : List[str] = self.get_dummy_components()
_a : List[str] = self.pipeline_class(**_a )
_a : Any = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : str = 1
_a : Dict = 2
_a : List[str] = self.get_dummy_inputs(_a )
for key in inputs.keys():
if key in self.batch_params:
_a : Optional[int] = batch_size * [inputs[key]]
_a : Union[str, Any] = pipe(**_a ,num_images_per_prompt=_a )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : Tuple ):
'''simple docstring'''
_a : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png' )
_a : Any = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy' )
_a : Optional[int] = ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img' )
_a : List[Any] = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : List[str] = torch.Generator(device=_a ).manual_seed(0 )
_a : int = pipe(
_a ,generator=_a ,guidance_scale=3.0 ,num_inference_steps=64 ,frame_size=64 ,output_type='np' ,).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_a ,_a )
| 271 |
'''simple docstring'''
def UpperCAmelCase_ (__a : list , __a : list , __a : int ):
"""simple docstring"""
_a : Optional[Any] = len(__a )
_a : int = [[0] * n for i in range(__a )]
for i in range(__a ):
_a : Tuple = y_points[i]
for i in range(2 , __a ):
for j in range(__a , __a ):
_a : Tuple = (
(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()
| 271 | 1 |
"""simple docstring"""
import heapq
import sys
import numpy as np
SCREAMING_SNAKE_CASE : List[str] = tuple[int, int]
class __lowerCamelCase :
def __init__(self ):
'''simple docstring'''
_lowerCAmelCase = []
_lowerCAmelCase = set()
def A__ (self ):
'''simple docstring'''
if not self.empty():
return self.elements[0][0]
else:
return float("""inf""" )
def A__ (self ):
'''simple docstring'''
return len(self.elements ) == 0
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(UpperCamelCase__ )
else:
# update
# print("update", item)
_lowerCAmelCase = []
((_lowerCAmelCase) , (_lowerCAmelCase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((_lowerCAmelCase) , (_lowerCAmelCase)) = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
if item in self.set:
self.set.remove(UpperCamelCase__ )
_lowerCAmelCase = []
((_lowerCAmelCase) , (_lowerCAmelCase)) = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((_lowerCAmelCase) , (_lowerCAmelCase)) = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def A__ (self ):
'''simple docstring'''
return self.elements[0][1]
def A__ (self ):
'''simple docstring'''
((_lowerCAmelCase) , (_lowerCAmelCase)) = heapq.heappop(self.elements )
self.set.remove(UpperCamelCase__ )
return (priority, item)
def __UpperCAmelCase ( snake_case_ : TPos , snake_case_ : TPos ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = np.array(__UpperCamelCase )
_lowerCAmelCase = np.array(__UpperCamelCase )
return np.linalg.norm(a - b )
def __UpperCAmelCase ( snake_case_ : TPos , snake_case_ : TPos ) -> str:
"""simple docstring"""
return consistent_heuristic(__UpperCamelCase , __UpperCamelCase ) // t
def __UpperCAmelCase ( snake_case_ : TPos , snake_case_ : TPos ) -> Dict:
"""simple docstring"""
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def __UpperCAmelCase ( snake_case_ : TPos , snake_case_ : int , snake_case_ : TPos , snake_case_ : dict[TPos, float] ) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase = g_function[start] + Wa * heuristics[i](__UpperCamelCase , __UpperCamelCase )
return ans
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : List[Any] , snake_case_ : int ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = np.chararray((n, n) )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
_lowerCAmelCase = """*"""
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
if (j, (n - 1) - i) in blocks:
_lowerCAmelCase = """#"""
_lowerCAmelCase = """-"""
_lowerCAmelCase = back_pointer[goal]
while x != start:
((_lowerCAmelCase) , (_lowerCAmelCase)) = x
# print(x)
_lowerCAmelCase = """-"""
_lowerCAmelCase = back_pointer[x]
_lowerCAmelCase = """-"""
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=""" """ )
print("""<-- End position""" , end=""" """ )
else:
print(grid[i][j] , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
print("""PATH TAKEN BY THE ALGORITHM IS:-""" )
_lowerCAmelCase = back_pointer[goal]
while x != start:
print(__UpperCamelCase , end=""" """ )
_lowerCAmelCase = back_pointer[x]
print(__UpperCamelCase )
sys.exit()
def __UpperCAmelCase ( snake_case_ : TPos ) -> int:
"""simple docstring"""
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : Dict , snake_case_ : List[Any] , snake_case_ : str , snake_case_ : Optional[Any] , snake_case_ : List[Any] , snake_case_ : Union[str, Any] , snake_case_ : str , ) -> Tuple:
"""simple docstring"""
for itera in range(__UpperCamelCase ):
open_list[itera].remove_element(__UpperCamelCase )
# print("s", s)
# print("j", j)
((_lowerCAmelCase) , (_lowerCAmelCase)) = s
_lowerCAmelCase = (x - 1, y)
_lowerCAmelCase = (x + 1, y)
_lowerCAmelCase = (x, y + 1)
_lowerCAmelCase = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(__UpperCamelCase ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(__UpperCamelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = float("""inf""" )
if valid(__UpperCamelCase ) and g_function[neighbours] > g_function[s] + 1:
_lowerCAmelCase = g_function[s] + 1
_lowerCAmelCase = s
if neighbours not in close_list_anchor:
open_list[0].put(__UpperCamelCase , key(__UpperCamelCase , 0 , __UpperCamelCase , __UpperCamelCase ) )
if neighbours not in close_list_inad:
for var in range(1 , __UpperCamelCase ):
if key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) <= Wa * key(
__UpperCamelCase , 0 , __UpperCamelCase , __UpperCamelCase ):
open_list[j].put(
__UpperCamelCase , key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) )
def __UpperCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
SCREAMING_SNAKE_CASE : List[str] = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
SCREAMING_SNAKE_CASE : List[Any] = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(1_0, 1),
(1_1, 1),
(1_2, 1),
(1_3, 1),
(1_4, 1),
(1_5, 1),
(1_6, 1),
(1_7, 1),
(1_8, 1),
(1_9, 1),
]
SCREAMING_SNAKE_CASE : List[str] = make_common_ground()
SCREAMING_SNAKE_CASE : Any = blocks_blk
# hyper parameters
SCREAMING_SNAKE_CASE : Tuple = 1
SCREAMING_SNAKE_CASE : Dict = 1
SCREAMING_SNAKE_CASE : List[str] = 2_0
SCREAMING_SNAKE_CASE : Optional[int] = 3 # one consistent and two other inconsistent
# start and end destination
SCREAMING_SNAKE_CASE : Optional[Any] = (0, 0)
SCREAMING_SNAKE_CASE : Dict = (n - 1, n - 1)
SCREAMING_SNAKE_CASE : Dict = 1
def __UpperCAmelCase ( snake_case_ : TPos , snake_case_ : TPos , snake_case_ : int ) -> str:
"""simple docstring"""
_lowerCAmelCase = {start: 0, goal: float("""inf""" )}
_lowerCAmelCase = {start: -1, goal: -1}
_lowerCAmelCase = []
_lowerCAmelCase = set()
for i in range(__UpperCamelCase ):
open_list.append(PriorityQueue() )
open_list[i].put(__UpperCamelCase , key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) )
_lowerCAmelCase = []
_lowerCAmelCase = []
while open_list[0].minkey() < float("""inf""" ):
for i in range(1 , __UpperCamelCase ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("""inf""" ):
do_something(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
else:
_lowerCAmelCase , _lowerCAmelCase = open_list[i].top_show()
visited.add(__UpperCamelCase )
expand_state(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
close_list_inad.append(__UpperCamelCase )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("""inf""" ):
do_something(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
else:
_lowerCAmelCase = open_list[0].top_show()
visited.add(__UpperCamelCase )
expand_state(
__UpperCamelCase , 0 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , )
close_list_anchor.append(__UpperCamelCase )
print("""No path found to goal""" )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(__UpperCamelCase ):
if (j, i) in blocks:
print("""#""" , end=""" """ )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("""*""" , end=""" """ )
else:
print("""-""" , end=""" """ )
else:
print("""*""" , end=""" """ )
if (j, i) == (n - 1, n - 1):
print("""<-- End position""" , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic) | 369 |
"""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
SCREAMING_SNAKE_CASE : List[str] = False
class __lowerCamelCase ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
image=lowerCamelCase , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
_lowerCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = 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 | 317 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCamelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : str = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 316 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = [1]
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 0, 0, 0
__UpperCamelCase = ugly_nums[ia] * 2
__UpperCamelCase = ugly_nums[ia] * 3
__UpperCamelCase = ugly_nums[ia] * 5
for _ in range(1 , snake_case ):
__UpperCamelCase = min(snake_case , snake_case , snake_case )
ugly_nums.append(snake_case )
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
__UpperCamelCase = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f'''{ugly_numbers(2_0_0) = }''')
| 316 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase__ :str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ :str = ["ViTFeatureExtractor"]
lowercase__ :int = ["ViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ :int = [
"VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"ViTForImageClassification",
"ViTForMaskedImageModeling",
"ViTModel",
"ViTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ :Any = [
"TFViTForImageClassification",
"TFViTModel",
"TFViTPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ :Optional[int] = [
"FlaxViTForImageClassification",
"FlaxViTModel",
"FlaxViTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
lowercase__ :Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 97 |
lowercase__ :Any = 8.3_144_598
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol''' )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
lowercase__ :Optional[Any] = 300
lowercase__ :List[Any] = 28
lowercase__ :Dict = rms_speed_of_molecule(temperature, molar_mass)
print(F'Vrms of Nitrogen gas at 300 K is {vrms} m/s')
| 97 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=False , A_=True , A_=99 , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ) -> int:
__UpperCamelCase =parent
__UpperCamelCase =batch_size
__UpperCamelCase =seq_length
__UpperCamelCase =is_training
__UpperCamelCase =use_input_mask
__UpperCamelCase =use_token_type_ids
__UpperCamelCase =use_labels
__UpperCamelCase =vocab_size
__UpperCamelCase =hidden_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_attention_heads
__UpperCamelCase =intermediate_size
__UpperCamelCase =hidden_act
__UpperCamelCase =hidden_dropout_prob
__UpperCamelCase =attention_probs_dropout_prob
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =type_vocab_size
__UpperCamelCase =type_sequence_label_size
__UpperCamelCase =initializer_range
__UpperCamelCase =num_labels
__UpperCamelCase =num_choices
__UpperCamelCase =scope
def _a ( self ) -> Dict:
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase =None
if self.use_input_mask:
__UpperCamelCase =random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase =None
if self.use_token_type_ids:
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase =None
__UpperCamelCase =None
__UpperCamelCase =None
if self.use_labels:
__UpperCamelCase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase =ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self ) -> int:
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A_ , initializer_range=self.initializer_range , use_stable_embedding=A_ , )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> Dict:
__UpperCamelCase =OpenLlamaModel(config=A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ )
__UpperCamelCase =model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> Optional[int]:
__UpperCamelCase =True
__UpperCamelCase =OpenLlamaModel(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , )
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , )
__UpperCamelCase =model(A_ , attention_mask=A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> Union[str, Any]:
__UpperCamelCase =OpenLlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> List[Any]:
__UpperCamelCase =True
__UpperCamelCase =True
__UpperCamelCase =OpenLlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
# first forward pass
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , use_cache=A_ , )
__UpperCamelCase =outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__UpperCamelCase =ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCamelCase =ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__UpperCamelCase =torch.cat([input_ids, next_tokens] , dim=-1 )
__UpperCamelCase =torch.cat([input_mask, next_mask] , dim=-1 )
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , output_hidden_states=A_ , )['hidden_states'][0]
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , past_key_values=A_ , output_hidden_states=A_ , )['hidden_states'][0]
# select random slice
__UpperCamelCase =ids_tensor((1,) , output_from_past.shape[-1] ).item()
__UpperCamelCase =output_from_no_past[:, -3:, random_slice_idx].detach()
__UpperCamelCase =output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A_ , A_ , atol=1E-3 ) )
def _a ( self ) -> List[str]:
__UpperCamelCase =self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) =config_and_inputs
__UpperCamelCase ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A_ , A_ , A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
UpperCAmelCase__ : List[Any] = (OpenLlamaForCausalLM,) if is_torch_available() else ()
UpperCAmelCase__ : str = (
{
"feature-extraction": OpenLlamaModel,
"text-classification": OpenLlamaForSequenceClassification,
"text-generation": OpenLlamaForCausalLM,
"zero-shot": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : Optional[int] = False
def _a ( self ) -> List[str]:
__UpperCamelCase =OpenLlamaModelTester(self )
__UpperCamelCase =ConfigTester(self , config_class=A_ , hidden_size=37 )
def _a ( self ) -> Tuple:
self.config_tester.run_common_tests()
def _a ( self ) -> Any:
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def _a ( self ) -> str:
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__UpperCamelCase =type
self.model_tester.create_and_check_model(*A_ )
def _a ( self ) -> Dict:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase ='single_label_classification'
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase ='multi_label_classification'
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test' )
def _a ( self ) -> List[Any]:
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def _a ( self , A_ ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =ids_tensor([1, 10] , config.vocab_size )
__UpperCamelCase =ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCamelCase =OpenLlamaModel(A_ )
original_model.to(A_ )
original_model.eval()
__UpperCamelCase =original_model(A_ ).last_hidden_state
__UpperCamelCase =original_model(A_ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCamelCase ={'type': scaling_type, 'factor': 10.0}
__UpperCamelCase =OpenLlamaModel(A_ )
scaled_model.to(A_ )
scaled_model.eval()
__UpperCamelCase =scaled_model(A_ ).last_hidden_state
__UpperCamelCase =scaled_model(A_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A_ , A_ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A_ , A_ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A_ , A_ , atol=1E-5 ) )
| 62 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = Dict[str, Any]
lowerCamelCase__ = List[Prediction]
@add_end_docstrings(lowerCamelCase__ )
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
def __init__( self : Tuple , *__lowercase : Tuple , **__lowercase : Optional[int] ):
'''simple docstring'''
super().__init__(*__lowercase , **__lowercase )
if self.framework == "tf":
raise ValueError(F"The {self.__class__} is only available in PyTorch." )
requires_backends(self , """vision""" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def UpperCamelCase_ ( self : Optional[int] , **__lowercase : List[str] ):
'''simple docstring'''
__a = {}
if "threshold" in kwargs:
__a = kwargs["""threshold"""]
return {}, {}, postprocess_kwargs
def __call__( self : List[Any] , *__lowercase : Any , **__lowercase : Tuple ):
'''simple docstring'''
return super().__call__(*__lowercase , **__lowercase )
def UpperCamelCase_ ( self : str , __lowercase : Tuple ):
'''simple docstring'''
__a = load_image(__lowercase )
__a = torch.IntTensor([[image.height, image.width]] )
__a = self.image_processor(images=[image] , return_tensors="""pt""" )
if self.tokenizer is not None:
__a = self.tokenizer(text=inputs["""words"""] , boxes=inputs["""boxes"""] , return_tensors="""pt""" )
__a = target_size
return inputs
def UpperCamelCase_ ( self : Dict , __lowercase : List[str] ):
'''simple docstring'''
__a = model_inputs.pop("""target_size""" )
__a = self.model(**__lowercase )
__a = outputs.__class__({"""target_size""": target_size, **outputs} )
if self.tokenizer is not None:
__a = model_inputs["""bbox"""]
return model_outputs
def UpperCamelCase_ ( self : Optional[int] , __lowercase : List[Any] , __lowercase : List[Any]=0.9 ):
'''simple docstring'''
__a = model_outputs["""target_size"""]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
__a , __a = target_size[0].tolist()
def unnormalize(__lowercase : Optional[Any] ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
__a , __a = model_outputs["""logits"""].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
__a = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
__a = [unnormalize(__lowercase ) for bbox in model_outputs["""bbox"""].squeeze(0 )]
__a = ["""score""", """label""", """box"""]
__a = [dict(zip(__lowercase , __lowercase ) ) for vals in zip(scores.tolist() , __lowercase , __lowercase ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
__a = self.image_processor.post_process_object_detection(__lowercase , __lowercase , __lowercase )
__a = raw_annotations[0]
__a = raw_annotation["""scores"""]
__a = raw_annotation["""labels"""]
__a = raw_annotation["""boxes"""]
__a = scores.tolist()
__a = [self.model.config.idalabel[label.item()] for label in labels]
__a = [self._get_bounding_box(__lowercase ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
__a = ["""score""", """label""", """box"""]
__a = [
dict(zip(__lowercase , __lowercase ) )
for vals in zip(raw_annotation["""scores"""] , raw_annotation["""labels"""] , raw_annotation["""boxes"""] )
]
return annotation
def UpperCamelCase_ ( self : Optional[int] , __lowercase : "torch.Tensor" ):
'''simple docstring'''
if self.framework != "pt":
raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""" )
__a , __a , __a , __a = box.int().tolist()
__a = {
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 302 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
UpperCAmelCase = logging.get_logger(__name__)
# General docstring
UpperCAmelCase = '''RegNetConfig'''
# Base docstring
UpperCAmelCase = '''facebook/regnet-y-040'''
UpperCAmelCase = [1, 1_088, 7, 7]
# Image classification docstring
UpperCAmelCase = '''facebook/regnet-y-040'''
UpperCAmelCase = '''tabby, tabby cat'''
UpperCAmelCase = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self : List[Any] , snake_case__ : int , snake_case__ : int = 3 , snake_case__ : int = 1 , snake_case__ : int = 1 , snake_case__ : Optional[str] = "relu" , **snake_case__ : Tuple , ):
'''simple docstring'''
super().__init__(**snake_case__ )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
lowercase :Optional[int] = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
lowercase :Dict = tf.keras.layers.ConvaD(
filters=snake_case__ , kernel_size=snake_case__ , strides=snake_case__ , padding='''VALID''' , groups=snake_case__ , use_bias=snake_case__ , name='''convolution''' , )
lowercase :Optional[Any] = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
lowercase :List[Any] = ACTaFN[activation] if activation is not None else tf.identity
def __snake_case ( self : str , snake_case__ : Tuple ):
'''simple docstring'''
lowercase :List[Any] = self.convolution(self.padding(snake_case__ ) )
lowercase :List[Any] = self.normalization(snake_case__ )
lowercase :List[Any] = self.activation(snake_case__ )
return hidden_state
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self : List[Any] , snake_case__ : RegNetConfig , **snake_case__ : List[str] ):
'''simple docstring'''
super().__init__(**snake_case__ )
lowercase :Union[str, Any] = config.num_channels
lowercase :Tuple = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='''embedder''' , )
def __snake_case ( self : Optional[int] , snake_case__ : Tuple ):
'''simple docstring'''
lowercase :Dict = shape_list(snake_case__ )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
lowercase :str = tf.transpose(snake_case__ , perm=(0, 2, 3, 1) )
lowercase :Any = self.embedder(snake_case__ )
return hidden_state
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self : Tuple , snake_case__ : int , snake_case__ : int = 2 , **snake_case__ : Dict ):
'''simple docstring'''
super().__init__(**snake_case__ )
lowercase :List[Any] = tf.keras.layers.ConvaD(
filters=snake_case__ , kernel_size=1 , strides=snake_case__ , use_bias=snake_case__ , name='''convolution''' )
lowercase :List[str] = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name='''normalization''' )
def __snake_case ( self : int , snake_case__ : tf.Tensor , snake_case__ : bool = False ):
'''simple docstring'''
return self.normalization(self.convolution(snake_case__ ) , training=snake_case__ )
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self : Dict , snake_case__ : int , snake_case__ : int , **snake_case__ : Optional[Any] ):
'''simple docstring'''
super().__init__(**snake_case__ )
lowercase :str = tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__ , name='''pooler''' )
lowercase :List[Any] = [
tf.keras.layers.ConvaD(filters=snake_case__ , kernel_size=1 , activation='''relu''' , name='''attention.0''' ),
tf.keras.layers.ConvaD(filters=snake_case__ , kernel_size=1 , activation='''sigmoid''' , name='''attention.2''' ),
]
def __snake_case ( self : Optional[Any] , snake_case__ : Dict ):
'''simple docstring'''
lowercase :Any = self.pooler(snake_case__ )
for layer_module in self.attention:
lowercase :List[str] = layer_module(snake_case__ )
lowercase :Tuple = hidden_state * pooled
return hidden_state
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self : Dict , snake_case__ : RegNetConfig , snake_case__ : int , snake_case__ : int , snake_case__ : int = 1 , **snake_case__ : Optional[int] ):
'''simple docstring'''
super().__init__(**snake_case__ )
lowercase :Tuple = in_channels != out_channels or stride != 1
lowercase :Dict = max(1 , out_channels // config.groups_width )
lowercase :Optional[int] = (
TFRegNetShortCut(snake_case__ , stride=snake_case__ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
lowercase :List[Any] = [
TFRegNetConvLayer(snake_case__ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
snake_case__ , stride=snake_case__ , groups=snake_case__ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetConvLayer(snake_case__ , kernel_size=1 , activation=snake_case__ , name='''layer.2''' ),
]
lowercase :Union[str, Any] = ACTaFN[config.hidden_act]
def __snake_case ( self : int , snake_case__ : Tuple ):
'''simple docstring'''
lowercase :Dict = hidden_state
for layer_module in self.layers:
lowercase :Optional[Any] = layer_module(snake_case__ )
lowercase :Optional[int] = self.shortcut(snake_case__ )
hidden_state += residual
lowercase :Dict = self.activation(snake_case__ )
return hidden_state
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self : Union[str, Any] , snake_case__ : RegNetConfig , snake_case__ : int , snake_case__ : int , snake_case__ : int = 1 , **snake_case__ : List[Any] ):
'''simple docstring'''
super().__init__(**snake_case__ )
lowercase :Dict = in_channels != out_channels or stride != 1
lowercase :Any = max(1 , out_channels // config.groups_width )
lowercase :Dict = (
TFRegNetShortCut(snake_case__ , stride=snake_case__ , name='''shortcut''' )
if should_apply_shortcut
else tf.keras.layers.Activation('''linear''' , name='''shortcut''' )
)
lowercase :Dict = [
TFRegNetConvLayer(snake_case__ , kernel_size=1 , activation=config.hidden_act , name='''layer.0''' ),
TFRegNetConvLayer(
snake_case__ , stride=snake_case__ , groups=snake_case__ , activation=config.hidden_act , name='''layer.1''' ),
TFRegNetSELayer(snake_case__ , reduced_channels=int(round(in_channels / 4 ) ) , name='''layer.2''' ),
TFRegNetConvLayer(snake_case__ , kernel_size=1 , activation=snake_case__ , name='''layer.3''' ),
]
lowercase :Dict = ACTaFN[config.hidden_act]
def __snake_case ( self : int , snake_case__ : List[Any] ):
'''simple docstring'''
lowercase :Tuple = hidden_state
for layer_module in self.layers:
lowercase :Any = layer_module(snake_case__ )
lowercase :Dict = self.shortcut(snake_case__ )
hidden_state += residual
lowercase :Dict = self.activation(snake_case__ )
return hidden_state
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self : List[Any] , snake_case__ : RegNetConfig , snake_case__ : int , snake_case__ : int , snake_case__ : int = 2 , snake_case__ : int = 2 , **snake_case__ : List[Any] ):
'''simple docstring'''
super().__init__(**snake_case__ )
lowercase :str = TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
lowercase :str = [
# downsampling is done in the first layer with stride of 2
layer(snake_case__ , snake_case__ , snake_case__ , stride=snake_case__ , name='''layers.0''' ),
*[layer(snake_case__ , snake_case__ , snake_case__ , name=f"""layers.{i+1}""" ) for i in range(depth - 1 )],
]
def __snake_case ( self : int , snake_case__ : str ):
'''simple docstring'''
for layer_module in self.layers:
lowercase :str = layer_module(snake_case__ )
return hidden_state
class __magic_name__ ( tf.keras.layers.Layer ):
def __init__( self : Any , snake_case__ : RegNetConfig , **snake_case__ : int ):
'''simple docstring'''
super().__init__(**snake_case__ )
lowercase :Optional[Any] = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
snake_case__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='''stages.0''' , ) )
lowercase :List[str] = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(snake_case__ , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(snake_case__ , snake_case__ , snake_case__ , depth=snake_case__ , name=f"""stages.{i+1}""" ) )
def __snake_case ( self : str , snake_case__ : tf.Tensor , snake_case__ : bool = False , snake_case__ : bool = True ):
'''simple docstring'''
lowercase :Union[str, Any] = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase :Optional[Any] = hidden_states + (hidden_state,)
lowercase :Optional[int] = stage_module(snake_case__ )
if output_hidden_states:
lowercase :List[str] = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=snake_case__ , hidden_states=snake_case__ )
@keras_serializable
class __magic_name__ ( tf.keras.layers.Layer ):
__A : Optional[Any] = RegNetConfig
def __init__( self : List[str] , snake_case__ : Dict , **snake_case__ : int ):
'''simple docstring'''
super().__init__(**snake_case__ )
lowercase :Tuple = config
lowercase :Optional[Any] = TFRegNetEmbeddings(snake_case__ , name='''embedder''' )
lowercase :int = TFRegNetEncoder(snake_case__ , name='''encoder''' )
lowercase :Any = tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__ , name='''pooler''' )
@unpack_inputs
def __snake_case ( self : str , snake_case__ : tf.Tensor , snake_case__ : Optional[bool] = None , snake_case__ : Optional[bool] = None , snake_case__ : bool = False , ):
'''simple docstring'''
lowercase :int = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase :int = return_dict if return_dict is not None else self.config.use_return_dict
lowercase :Tuple = self.embedder(snake_case__ , training=snake_case__ )
lowercase :Optional[Any] = self.encoder(
snake_case__ , output_hidden_states=snake_case__ , return_dict=snake_case__ , training=snake_case__ )
lowercase :Optional[Any] = encoder_outputs[0]
lowercase :Union[str, Any] = self.pooler(snake_case__ )
# Change to NCHW output format have uniformity in the modules
lowercase :Tuple = tf.transpose(snake_case__ , perm=(0, 3, 1, 2) )
lowercase :Any = tf.transpose(snake_case__ , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
lowercase :int = tuple([tf.transpose(snake_case__ , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=snake_case__ , pooler_output=snake_case__ , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class __magic_name__ ( __UpperCAmelCase ):
__A : Optional[Any] = RegNetConfig
__A : Any = "regnet"
__A : Union[str, Any] = "pixel_values"
@property
def __snake_case ( self : Tuple ):
'''simple docstring'''
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
UpperCAmelCase = R'''
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''
UpperCAmelCase = R'''
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__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 RegNet model outputting raw features without any specific head on top." , __UpperCAmelCase , )
class __magic_name__ ( __UpperCAmelCase ):
def __init__( self : List[Any] , snake_case__ : RegNetConfig , *snake_case__ : Optional[Any] , **snake_case__ : Optional[Any] ):
'''simple docstring'''
super().__init__(snake_case__ , *snake_case__ , **snake_case__ )
lowercase :Optional[Any] = TFRegNetMainLayer(snake_case__ , name='''regnet''' )
@unpack_inputs
@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 __snake_case ( self : Optional[Any] , snake_case__ : tf.Tensor , snake_case__ : Optional[bool] = None , snake_case__ : Optional[bool] = None , snake_case__ : Optional[int]=False , ):
'''simple docstring'''
lowercase :Optional[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase :Optional[int] = return_dict if return_dict is not None else self.config.use_return_dict
lowercase :List[Any] = self.regnet(
pixel_values=snake_case__ , output_hidden_states=snake_case__ , return_dict=snake_case__ , training=snake_case__ , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __UpperCAmelCase , )
class __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ):
def __init__( self : List[str] , snake_case__ : RegNetConfig , *snake_case__ : List[str] , **snake_case__ : Union[str, Any] ):
'''simple docstring'''
super().__init__(snake_case__ , *snake_case__ , **snake_case__ )
lowercase :Tuple = config.num_labels
lowercase :Union[str, Any] = TFRegNetMainLayer(snake_case__ , name='''regnet''' )
# classification head
lowercase :Optional[int] = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='''classifier.1''' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@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 __snake_case ( self : Any , snake_case__ : tf.Tensor = None , snake_case__ : tf.Tensor = None , snake_case__ : bool = None , snake_case__ : bool = None , snake_case__ : Union[str, Any]=False , ):
'''simple docstring'''
lowercase :List[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase :Any = return_dict if return_dict is not None else self.config.use_return_dict
lowercase :Any = self.regnet(
snake_case__ , output_hidden_states=snake_case__ , return_dict=snake_case__ , training=snake_case__ )
lowercase :int = outputs.pooler_output if return_dict else outputs[1]
lowercase :Any = self.classifier[0](snake_case__ )
lowercase :Union[str, Any] = self.classifier[1](snake_case__ )
lowercase :int = None if labels is None else self.hf_compute_loss(labels=snake_case__ , logits=snake_case__ )
if not return_dict:
lowercase :int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=snake_case__ , logits=snake_case__ , hidden_states=outputs.hidden_states )
| 172 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase (a_ :Optional[int] , a_ :Union[str, Any] , a_ :Optional[Any]=None) -> List[Any]:
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
lowercase :int = nn.Parameter(a_)
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
lowercase :Tuple = nn.Parameter(a_)
def lowerCamelCase (a_ :int , a_ :Any , a_ :Optional[int]) -> List[Any]:
# set torch weights for 1-to-1 comparison
lowercase :str = np.asarray(weights[0])
lowercase :List[Any] = np.asarray(weights[1])
lowercase :Optional[int] = np.asarray(weights[2])
set_param(
torch_layer.self_attention.query_key , torch.tensor(a_).transpose(1 , 2).contiguous().view(-1 , a_) , )
set_param(
torch_layer.self_attention.value , torch.tensor(a_).transpose(1 , 2).contiguous().view(-1 , a_) , )
set_param(
torch_layer.output.dense , torch.tensor(a_).view(-1 , a_).contiguous().transpose(0 , 1) , )
def lowerCamelCase (a_ :str , a_ :Any , a_ :Union[str, Any]) -> Dict:
# set torch weights for 1-to-1 comparison
lowercase :str = np.asarray(weights[0])
lowercase :Dict = np.asarray(weights[1])
lowercase :Dict = np.asarray(weights[2])
lowercase :Optional[Any] = np.asarray(weights[3])
set_param(
torch_layer.self_attention.query , torch.tensor(a_).transpose(1 , 2).contiguous().view(-1 , a_) , )
set_param(
torch_layer.self_attention.key , torch.tensor(a_).transpose(1 , 2).contiguous().view(-1 , a_) , )
set_param(
torch_layer.self_attention.value , torch.tensor(a_).transpose(1 , 2).contiguous().view(-1 , a_) , )
set_param(
torch_layer.output.dense , torch.tensor(a_).view(-1 , a_).contiguous().transpose(0 , 1) , )
def lowerCamelCase (a_ :Union[str, Any] , a_ :Dict , a_ :Optional[int]) -> Optional[Any]:
# layernorm 1
lowercase :Optional[int] = weights[0][0][0]
lowercase :Union[str, Any] = np.asarray(layer_norm_a[0])
lowercase :List[str] = np.asarray(layer_norm_a[1])
set_param(
torch_block.attention.layer_norm , torch.tensor(a_) , torch.tensor(a_) , )
# lsh weights + output
lowercase :Optional[Any] = weights[0][1]
if len(a_) < 4:
set_layer_weights_in_torch_lsh(a_ , torch_block.attention , a_)
else:
set_layer_weights_in_torch_local(a_ , torch_block.attention , a_)
# intermediate weighs
lowercase :Optional[int] = weights[2][0][1][2]
# Chunked Feed Forward
if len(a_) == 4:
lowercase :int = intermediate_weights[2]
# layernorm 2
lowercase :int = np.asarray(intermediate_weights[0][0])
lowercase :Union[str, Any] = np.asarray(intermediate_weights[0][1])
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(a_) , torch.tensor(a_) , )
# intermediate dense
lowercase :Dict = np.asarray(intermediate_weights[1][0])
lowercase :Optional[Any] = np.asarray(intermediate_weights[1][1])
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(a_).transpose(0 , 1).contiguous() , torch.tensor(a_) , )
# intermediate out
lowercase :Union[str, Any] = np.asarray(intermediate_weights[4][0])
lowercase :Tuple = np.asarray(intermediate_weights[4][1])
set_param(
torch_block.feed_forward.output.dense , torch.tensor(a_).transpose(0 , 1).contiguous() , torch.tensor(a_) , )
def lowerCamelCase (a_ :Tuple , a_ :Dict , a_ :Tuple) -> str:
# reformer model
lowercase :Union[str, Any] = torch_model.reformer
# word embeds
lowercase :Tuple = np.asarray(weights[1])
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(a_) , )
if isinstance(weights[3] , a_):
lowercase :str = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights)):
lowercase :List[str] = np.asarray(weights[3][emb_idx][0])
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F"""{position_embeddings[emb_idx]} emb does not match"""
lowercase :int = nn.Parameter(torch.tensor(a_))
lowercase :Dict = weights[5]
assert len(torch_model_reformer.encoder.layers) * 4 == len(
a_), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers):
lowercase :Optional[int] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(a_ , a_ , a_)
# output layer norm
lowercase :Dict = np.asarray(weights[7][0])
lowercase :Optional[Any] = np.asarray(weights[7][1])
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(a_) , torch.tensor(a_) , )
# output embeddings
lowercase :str = np.asarray(weights[9][0])
lowercase :Union[str, Any] = np.asarray(weights[9][1])
set_param(
torch_model.lm_head.decoder , torch.tensor(a_).transpose(0 , 1).contiguous() , torch.tensor(a_) , )
def lowerCamelCase (a_ :Optional[Any] , a_ :List[Any] , a_ :Tuple) -> Union[str, Any]:
# Initialise PyTorch model
lowercase :Optional[Any] = ReformerConfig.from_json_file(a_)
print(F"""Building PyTorch model from configuration: {config}""")
lowercase :Dict = ReformerModelWithLMHead(a_)
with open(a_ , '''rb''') as f:
lowercase :Tuple = pickle.load(a_)['''weights''']
set_model_weights_in_torch(a_ , a_ , config.hidden_size)
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""")
torch.save(model.state_dict() , a_)
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
UpperCAmelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 172 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _lowercase ( lowercase__ ):
__lowerCAmelCase : List[Any] = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def _lowercase ( lowercase__ ):
__lowerCAmelCase : int = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
__lowerCAmelCase : List[str] = s_dict.pop(lowercase__ )
elif "subsample" in key:
__lowerCAmelCase : Any = s_dict.pop(lowercase__ )
def _lowercase ( lowercase__ ):
__lowerCAmelCase, __lowerCAmelCase : List[Any] = emb.weight.shape
__lowerCAmelCase : int = nn.Linear(lowercase__ , lowercase__ , bias=lowercase__ )
__lowerCAmelCase : str = emb.weight.data
return lin_layer
def _lowercase ( lowercase__ , lowercase__ ):
__lowerCAmelCase : Optional[Any] = torch.load(lowercase__ , map_location='''cpu''' )
__lowerCAmelCase : int = mam_aaa['''args''']
__lowerCAmelCase : Optional[Any] = mam_aaa['''model''']
__lowerCAmelCase : List[str] = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(lowercase__ )
rename_keys(lowercase__ )
__lowerCAmelCase : Any = state_dict['''decoder.embed_tokens.weight'''].shape[0]
__lowerCAmelCase : List[str] = args.share_decoder_input_output_embed
__lowerCAmelCase : Any = [int(lowercase__ ) for i in args.conv_kernel_sizes.split(''',''' )]
__lowerCAmelCase : Union[str, Any] = SpeechaTextConfig(
vocab_size=lowercase__ , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(lowercase__ ) , conv_channels=args.conv_channels , conv_kernel_sizes=lowercase__ , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=lowercase__ , num_beams=5 , max_length=2_0_0 , use_cache=lowercase__ , decoder_start_token_id=2 , early_stopping=lowercase__ , )
__lowerCAmelCase : Tuple = SpeechaTextForConditionalGeneration(lowercase__ )
__lowerCAmelCase, __lowerCAmelCase : Tuple = model.model.load_state_dict(lowercase__ , strict=lowercase__ )
if len(lowercase__ ) > 0 and not set(lowercase__ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f""" but all the following weights are missing {missing}""" )
if tie_embeds:
__lowerCAmelCase : int = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
__lowerCAmelCase : str = lm_head_weights
model.save_pretrained(lowercase__ )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
_UpperCamelCase = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 275 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {"vocab_file": "spiece.model"}
_UpperCamelCase = {
"vocab_file": {
"AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
}
}
_UpperCamelCase = {
"AI-Sweden/gpt-sw3-126m": 2048,
"AI-Sweden/gpt-sw3-350m": 2048,
"AI-Sweden/gpt-sw3-1.6b": 2048,
"AI-Sweden/gpt-sw3-6.7b": 2048,
"AI-Sweden/gpt-sw3-20b": 2048,
}
class __lowercase (_UpperCAmelCase ):
_UpperCamelCase = VOCAB_FILES_NAMES
_UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase = ["""input_ids""", """attention_mask"""]
def __init__( self , A_ , A_=False , A_=False , A_=False , A_=None , A_=None , A_=None , A_=None , A_ = None , **A_ , ) ->None:
'''simple docstring'''
__lowerCAmelCase : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
__lowerCAmelCase : int = kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
__lowerCAmelCase : Union[str, Any] = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__lowerCAmelCase : str = '''<|endoftext|>''' if eos_token is None else eos_token
__lowerCAmelCase : Any = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__lowerCAmelCase : Dict = unk_token if pad_token is None else pad_token
__lowerCAmelCase : int = eos_token if bos_token is None else bos_token
else:
__lowerCAmelCase : Optional[int] = '''<pad>''' if pad_token is None else pad_token
__lowerCAmelCase : List[str] = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=A_ , remove_space=A_ , keep_accents=A_ , bos_token=A_ , eos_token=A_ , unk_token=A_ , pad_token=A_ , sp_model_kwargs=self.sp_model_kwargs , **A_ , )
__lowerCAmelCase : Union[str, Any] = do_lower_case
__lowerCAmelCase : Union[str, Any] = remove_space
__lowerCAmelCase : int = keep_accents
__lowerCAmelCase : Union[str, Any] = vocab_file
__lowerCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A_ )
# Used for whitespace normalization in input texts
# fmt : off
__lowerCAmelCase : List[Any] = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__lowerCAmelCase : int = re.compile(
f"""[{"".join(map(A_ , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8203] ) )}]""" )
def __getstate__( self ) ->Dict:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = self.__dict__.copy()
__lowerCAmelCase : List[Any] = None
return state
def __setstate__( self , A_ ) ->Tuple:
'''simple docstring'''
__lowerCAmelCase : int = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__lowerCAmelCase : List[Any] = {}
__lowerCAmelCase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCamelCase__ ( self ) ->int:
'''simple docstring'''
return len(self.sp_model )
def UpperCamelCase__ ( self , A_ ) ->str:
'''simple docstring'''
__lowerCAmelCase : int = self.non_printing_characters_re.sub('''''' , A_ )
# Normalize whitespaces
__lowerCAmelCase : List[str] = ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
__lowerCAmelCase : Tuple = unicodedata.normalize('''NFC''' , A_ )
return text
def UpperCamelCase__ ( self , A_ , **A_ ) ->List[str]:
'''simple docstring'''
__lowerCAmelCase : int = self.preprocess_text(A_ )
return self.sp_model.encode(A_ , out_type=A_ )
def UpperCamelCase__ ( self , A_ ) ->int:
'''simple docstring'''
return self.sp_model.PieceToId(A_ )
def UpperCamelCase__ ( self , A_ ) ->str:
'''simple docstring'''
return self.sp_model.IdToPiece(A_ )
@staticmethod
def UpperCamelCase__ ( A_ ) ->str:
'''simple docstring'''
return out_string
def UpperCamelCase__ ( self , A_ ) ->str:
'''simple docstring'''
__lowerCAmelCase : str = []
__lowerCAmelCase : Tuple = ''''''
__lowerCAmelCase : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A_ ) + token
__lowerCAmelCase : Optional[Any] = True
__lowerCAmelCase : Optional[int] = []
else:
current_sub_tokens.append(A_ )
__lowerCAmelCase : str = False
out_string += self.sp_model.decode(A_ )
return out_string
def UpperCamelCase__ ( self ) ->Dict[str, int]:
'''simple docstring'''
__lowerCAmelCase : str = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase__ ( self , A_ , A_ = None ) ->Tuple[str]:
'''simple docstring'''
if not os.path.isdir(A_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__lowerCAmelCase : Any = 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:
__lowerCAmelCase : Dict = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (out_vocab_file,)
def UpperCamelCase__ ( self , A_ , A_ = False ) ->Union[List[int], List[List[int]], "torch.Tensor"]:
'''simple docstring'''
if isinstance(A_ , A_ ):
__lowerCAmelCase : Optional[Any] = self.preprocess_text(A_ )
__lowerCAmelCase : Dict = self.sp_model.encode(A_ )
else:
__lowerCAmelCase : Dict = [self.preprocess_text(A_ ) for t in text]
__lowerCAmelCase : Optional[int] = self.sp_model.encode(A_ )
if return_tensors is True or return_tensors == "pt":
__lowerCAmelCase : Tuple = torch.tensor(A_ )
return token_ids
def UpperCamelCase__ ( self , A_ ) ->str:
'''simple docstring'''
return self.sp_model.decode(A_ )
def UpperCamelCase__ ( self , A_ ) ->List[int]:
'''simple docstring'''
__lowerCAmelCase : int = [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
__lowerCAmelCase : Any = (
f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(A_ ) + f"""{self.bos_token}Bot:"""
)
return self.encode(text=A_ )
| 275 | 1 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def lowercase__ ( snake_case_ :int , snake_case_ :Union[str, Any]=False ):
try:
__UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
__UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
__UpperCAmelCase = strtobool(snake_case_ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F'''If set, {key} must be yes or no.''' )
return _value
_lowercase : Optional[int] = parse_flag_from_env('RUN_SLOW', default=False)
_lowercase : str = parse_flag_from_env('RUN_REMOTE', default=False)
_lowercase : Dict = parse_flag_from_env('RUN_LOCAL', default=True)
_lowercase : Optional[int] = parse_flag_from_env('RUN_PACKAGED', default=True)
# Compression
_lowercase : str = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4')
_lowercase : str = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr')
_lowercase : str = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard')
# Audio
_lowercase : Dict = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'),
reason='test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ',
)
# Beam
_lowercase : int = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'),
reason='test requires apache-beam and a compatible dill version',
)
# Dill-cloudpickle compatibility
_lowercase : Union[str, Any] = pytest.mark.skipif(
config.DILL_VERSION <= version.parse('0.3.2'),
reason='test requires dill>0.3.2 for cloudpickle compatibility',
)
# Windows
_lowercase : int = pytest.mark.skipif(
sys.platform == 'win32',
reason='test should not be run on Windows',
)
def lowercase__ ( snake_case_ :List[str] ):
try:
import faiss # noqa
except ImportError:
__UpperCAmelCase = unittest.skip('''test requires faiss''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :str ):
try:
import regex # noqa
except ImportError:
__UpperCAmelCase = unittest.skip('''test requires regex''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :str ):
try:
import elasticsearch # noqa
except ImportError:
__UpperCAmelCase = unittest.skip('''test requires elasticsearch''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :Optional[Any] ):
try:
import sqlalchemy # noqa
except ImportError:
__UpperCAmelCase = unittest.skip('''test requires sqlalchemy''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :Optional[Any] ):
if not config.TORCH_AVAILABLE:
__UpperCAmelCase = unittest.skip('''test requires PyTorch''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :str ):
if not config.TF_AVAILABLE:
__UpperCAmelCase = unittest.skip('''test requires TensorFlow''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :List[Any] ):
if not config.JAX_AVAILABLE:
__UpperCAmelCase = unittest.skip('''test requires JAX''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :Any ):
if not config.PIL_AVAILABLE:
__UpperCAmelCase = unittest.skip('''test requires Pillow''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :int ):
try:
import transformers # noqa F401
except ImportError:
return unittest.skip('''test requires transformers''' )(snake_case_ )
else:
return test_case
def lowercase__ ( snake_case_ :List[Any] ):
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip('''test requires tiktoken''' )(snake_case_ )
else:
return test_case
def lowercase__ ( snake_case_ :int ):
try:
import spacy # noqa F401
except ImportError:
return unittest.skip('''test requires spacy''' )(snake_case_ )
else:
return test_case
def lowercase__ ( snake_case_ :str ):
def _require_spacy_model(snake_case_ :Optional[int] ):
try:
import spacy # noqa F401
spacy.load(snake_case_ )
except ImportError:
return unittest.skip('''test requires spacy''' )(snake_case_ )
except OSError:
return unittest.skip('''test requires spacy model \'{}\''''.format(snake_case_ ) )(snake_case_ )
else:
return test_case
return _require_spacy_model
def lowercase__ ( snake_case_ :Any ):
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip('''test requires pyspark''' )(snake_case_ )
else:
return test_case
def lowercase__ ( snake_case_ :Dict ):
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip('''test requires joblibspark''' )(snake_case_ )
else:
return test_case
def lowercase__ ( snake_case_ :Optional[Any] ):
if not _run_slow_tests or _run_slow_tests == 0:
__UpperCAmelCase = unittest.skip('''test is slow''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :int ):
if not _run_local_tests or _run_local_tests == 0:
__UpperCAmelCase = unittest.skip('''test is local''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :int ):
if not _run_packaged_tests or _run_packaged_tests == 0:
__UpperCAmelCase = unittest.skip('''test is packaged''' )(snake_case_ )
return test_case
def lowercase__ ( snake_case_ :Any ):
if not _run_remote_tests or _run_remote_tests == 0:
__UpperCAmelCase = unittest.skip('''test requires remote''' )(snake_case_ )
return test_case
def lowercase__ ( *snake_case_ :Tuple ):
def decorate(cls :Optional[Any] ):
for name, fn in cls.__dict__.items():
if callable(snake_case_ ) and name.startswith('''test''' ):
for decorator in decorators:
__UpperCAmelCase = decorator(snake_case_ )
setattr(cls , snake_case_ , snake_case_ )
return cls
return decorate
class _UpperCAmelCase ( _lowerCAmelCase ):
pass
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : int = 0
a__ : Union[str, Any] = 1
a__ : List[str] = 2
@contextmanager
def lowercase__ ( snake_case_ :str=OfflineSimulationMode.CONNECTION_FAILS , snake_case_ :List[str]=1E-16 ):
__UpperCAmelCase = requests.Session().request
def timeout_request(snake_case_ :Any , snake_case_ :Any , snake_case_ :Tuple , **snake_case_ :Union[str, Any] ):
# Change the url to an invalid url so that the connection hangs
__UpperCAmelCase = '''https://10.255.255.1'''
if kwargs.get('''timeout''' ) is None:
raise RequestWouldHangIndefinitelyError(
F'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.''' )
__UpperCAmelCase = timeout
try:
return online_request(snake_case_ , snake_case_ , **snake_case_ )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
__UpperCAmelCase = url
__UpperCAmelCase = e.args[0]
__UpperCAmelCase = (max_retry_error.args[0].replace('''10.255.255.1''' , F'''OfflineMock[{url}]''' ),)
__UpperCAmelCase = (max_retry_error,)
raise
def raise_connection_error(snake_case_ :int , snake_case_ :Union[str, Any] , **snake_case_ :str ):
raise requests.ConnectionError('''Offline mode is enabled.''' , request=snake_case_ )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch('''requests.Session.send''' , snake_case_ ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch('''requests.Session.request''' , snake_case_ ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch('''datasets.config.HF_DATASETS_OFFLINE''' , snake_case_ ):
yield
else:
raise ValueError('''Please use a value from the OfflineSimulationMode enum.''' )
@contextmanager
def lowercase__ ( *snake_case_ :Tuple , **snake_case_ :Optional[Any] ):
__UpperCAmelCase = str(Path().resolve() )
with tempfile.TemporaryDirectory(*snake_case_ , **snake_case_ ) as tmp_dir:
try:
os.chdir(snake_case_ )
yield
finally:
os.chdir(snake_case_ )
@contextmanager
def lowercase__ ( ):
import gc
gc.collect()
__UpperCAmelCase = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def lowercase__ ( ):
import gc
gc.collect()
__UpperCAmelCase = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def lowercase__ ( snake_case_ :int , snake_case_ :Tuple ):
return deepcopy(snake_case_ ).integers(0 , 100 , 10 ).tolist() == deepcopy(snake_case_ ).integers(0 , 100 , 10 ).tolist()
def lowercase__ ( snake_case_ :str ):
import decorator
from requests.exceptions import HTTPError
def _wrapper(snake_case_ :List[Any] , *snake_case_ :int , **snake_case_ :Optional[int] ):
try:
return func(*snake_case_ , **snake_case_ )
except HTTPError as err:
if str(snake_case_ ).startswith('''500''' ) or str(snake_case_ ).startswith('''502''' ):
pytest.xfail(str(snake_case_ ) )
raise err
return decorator.decorator(_wrapper , snake_case_ )
class _UpperCAmelCase :
def __init__( self : Optional[Any] , _lowercase : str , _lowercase : Dict , _lowercase : str ):
__UpperCAmelCase = returncode
__UpperCAmelCase = stdout
__UpperCAmelCase = stderr
async def lowercase__ ( snake_case_ :Optional[Any] , snake_case_ :Optional[int] ):
while True:
__UpperCAmelCase = await stream.readline()
if line:
callback(snake_case_ )
else:
break
async def lowercase__ ( snake_case_ :Union[str, Any] , snake_case_ :Dict=None , snake_case_ :Tuple=None , snake_case_ :Optional[int]=None , snake_case_ :str=False , snake_case_ :Optional[Any]=False ):
if echo:
print('''\nRunning: ''' , ''' '''.join(snake_case_ ) )
__UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=snake_case_ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=snake_case_ , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
__UpperCAmelCase = []
__UpperCAmelCase = []
def tee(snake_case_ :Optional[int] , snake_case_ :List[Any] , snake_case_ :Tuple , snake_case_ :List[str]="" ):
__UpperCAmelCase = line.decode('''utf-8''' ).rstrip()
sink.append(snake_case_ )
if not quiet:
print(snake_case_ , snake_case_ , file=snake_case_ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda snake_case_ : tee(snake_case_ , snake_case_ , sys.stdout , label='''stdout:''' ) ),
_read_stream(p.stderr , lambda snake_case_ : tee(snake_case_ , snake_case_ , sys.stderr , label='''stderr:''' ) ),
] , timeout=snake_case_ , )
return _RunOutput(await p.wait() , snake_case_ , snake_case_ )
def lowercase__ ( snake_case_ :str , snake_case_ :Optional[Any]=None , snake_case_ :Any=None , snake_case_ :Any=180 , snake_case_ :Optional[int]=False , snake_case_ :int=True ):
__UpperCAmelCase = asyncio.get_event_loop()
__UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(snake_case_ , env=snake_case_ , stdin=snake_case_ , timeout=snake_case_ , quiet=snake_case_ , echo=snake_case_ ) )
__UpperCAmelCase = ''' '''.join(snake_case_ )
if result.returncode > 0:
__UpperCAmelCase = '''\n'''.join(result.stderr )
raise RuntimeError(
F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
F'''The combined stderr from workers follows:\n{stderr}''' )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(F'''\'{cmd_str}\' produced no output.''' )
return result
def lowercase__ ( ):
__UpperCAmelCase = os.environ.get('''PYTEST_XDIST_WORKER''' , '''gw0''' )
__UpperCAmelCase = re.sub(r'''^gw''' , '''''' , snake_case_ , 0 , re.M )
return int(snake_case_ )
def lowercase__ ( ):
__UpperCAmelCase = 29_500
__UpperCAmelCase = pytest_xdist_worker_id()
return port + uniq_delta
| 86 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 86 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
__UpperCamelCase = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ : str = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
SCREAMING_SNAKE_CASE_ : int = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
SCREAMING_SNAKE_CASE_ : List[Any] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def __A ( self ) -> Tuple:
SCREAMING_SNAKE_CASE = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='pt' )
SCREAMING_SNAKE_CASE = text_classifier('This is great !' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
SCREAMING_SNAKE_CASE = text_classifier('This is great !' , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}] )
SCREAMING_SNAKE_CASE = text_classifier(['This is great !', 'This is bad'] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , [
[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
] , )
SCREAMING_SNAKE_CASE = text_classifier('This is great !' , top_k=1 )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
# Legacy behavior
SCREAMING_SNAKE_CASE = text_classifier('This is great !' , return_all_scores=lowerCAmelCase__ )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
SCREAMING_SNAKE_CASE = text_classifier('This is great !' , return_all_scores=lowerCAmelCase__ )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , [[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]] )
SCREAMING_SNAKE_CASE = text_classifier(['This is great !', 'Something else'] , return_all_scores=lowerCAmelCase__ )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , [
[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
] , )
SCREAMING_SNAKE_CASE = text_classifier(['This is great !', 'Something else'] , return_all_scores=lowerCAmelCase__ )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , [
{'label': 'LABEL_0', 'score': 0.5_04},
{'label': 'LABEL_0', 'score': 0.5_04},
] , )
@require_torch
def __A ( self ) -> List[Any]:
import torch
SCREAMING_SNAKE_CASE = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='pt' , device=torch.device('cpu' ) , )
SCREAMING_SNAKE_CASE = text_classifier('This is great !' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
@require_tf
def __A ( self ) -> Any:
SCREAMING_SNAKE_CASE = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='tf' )
SCREAMING_SNAKE_CASE = text_classifier('This is great !' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
@slow
@require_torch
def __A ( self ) -> Dict:
SCREAMING_SNAKE_CASE = pipeline('text-classification' )
SCREAMING_SNAKE_CASE = text_classifier('This is great !' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'POSITIVE', 'score': 1.0}] )
SCREAMING_SNAKE_CASE = text_classifier('This is bad !' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'NEGATIVE', 'score': 1.0}] )
SCREAMING_SNAKE_CASE = text_classifier('Birds are a type of animal' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'POSITIVE', 'score': 0.9_88}] )
@slow
@require_tf
def __A ( self ) -> Any:
SCREAMING_SNAKE_CASE = pipeline('text-classification' , framework='tf' )
SCREAMING_SNAKE_CASE = text_classifier('This is great !' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'POSITIVE', 'score': 1.0}] )
SCREAMING_SNAKE_CASE = text_classifier('This is bad !' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'NEGATIVE', 'score': 1.0}] )
SCREAMING_SNAKE_CASE = text_classifier('Birds are a type of animal' )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': 'POSITIVE', 'score': 0.9_88}] )
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
SCREAMING_SNAKE_CASE = TextClassificationPipeline(model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ )
return text_classifier, ["HuggingFace is in", "This is another test"]
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
SCREAMING_SNAKE_CASE = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
SCREAMING_SNAKE_CASE = 'HuggingFace is in'
SCREAMING_SNAKE_CASE = text_classifier(lowerCAmelCase__ )
self.assertEqual(nested_simplify(lowerCAmelCase__ ) , [{'label': ANY(lowerCAmelCase__ ), 'score': ANY(lowerCAmelCase__ )}] )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
SCREAMING_SNAKE_CASE = ['HuggingFace is in ', 'Paris is in France']
SCREAMING_SNAKE_CASE = text_classifier(lowerCAmelCase__ )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , [{'label': ANY(lowerCAmelCase__ ), 'score': ANY(lowerCAmelCase__ )}, {'label': ANY(lowerCAmelCase__ ), 'score': ANY(lowerCAmelCase__ )}] , )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
self.assertTrue(outputs[1]['label'] in model.config.idalabel.values() )
# Forcing to get all results with `top_k=None`
# This is NOT the legacy format
SCREAMING_SNAKE_CASE = text_classifier(lowerCAmelCase__ , top_k=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , [[{'label': ANY(lowerCAmelCase__ ), 'score': ANY(lowerCAmelCase__ )}] * N, [{'label': ANY(lowerCAmelCase__ ), 'score': ANY(lowerCAmelCase__ )}] * N] , )
SCREAMING_SNAKE_CASE = {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
SCREAMING_SNAKE_CASE = text_classifier(lowerCAmelCase__ )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , {'label': ANY(lowerCAmelCase__ ), 'score': ANY(lowerCAmelCase__ )} , )
self.assertTrue(outputs['label'] in model.config.idalabel.values() )
# This might be used a text pair, but tokenizer + pipe interaction
# makes it hard to understand that it's not using the pair properly
# https://github.com/huggingface/transformers/issues/17305
# We disabled this usage instead as it was outputting wrong outputs.
SCREAMING_SNAKE_CASE = [['HuggingFace is in ', 'Paris is in France']]
with self.assertRaises(lowerCAmelCase__ ):
text_classifier(lowerCAmelCase__ )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
SCREAMING_SNAKE_CASE = text_classifier([[['HuggingFace is in ', 'Paris is in France']]] )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , [{'label': ANY(lowerCAmelCase__ ), 'score': ANY(lowerCAmelCase__ )}] , )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
| 113 |
"""simple docstring"""
from math import isclose, sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ) -> tuple[float, float, float]:
SCREAMING_SNAKE_CASE = point_y / 4 / point_x
SCREAMING_SNAKE_CASE = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
SCREAMING_SNAKE_CASE = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
SCREAMING_SNAKE_CASE = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
SCREAMING_SNAKE_CASE = outgoing_gradient**2 + 4
SCREAMING_SNAKE_CASE = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
SCREAMING_SNAKE_CASE = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
SCREAMING_SNAKE_CASE = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
SCREAMING_SNAKE_CASE = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
SCREAMING_SNAKE_CASE = x_minus if isclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else x_plus
SCREAMING_SNAKE_CASE = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def lowercase (SCREAMING_SNAKE_CASE_ : float = 1.4 , SCREAMING_SNAKE_CASE_ : float = -9.6 ) -> int:
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = first_x_coord
SCREAMING_SNAKE_CASE = first_y_coord
SCREAMING_SNAKE_CASE = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = next_point(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 113 | 1 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowerCamelCase (a_ :list[int] , a_ :list[int] , a_ :int) -> list[int]:
lowercase :Dict = [0] * no_of_processes
lowercase :List[Any] = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(_snake_case):
lowercase :Any = burst_time[i]
lowercase :list[int] = []
lowercase :List[Any] = 0
lowercase :Any = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
lowercase :List[str] = []
lowercase :List[str] = -1
for i in range(_snake_case):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(_snake_case)
if len(_snake_case) > 0:
lowercase :List[Any] = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
lowercase :Union[str, Any] = i
total_time += burst_time[target_process]
completed += 1
lowercase :str = 0
lowercase :List[str] = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def lowerCamelCase (a_ :list[int] , a_ :int , a_ :list[int]) -> list[int]:
lowercase :int = [0] * no_of_processes
for i in range(_snake_case):
lowercase :int = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print('''[TEST CASE 01]''')
UpperCAmelCase = 4
UpperCAmelCase = [2, 5, 3, 7]
UpperCAmelCase = [0, 0, 0, 0]
UpperCAmelCase = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
UpperCAmelCase = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
for i, process_id in enumerate(list(range(1, 5))):
print(
F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
)
print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")
| 369 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCAmelCase = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 172 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A_ ( __UpperCAmelCase ):
_UpperCAmelCase : Any = '''naver-clova-ix/donut-base-finetuned-docvqa'''
_UpperCAmelCase : Any = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
_UpperCAmelCase : Optional[int] = '''document_qa'''
_UpperCAmelCase : List[str] = AutoProcessor
_UpperCAmelCase : Optional[Any] = VisionEncoderDecoderModel
_UpperCAmelCase : Optional[Any] = ['''image''', '''text''']
_UpperCAmelCase : int = ['''text''']
def __init__( self : str ,*SCREAMING_SNAKE_CASE__ : Optional[Any] ,**SCREAMING_SNAKE_CASE__ : str):
if not is_vision_available():
raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.')
super().__init__(*UpperCamelCase__ ,**UpperCamelCase__)
def lowerCAmelCase ( self : List[str] ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Optional[int]):
__lowerCamelCase : List[Any] = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
__lowerCamelCase : Union[str, Any] = task_prompt.replace('{user_input}' ,UpperCamelCase__)
__lowerCamelCase : int = self.pre_processor.tokenizer(
UpperCamelCase__ ,add_special_tokens=UpperCamelCase__ ,return_tensors='pt').input_ids
__lowerCamelCase : Tuple = self.pre_processor(UpperCamelCase__ ,return_tensors='pt').pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def lowerCAmelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Union[str, Any]):
return self.model.generate(
inputs['pixel_values'].to(self.device) ,decoder_input_ids=inputs['decoder_input_ids'].to(self.device) ,max_length=self.model.decoder.config.max_position_embeddings ,early_stopping=UpperCamelCase__ ,pad_token_id=self.pre_processor.tokenizer.pad_token_id ,eos_token_id=self.pre_processor.tokenizer.eos_token_id ,use_cache=UpperCamelCase__ ,num_beams=1 ,bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] ,return_dict_in_generate=UpperCamelCase__ ,).sequences
def lowerCAmelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : Any):
__lowerCamelCase : Any = self.pre_processor.batch_decode(UpperCamelCase__)[0]
__lowerCamelCase : Union[str, Any] = sequence.replace(self.pre_processor.tokenizer.eos_token ,'')
__lowerCamelCase : str = sequence.replace(self.pre_processor.tokenizer.pad_token ,'')
__lowerCamelCase : Tuple = re.sub(R'<.*?>' ,'' ,UpperCamelCase__ ,count=1).strip() # remove first task start token
__lowerCamelCase : Any = self.pre_processor.tokenajson(UpperCamelCase__)
return sequence["answer"]
| 73 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = checkpoints.load_tax_checkpoint(_A )
lowerCAmelCase_ = flatten_dict(_A )
return flax_params
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = {}
lowerCAmelCase_ = {
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCAmelCase_ = {
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCAmelCase_ = '''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCAmelCase_ = new_key.replace(_A , _A )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCAmelCase_ = new_key.replace(_A , _A )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCAmelCase_ = re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , _A )
lowerCAmelCase_ = new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCAmelCase_ = re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , _A )
lowerCAmelCase_ = flax_dict[key]
lowerCAmelCase_ = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCAmelCase_ = torch.from_numpy(converted_dict[key].T )
else:
lowerCAmelCase_ = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def __UpperCamelCase ( _A , _A , _A=False , _A=False ):
lowerCAmelCase_ = get_flax_param(_A )
if not use_large:
lowerCAmelCase_ = PixaStructVisionConfig()
lowerCAmelCase_ = PixaStructTextConfig()
else:
lowerCAmelCase_ = PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowerCAmelCase_ = PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowerCAmelCase_ = PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=_A )
lowerCAmelCase_ = PixaStructForConditionalGeneration(_A )
lowerCAmelCase_ = rename_and_convert_flax_params(_A )
model.load_state_dict(_A )
lowerCAmelCase_ = AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCAmelCase_ = PixaStructImageProcessor()
lowerCAmelCase_ = PixaStructProcessor(image_processor=_A , tokenizer=_A )
if use_large:
lowerCAmelCase_ = 4096
lowerCAmelCase_ = True
# mkdir if needed
os.makedirs(_A , exist_ok=_A )
model.save_pretrained(_A )
processor.save_pretrained(_A )
print('''Model saved in {}'''.format(_A ) )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
_A = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 278 | 0 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def _lowercase ( __snake_case ) -> Optional[int]:
__lowerCAmelCase : str = np.inf
def set_batch_size(__snake_case ) -> None:
nonlocal batch_size
if isinstance(__snake_case ,__snake_case ):
__lowerCAmelCase : Any = min(__snake_case ,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(__snake_case ,__snake_case ):
__lowerCAmelCase : Union[str, Any] = min(__snake_case ,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(__snake_case ,__snake_case ) and feature.dtype == "binary":
__lowerCAmelCase : str = min(__snake_case ,config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(__snake_case ,__snake_case )
return None if batch_size is np.inf else batch_size
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: int , _SCREAMING_SNAKE_CASE: NestedDataStructureLike[PathLike] , _SCREAMING_SNAKE_CASE: Optional[NamedSplit] = None , _SCREAMING_SNAKE_CASE: Optional[Features] = None , _SCREAMING_SNAKE_CASE: str = None , _SCREAMING_SNAKE_CASE: bool = False , _SCREAMING_SNAKE_CASE: bool = False , _SCREAMING_SNAKE_CASE: Optional[int] = None , **_SCREAMING_SNAKE_CASE: List[str] , ) -> str:
"""simple docstring"""
super().__init__(
_SCREAMING_SNAKE_CASE , split=_SCREAMING_SNAKE_CASE , features=_SCREAMING_SNAKE_CASE , cache_dir=_SCREAMING_SNAKE_CASE , keep_in_memory=_SCREAMING_SNAKE_CASE , streaming=_SCREAMING_SNAKE_CASE , num_proc=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Union[str, Any] = path_or_paths if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
__lowerCAmelCase : int = _PACKAGED_DATASETS_MODULES["parquet"][1]
__lowerCAmelCase : Any = Parquet(
cache_dir=_SCREAMING_SNAKE_CASE , data_files=_SCREAMING_SNAKE_CASE , features=_SCREAMING_SNAKE_CASE , hash=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
def _SCREAMING_SNAKE_CASE ( self: int) -> int:
"""simple docstring"""
if self.streaming:
__lowerCAmelCase : int = self.builder.as_streaming_dataset(split=self.split)
# Build regular (map-style) dataset
else:
__lowerCAmelCase : str = None
__lowerCAmelCase : Any = None
__lowerCAmelCase : List[Any] = None
__lowerCAmelCase : Optional[int] = None
self.builder.download_and_prepare(
download_config=_SCREAMING_SNAKE_CASE , download_mode=_SCREAMING_SNAKE_CASE , verification_mode=_SCREAMING_SNAKE_CASE , base_path=_SCREAMING_SNAKE_CASE , num_proc=self.num_proc , )
__lowerCAmelCase : List[str] = self.builder.as_dataset(
split=self.split , verification_mode=_SCREAMING_SNAKE_CASE , in_memory=self.keep_in_memory)
return dataset
class A__ :
'''simple docstring'''
def __init__( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Dataset , _SCREAMING_SNAKE_CASE: Union[PathLike, BinaryIO] , _SCREAMING_SNAKE_CASE: Optional[int] = None , **_SCREAMING_SNAKE_CASE: Tuple , ) -> int:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = dataset
__lowerCAmelCase : Optional[int] = path_or_buf
__lowerCAmelCase : int = batch_size or get_writer_batch_size(dataset.features)
__lowerCAmelCase : str = parquet_writer_kwargs
def _SCREAMING_SNAKE_CASE ( self: str) -> int:
"""simple docstring"""
__lowerCAmelCase : int = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike)):
with open(self.path_or_buf , "wb+") as buffer:
__lowerCAmelCase : int = self._write(file_obj=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , **self.parquet_writer_kwargs)
else:
__lowerCAmelCase : List[Any] = self._write(file_obj=self.path_or_buf , batch_size=_SCREAMING_SNAKE_CASE , **self.parquet_writer_kwargs)
return written
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: BinaryIO , _SCREAMING_SNAKE_CASE: int , **_SCREAMING_SNAKE_CASE: Optional[Any]) -> int:
"""simple docstring"""
__lowerCAmelCase : str = 0
__lowerCAmelCase : Dict = parquet_writer_kwargs.pop("path_or_buf" , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = self.dataset.features.arrow_schema
__lowerCAmelCase : Any = pq.ParquetWriter(_SCREAMING_SNAKE_CASE , schema=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
for offset in logging.tqdm(
range(0 , len(self.dataset) , _SCREAMING_SNAKE_CASE) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
__lowerCAmelCase : List[Any] = query_table(
table=self.dataset._data , key=slice(_SCREAMING_SNAKE_CASE , offset + batch_size) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(_SCREAMING_SNAKE_CASE)
written += batch.nbytes
writer.close()
return written | 58 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __lt__( self: List[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any]) -> Dict:
"""simple docstring"""
return self[-1] < other[-1]
def __eq__( self: int , _SCREAMING_SNAKE_CASE: Any) -> Tuple:
"""simple docstring"""
return self[-1] == other[-1]
def _lowercase ( __snake_case ) -> list:
__lowerCAmelCase : list[Stack] = []
# sort into stacks
for element in collection:
__lowerCAmelCase : Dict = Stack([element] )
__lowerCAmelCase : str = bisect_left(__snake_case ,__snake_case )
if i != len(__snake_case ):
stacks[i].append(__snake_case )
else:
stacks.append(__snake_case )
# use a heap-based merge to merge stack efficiently
__lowerCAmelCase : Union[str, Any] = merge(*(reversed(__snake_case ) for stack in stacks) )
return collection
if __name__ == "__main__":
__snake_case : Union[str, Any] = input('Enter numbers separated by a comma:\n').strip()
__snake_case : Optional[int] = [int(item) for item in user_input.split(',')]
print(patience_sort(unsorted)) | 58 | 1 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : int = {
"""google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}
class UpperCamelCase_ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase__ = "efficientnet"
def __init__( self : Optional[Any] , UpperCAmelCase__ : int = 3 , UpperCAmelCase__ : int = 600 , UpperCAmelCase__ : float = 2.0 , UpperCAmelCase__ : float = 3.1 , UpperCAmelCase__ : int = 8 , UpperCAmelCase__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , UpperCAmelCase__ : List[int] = [32, 16, 24, 40, 80, 112, 192] , UpperCAmelCase__ : List[int] = [16, 24, 40, 80, 112, 192, 320] , UpperCAmelCase__ : List[int] = [] , UpperCAmelCase__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , UpperCAmelCase__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , UpperCAmelCase__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , UpperCAmelCase__ : float = 0.25 , UpperCAmelCase__ : str = "swish" , UpperCAmelCase__ : int = 2_560 , UpperCAmelCase__ : str = "mean" , UpperCAmelCase__ : float = 0.02 , UpperCAmelCase__ : float = 0.001 , UpperCAmelCase__ : float = 0.99 , UpperCAmelCase__ : float = 0.5 , UpperCAmelCase__ : float = 0.2 , **UpperCAmelCase__ : str , ) ->str:
'''simple docstring'''
super().__init__(**__lowerCAmelCase)
A__ = num_channels
A__ = image_size
A__ = width_coefficient
A__ = depth_coefficient
A__ = depth_divisor
A__ = kernel_sizes
A__ = in_channels
A__ = out_channels
A__ = depthwise_padding
A__ = strides
A__ = num_block_repeats
A__ = expand_ratios
A__ = squeeze_expansion_ratio
A__ = hidden_act
A__ = hidden_dim
A__ = pooling_type
A__ = initializer_range
A__ = batch_norm_eps
A__ = batch_norm_momentum
A__ = dropout_rate
A__ = drop_connect_rate
A__ = sum(__lowerCAmelCase) * 4
class UpperCamelCase_ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase__ = version.parse('''1.11''' )
@property
def SCREAMING_SNAKE_CASE ( self : Dict) ->int:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
])
@property
def SCREAMING_SNAKE_CASE ( self : int) ->Union[str, Any]:
'''simple docstring'''
return 1e-5
| 14 |
"""simple docstring"""
def snake_case_ ( A_ : list[int], A_ : str ):
'''simple docstring'''
_lowerCamelCase : Tuple = int(A_ )
# Initialize Result
_lowerCamelCase : Dict = []
# Traverse through all denomination
for denomination in reversed(A_ ):
# Find denominations
while int(A_ ) >= int(A_ ):
total_value -= int(A_ )
answer.append(A_ ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
lowerCAmelCase__ = []
lowerCAmelCase__ = '''0'''
if (
input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
== "y"
):
lowerCAmelCase__ = int(input('''Enter the number of denominations you want to add: ''').strip())
for i in range(0, n):
denominations.append(int(input(F"""Denomination {i}: """).strip()))
lowerCAmelCase__ = input('''Enter the change you want to make in Indian Currency: ''').strip()
else:
# All denominations of Indian Currency if user does not enter
lowerCAmelCase__ = [1, 2, 5, 10, 20, 50, 100, 500, 2000]
lowerCAmelCase__ = input('''Enter the change you want to make: ''').strip()
if int(value) == 0 or int(value) < 0:
print('''The total value cannot be zero or negative.''')
else:
print(F"""Following is minimal change for {value}: """)
lowerCAmelCase__ = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=''' ''')
| 72 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowercase__ = logging.get_logger(__name__)
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase = """AutoTokenizer"""
lowerCamelCase = ["""tokenizer"""]
lowerCamelCase = {
"""semantic_prompt""": 1,
"""coarse_prompt""": 2,
"""fine_prompt""": 2,
}
def __init__( self : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Any=None ) -> int:
"""simple docstring"""
super().__init__(UpperCamelCase__ )
snake_case : int = speaker_embeddings
@classmethod
def lowerCAmelCase ( cls : int , UpperCamelCase__ : int , UpperCamelCase__ : Any="speaker_embeddings_path.json" , **UpperCamelCase__ : str ) -> int:
"""simple docstring"""
if speaker_embeddings_dict_path is not None:
snake_case : List[Any] = get_file_from_repo(
UpperCamelCase__ , UpperCamelCase__ , subfolder=kwargs.pop('''subfolder''' , UpperCamelCase__ ) , cache_dir=kwargs.pop('''cache_dir''' , UpperCamelCase__ ) , force_download=kwargs.pop('''force_download''' , UpperCamelCase__ ) , proxies=kwargs.pop('''proxies''' , UpperCamelCase__ ) , resume_download=kwargs.pop('''resume_download''' , UpperCamelCase__ ) , local_files_only=kwargs.pop('''local_files_only''' , UpperCamelCase__ ) , use_auth_token=kwargs.pop('''use_auth_token''' , UpperCamelCase__ ) , revision=kwargs.pop('''revision''' , UpperCamelCase__ ) , )
if speaker_embeddings_path is None:
logger.warning(
f'`{os.path.join(UpperCamelCase__ , UpperCamelCase__ )}` does not exists\n , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.' )
snake_case : Tuple = None
else:
with open(UpperCamelCase__ ) as speaker_embeddings_json:
snake_case : Dict = json.load(UpperCamelCase__ )
else:
snake_case : str = None
snake_case : Optional[Any] = AutoTokenizer.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
return cls(tokenizer=UpperCamelCase__ , speaker_embeddings=UpperCamelCase__ )
def lowerCAmelCase ( self : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str="speaker_embeddings_path.json" , UpperCamelCase__ : List[Any]="speaker_embeddings" , UpperCamelCase__ : bool = False , **UpperCamelCase__ : List[str] , ) -> Any:
"""simple docstring"""
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(UpperCamelCase__ , UpperCamelCase__ , '''v2''' ) , exist_ok=UpperCamelCase__ )
snake_case : List[Any] = {}
snake_case : Optional[Any] = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
snake_case : Optional[Any] = self._load_voice_preset(UpperCamelCase__ )
snake_case : Optional[int] = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict['''repo_or_path'''] , UpperCamelCase__ , f'{prompt_key}_{key}' ) , voice_preset[key] , allow_pickle=UpperCamelCase__ , )
snake_case : Dict = os.path.join(UpperCamelCase__ , f'{prompt_key}_{key}.npy' )
snake_case : List[str] = tmp_dict
with open(os.path.join(UpperCamelCase__ , UpperCamelCase__ ) , '''w''' ) as fp:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
super().save_pretrained(UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
def lowerCAmelCase ( self : int , UpperCamelCase__ : str = None , **UpperCamelCase__ : int ) -> Union[str, Any]:
"""simple docstring"""
snake_case : List[str] = self.speaker_embeddings[voice_preset]
snake_case : Union[str, Any] = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].' )
snake_case : Optional[int] = get_file_from_repo(
self.speaker_embeddings.get('''repo_or_path''' , '''/''' ) , voice_preset_paths[key] , subfolder=kwargs.pop('''subfolder''' , UpperCamelCase__ ) , cache_dir=kwargs.pop('''cache_dir''' , UpperCamelCase__ ) , force_download=kwargs.pop('''force_download''' , UpperCamelCase__ ) , proxies=kwargs.pop('''proxies''' , UpperCamelCase__ ) , resume_download=kwargs.pop('''resume_download''' , UpperCamelCase__ ) , local_files_only=kwargs.pop('''local_files_only''' , UpperCamelCase__ ) , use_auth_token=kwargs.pop('''use_auth_token''' , UpperCamelCase__ ) , revision=kwargs.pop('''revision''' , UpperCamelCase__ ) , )
if path is None:
raise ValueError(
f'`{os.path.join(self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] )}` does not exists\n , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n embeddings.' )
snake_case : Tuple = np.load(UpperCamelCase__ )
return voice_preset_dict
def lowerCAmelCase ( self : Tuple , UpperCamelCase__ : Optional[dict] = None ) -> str:
"""simple docstring"""
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'Voice preset unrecognized, missing {key} as a key.' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.' )
def __call__( self : int , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : int=None , UpperCamelCase__ : Dict="pt" , UpperCamelCase__ : Union[str, Any]=256 , UpperCamelCase__ : Any=False , UpperCamelCase__ : Dict=True , UpperCamelCase__ : List[Any]=False , **UpperCamelCase__ : Union[str, Any] , ) -> Optional[int]:
"""simple docstring"""
if voice_preset is not None and not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
if (
isinstance(UpperCamelCase__ , UpperCamelCase__ )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
snake_case : str = self._load_voice_preset(UpperCamelCase__ )
else:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ) and not voice_preset.endswith('''.npz''' ):
snake_case : int = voice_preset + '''.npz'''
snake_case : Tuple = np.load(UpperCamelCase__ )
if voice_preset is not None:
self._validate_voice_preset_dict(UpperCamelCase__ , **UpperCamelCase__ )
snake_case : Tuple = BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__ )
snake_case : Union[str, Any] = self.tokenizer(
UpperCamelCase__ , return_tensors=UpperCamelCase__ , padding='''max_length''' , max_length=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
if voice_preset is not None:
snake_case : Optional[int] = voice_preset
return encoded_text
| 83 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = StableDiffusionLDMaDPipeline
lowerCamelCase = TEXT_TO_IMAGE_PARAMS
lowerCamelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
snake_case : str = 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''' , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
torch.manual_seed(0 )
snake_case : int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=6 , out_channels=6 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case : int = CLIPTextModel(UpperCamelCase__ )
snake_case : Tuple = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
snake_case : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowerCAmelCase ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str=0 ) -> Optional[int]:
"""simple docstring"""
if str(UpperCamelCase__ ).startswith('''mps''' ):
snake_case : Optional[int] = torch.manual_seed(UpperCamelCase__ )
else:
snake_case : Optional[int] = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Tuple = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
snake_case : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case : List[Any] = self.get_dummy_components()
snake_case : str = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Union[str, Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : List[str] = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : Tuple = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : int = output.rgb, output.depth
snake_case : str = rgb[0, -3:, -3:, -1]
snake_case : Union[str, Any] = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
snake_case : int = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
snake_case : str = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
snake_case : int = self.get_dummy_components()
snake_case : Any = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Optional[Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : int = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : str = 3 * [inputs['''prompt''']]
# forward
snake_case : Union[str, Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Tuple = rgb_slice_a[0, -3:, -3:, -1]
snake_case : List[str] = depth_slice_a[0, -3:, -1]
snake_case : int = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : Optional[int] = 3 * [inputs.pop('''prompt''' )]
snake_case : Dict = ldmad_pipe.tokenizer(
UpperCamelCase__ , padding='''max_length''' , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='''pt''' , )
snake_case : Optional[Any] = text_inputs['''input_ids'''].to(UpperCamelCase__ )
snake_case : Any = ldmad_pipe.text_encoder(UpperCamelCase__ )[0]
snake_case : Tuple = prompt_embeds
# forward
snake_case : List[Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Dict = output.rgb, output.depth
snake_case : Any = rgb_slice_a[0, -3:, -3:, -1]
snake_case : Dict = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
snake_case : str = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case : Dict = self.get_dummy_components()
snake_case : List[str] = PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
snake_case : Optional[Any] = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Dict = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Dict = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : str = '''french fries'''
snake_case : List[str] = ldmad_pipe(**UpperCamelCase__ , negative_prompt=UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Union[str, Any] = rgb[0, -3:, -3:, -1]
snake_case : int = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
snake_case : Dict = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
snake_case : Any = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any]="cpu" , UpperCamelCase__ : Optional[int]=torch.floataa , UpperCamelCase__ : Union[str, Any]=0 ) -> Any:
"""simple docstring"""
snake_case : Any = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Optional[Any] = np.random.RandomState(UpperCamelCase__ ).standard_normal((1, 4, 64, 64) )
snake_case : Any = torch.from_numpy(UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ )
snake_case : List[Any] = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Dict = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' )
snake_case : Optional[Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Any = self.get_inputs(UpperCamelCase__ )
snake_case : str = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : List[Any] = output.rgb, output.depth
snake_case : Optional[Any] = rgb[0, -3:, -3:, -1].flatten()
snake_case : Tuple = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
snake_case : Optional[Any] = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
snake_case : str = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : Optional[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Any="cpu" , UpperCamelCase__ : Optional[int]=torch.floataa , UpperCamelCase__ : Optional[int]=0 ) -> str:
"""simple docstring"""
snake_case : Dict = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Optional[Any] = np.random.RandomState(UpperCamelCase__ ).standard_normal((1, 4, 64, 64) )
snake_case : int = torch.from_numpy(UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ )
snake_case : List[str] = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 50,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
snake_case : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' ).to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Dict = self.get_inputs(UpperCamelCase__ )
snake_case : str = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Union[str, Any] = 0.495_586
snake_case : Tuple = 0.33_795_515
snake_case : Dict = 112.48_518
snake_case : Optional[int] = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
snake_case : Optional[int] = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c''' ).to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : int = self.get_inputs(UpperCamelCase__ )
snake_case : List[Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Tuple = 0.4_194_127
snake_case : Optional[Any] = 0.35_375_586
snake_case : Any = 0.5_638_502
snake_case : int = 0.34_686_103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 83 | 1 |
"""simple docstring"""
from manim import *
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def snake_case ( self ):
__lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
__lowerCAmelCase = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
__lowerCAmelCase = [mem.copy() for i in range(6 )]
__lowerCAmelCase = [mem.copy() for i in range(6 )]
__lowerCAmelCase = VGroup(*__a ).arrange(__a , buff=0 )
__lowerCAmelCase = VGroup(*__a ).arrange(__a , buff=0 )
__lowerCAmelCase = VGroup(__a , __a ).arrange(__a , buff=0 )
__lowerCAmelCase = Text("CPU" , font_size=24 )
__lowerCAmelCase = Group(__a , __a ).arrange(__a , buff=0.5 , aligned_edge=__a )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__a )
__lowerCAmelCase = [mem.copy() for i in range(4 )]
__lowerCAmelCase = VGroup(*__a ).arrange(__a , buff=0 )
__lowerCAmelCase = Text("GPU" , font_size=24 )
__lowerCAmelCase = Group(__a , __a ).arrange(__a , buff=0.5 , aligned_edge=__a )
gpu.move_to([-1, -1, 0] )
self.add(__a )
__lowerCAmelCase = [mem.copy() for i in range(6 )]
__lowerCAmelCase = VGroup(*__a ).arrange(__a , buff=0 )
__lowerCAmelCase = Text("Model" , font_size=24 )
__lowerCAmelCase = Group(__a , __a ).arrange(__a , buff=0.5 , aligned_edge=__a )
model.move_to([3, -1.0, 0] )
self.add(__a )
__lowerCAmelCase = []
for i, rect in enumerate(__a ):
rect.set_stroke(__a )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
__lowerCAmelCase = Rectangle(height=0.4_6 / 4 , width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(__a , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=__a )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__a , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__a , buff=0.0 )
self.add(__a )
cpu_targs.append(__a )
__lowerCAmelCase = [mem.copy() for i in range(6 )]
__lowerCAmelCase = VGroup(*__a ).arrange(__a , buff=0 )
__lowerCAmelCase = Text("Loaded Checkpoint" , font_size=24 )
__lowerCAmelCase = Group(__a , __a ).arrange(__a , aligned_edge=__a , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
__lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
__lowerCAmelCase = 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(__a , __a )
__lowerCAmelCase = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__a , DOWN * 2.4 , aligned_edge=key_text.get_left() )
__lowerCAmelCase = MarkupText(
f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__a ) , Write(__a ) )
self.play(Write(__a , run_time=1 ) , Create(__a , run_time=1 ) )
__lowerCAmelCase = []
__lowerCAmelCase = []
for i, rect in enumerate(__a ):
__lowerCAmelCase = fill.copy().set_fill(__a , opacity=0.7 )
target.move_to(__a )
first_animations.append(GrowFromCenter(__a , run_time=1 ) )
__lowerCAmelCase = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__a , run_time=1.5 ) )
self.play(*__a )
self.play(*__a )
self.wait()
| 57 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCamelCase_ = logging.get_logger(__name__)
class __A( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ["""pixel_values"""]
def __init__(self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 0.9 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 / 2_55 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = size if size is not None else {"""shortest_edge""": 2_24}
UpperCamelCase__ = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
UpperCamelCase__ = get_size_dict(SCREAMING_SNAKE_CASE_ , param_name="""crop_size""" )
UpperCamelCase__ = do_resize
UpperCamelCase__ = size
UpperCamelCase__ = crop_pct
UpperCamelCase__ = resample
UpperCamelCase__ = do_center_crop
UpperCamelCase__ = crop_size
UpperCamelCase__ = do_rescale
UpperCamelCase__ = rescale_factor
UpperCamelCase__ = do_normalize
UpperCamelCase__ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCamelCase__ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase__ = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
if "shortest_edge" not in size and ("height" not in size or "width" not in size):
raise ValueError(F"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
if crop_pct is not None:
if "shortest_edge" in size:
UpperCamelCase__ = int(size["""shortest_edge"""] / crop_pct )
elif "height" in size and "width" in size:
if size["height"] == size["width"]:
UpperCamelCase__ = int(size["""height"""] / crop_pct )
else:
UpperCamelCase__ = (int(size["""height"""] / crop_pct ), int(size["""width"""] / crop_pct ))
else:
raise ValueError("""Invalid size for resize: {}""".format(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase__ = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
else:
if "shortest_edge" in size:
UpperCamelCase__ = get_resize_output_image_size(SCREAMING_SNAKE_CASE_ , size=size["""shortest_edge"""] , default_to_square=SCREAMING_SNAKE_CASE_ )
elif "height" in size and "width" in size:
UpperCamelCase__ = (size["""height"""], size["""width"""])
else:
raise ValueError("""Invalid size for resize: {}""".format(SCREAMING_SNAKE_CASE_ ) )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase__ = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(F"size must contain '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 UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (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_ , ):
UpperCamelCase__ = do_resize if do_resize is not None else self.do_resize
UpperCamelCase__ = crop_pct if crop_pct is not None else self.crop_pct
UpperCamelCase__ = resample if resample is not None else self.resample
UpperCamelCase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCamelCase__ = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase__ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase__ = image_mean if image_mean is not None else self.image_mean
UpperCamelCase__ = image_std if image_std is not None else self.image_std
UpperCamelCase__ = size if size is not None else self.size
UpperCamelCase__ = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = crop_size if crop_size is not None else self.crop_size
UpperCamelCase__ = get_size_dict(SCREAMING_SNAKE_CASE_ , param_name="""crop_size""" )
UpperCamelCase__ = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_pct is None:
raise ValueError("""Crop_pct 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.""" )
# All transformations expect numpy arrays.
UpperCamelCase__ = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase__ = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , crop_pct=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_center_crop:
UpperCamelCase__ = [self.center_crop(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase__ = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase__ = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase__ = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase__ = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
| 244 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format="""%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s""",
datefmt="""%Y-%m-%d %H:%M:%S""",
level=os.environ.get("""LOGLEVEL""", """INFO""").upper(),
stream=sys.stdout,
)
_a = logging.getLogger(__name__)
_a = {"""facebook/bart-base""": BartForConditionalGeneration}
_a = {"""facebook/bart-base""": BartTokenizer}
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
_UpperCamelCase = argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.''' )
parser.add_argument(
'''--validation_file''', type=__snake_case, default=__snake_case, help='''A csv or a json file containing the validation data.''' )
parser.add_argument(
'''--max_length''', type=__snake_case, default=5, help='''The maximum total input sequence length after tokenization.''', )
parser.add_argument(
'''--num_beams''', type=__snake_case, default=__snake_case, help=(
'''Number of beams to use for evaluation. This argument will be '''
'''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
), )
parser.add_argument(
'''--model_name_or_path''', type=__snake_case, help='''Path to pretrained model or model identifier from huggingface.co/models.''', required=__snake_case, )
parser.add_argument(
'''--config_name''', type=__snake_case, default=__snake_case, help='''Pretrained config name or path if not the same as model_name''', )
parser.add_argument(
'''--device''', type=__snake_case, default='''cpu''', help='''Device where the model will be run''', )
parser.add_argument('''--output_file_path''', type=__snake_case, default=__snake_case, help='''Where to store the final ONNX file.''' )
_UpperCamelCase = parser.parse_args()
return args
def lowerCamelCase__ ( __snake_case, __snake_case="cpu" ) -> Any:
"""simple docstring"""
_UpperCamelCase = model_dict[model_name].from_pretrained(__snake_case ).to(__snake_case )
_UpperCamelCase = tokenizer_dict[model_name].from_pretrained(__snake_case )
if model_name in ["facebook/bart-base"]:
_UpperCamelCase = 0
_UpperCamelCase = None
_UpperCamelCase = 0
return huggingface_model, tokenizer
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
model.eval()
_UpperCamelCase = None
_UpperCamelCase = torch.jit.script(BARTBeamSearchGenerator(__snake_case ) )
with torch.no_grad():
_UpperCamelCase = '''My friends are cool but they eat too many carbs.'''
_UpperCamelCase = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=10_24, return_tensors='''pt''' ).to(model.device )
_UpperCamelCase = model.generate(
inputs['''input_ids'''], attention_mask=inputs['''attention_mask'''], num_beams=__snake_case, max_length=__snake_case, early_stopping=__snake_case, decoder_start_token_id=model.config.decoder_start_token_id, )
torch.onnx.export(
__snake_case, (
inputs['''input_ids'''],
inputs['''attention_mask'''],
num_beams,
max_length,
model.config.decoder_start_token_id,
), __snake_case, opset_version=14, input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id'''], output_names=['''output_ids'''], dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''seq'''},
'''output_ids''': {0: '''batch''', 1: '''seq_out'''},
}, example_outputs=__snake_case, )
logger.info('''Model exported to {}'''.format(__snake_case ) )
_UpperCamelCase = remove_dup_initializers(os.path.abspath(__snake_case ) )
logger.info('''Deduplicated and optimized model written to {}'''.format(__snake_case ) )
_UpperCamelCase = onnxruntime.InferenceSession(__snake_case )
_UpperCamelCase = ort_sess.run(
__snake_case, {
'''input_ids''': inputs['''input_ids'''].cpu().numpy(),
'''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
'''num_beams''': np.array(__snake_case ),
'''max_length''': np.array(__snake_case ),
'''decoder_start_token_id''': np.array(model.config.decoder_start_token_id ),
}, )
np.testing.assert_allclose(summary_ids.cpu().numpy(), ort_out[0], rtol=1e-3, atol=1e-3 )
logger.info('''Model outputs from torch and ONNX Runtime are similar.''' )
logger.info('''Success.''' )
def lowerCamelCase__ ( ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = parse_args()
_UpperCamelCase = 5
_UpperCamelCase = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO, )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
_UpperCamelCase = torch.device(args.device )
_UpperCamelCase , _UpperCamelCase = load_model_tokenizer(args.model_name_or_path, __snake_case )
if model.config.decoder_start_token_id is None:
raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined''' )
model.to(__snake_case )
if args.max_length:
_UpperCamelCase = args.max_length
if args.num_beams:
_UpperCamelCase = args.num_beams
if args.output_file_path:
_UpperCamelCase = args.output_file_path
else:
_UpperCamelCase = '''BART.onnx'''
logger.info('''Exporting model to ONNX''' )
export_and_validate_model(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
if __name__ == "__main__":
main()
| 369 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"""_TestCommandArgs""",
[
"""dataset""",
"""name""",
"""cache_dir""",
"""data_dir""",
"""all_configs""",
"""save_infos""",
"""ignore_verifications""",
"""force_redownload""",
"""clear_cache""",
],
defaults=[None, None, None, False, False, False, False, False],
)
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[int]:
"""simple docstring"""
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def lowerCamelCase__ ( __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = _TestCommandArgs(dataset=__snake_case, all_configs=__snake_case, save_infos=__snake_case )
_UpperCamelCase = TestCommand(*__snake_case )
test_command.run()
_UpperCamelCase = os.path.join(__snake_case, '''README.md''' )
assert os.path.exists(__snake_case )
_UpperCamelCase = DatasetInfosDict.from_directory(__snake_case )
_UpperCamelCase = DatasetInfosDict(
{
'''default''': DatasetInfo(
features=Features(
{
'''tokens''': Sequence(Value('''string''' ) ),
'''ner_tags''': Sequence(
ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC'''] ) ),
'''langs''': Sequence(Value('''string''' ) ),
'''spans''': Sequence(Value('''string''' ) ),
} ), splits=[
{
'''name''': '''train''',
'''num_bytes''': 2_35_15_63,
'''num_examples''': 1_00_00,
},
{
'''name''': '''validation''',
'''num_bytes''': 23_84_18,
'''num_examples''': 10_00,
},
], download_size=3_94_06_80, dataset_size=2_58_99_81, )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
_UpperCamelCase , _UpperCamelCase = getattr(dataset_infos['''default'''], __snake_case ), getattr(expected_dataset_infos['''default'''], __snake_case )
if key == "num_bytes":
assert is_apercent_close(__snake_case, __snake_case )
elif key == "splits":
assert list(__snake_case ) == list(__snake_case )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes, expected[split].num_bytes )
else:
result == expected
| 100 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ ='blenderbot-small'
lowerCamelCase__ =['past_key_values']
lowerCamelCase__ ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__(self , a_=5_02_65 , a_=5_12 , a_=8 , a_=20_48 , a_=16 , a_=8 , a_=20_48 , a_=16 , a_=0.0 , a_=0.0 , a_=True , a_=True , a_="gelu" , a_=5_12 , a_=0.1 , a_=0.0 , a_=0.0 , a_=0.02 , a_=1 , a_=False , a_=0 , a_=1 , a_=2 , a_=2 , **a_ , ):
'''simple docstring'''
__snake_case : Any = vocab_size
__snake_case : Any = max_position_embeddings
__snake_case : Tuple = d_model
__snake_case : str = encoder_ffn_dim
__snake_case : Optional[int] = encoder_layers
__snake_case : int = encoder_attention_heads
__snake_case : Optional[Any] = decoder_ffn_dim
__snake_case : List[str] = decoder_layers
__snake_case : List[str] = decoder_attention_heads
__snake_case : Union[str, Any] = dropout
__snake_case : str = attention_dropout
__snake_case : List[Any] = activation_dropout
__snake_case : Union[str, Any] = activation_function
__snake_case : List[Any] = init_std
__snake_case : List[str] = encoder_layerdrop
__snake_case : List[Any] = decoder_layerdrop
__snake_case : Optional[int] = use_cache
__snake_case : Optional[Any] = encoder_layers
__snake_case : List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , is_encoder_decoder=a_ , decoder_start_token_id=a_ , forced_eos_token_id=a_ , **a_ , )
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__snake_case : Tuple = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
__snake_case : Optional[Any] = {0: '''batch'''}
__snake_case : Tuple = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
__snake_case : Dict = {0: '''batch''', 1: '''decoder_sequence'''}
__snake_case : Optional[Any] = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(a_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
__snake_case : str = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
__snake_case , __snake_case : Tuple = self.num_layers
for i in range(a_ ):
__snake_case : int = {0: '''batch''', 2: '''past_sequence + sequence'''}
__snake_case : int = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
__snake_case : Dict = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__snake_case : str = super().outputs
else:
__snake_case : List[Any] = super(a_ , self ).outputs
if self.use_past:
__snake_case , __snake_case : int = self.num_layers
for i in range(a_ ):
__snake_case : Tuple = {0: '''batch''', 2: '''past_sequence + sequence'''}
__snake_case : Optional[int] = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def SCREAMING_SNAKE_CASE (self , a_ , a_ = -1 , a_ = -1 , a_ = False , a_ = None , ):
'''simple docstring'''
__snake_case : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
a_ , a_ , a_ , a_ , a_ )
# Generate decoder inputs
__snake_case : str = seq_length if not self.use_past else 1
__snake_case : Union[str, Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
a_ , a_ , a_ , a_ , a_ )
__snake_case : List[Any] = {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
__snake_case : Dict = dict(**a_ , **a_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__snake_case , __snake_case : int = common_inputs['''input_ids'''].shape
__snake_case : Union[str, Any] = common_inputs['''decoder_input_ids'''].shape[1]
__snake_case , __snake_case : Union[str, Any] = self.num_attention_heads
__snake_case : Dict = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__snake_case : Tuple = decoder_seq_length + 3
__snake_case : Dict = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
__snake_case : Optional[Any] = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(a_ , a_ )] , dim=1 )
__snake_case : int = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
__snake_case , __snake_case : str = self.num_layers
__snake_case : Dict = min(a_ , a_ )
__snake_case : List[str] = max(a_ , a_ ) - min_num_layers
__snake_case : Optional[int] = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(a_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(a_ ),
torch.zeros(a_ ),
torch.zeros(a_ ),
torch.zeros(a_ ),
) )
# TODO: test this.
__snake_case : Optional[Any] = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(a_ , a_ ):
common_inputs["past_key_values"].append((torch.zeros(a_ ), torch.zeros(a_ )) )
return common_inputs
def SCREAMING_SNAKE_CASE (self , a_ , a_ = -1 , a_ = -1 , a_ = False , a_ = None , ):
'''simple docstring'''
__snake_case : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
a_ , a_ , a_ , a_ , a_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
__snake_case , __snake_case : Dict = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
__snake_case : Optional[int] = seqlen + 2
__snake_case , __snake_case : Dict = self.num_layers
__snake_case , __snake_case : Optional[Any] = self.num_attention_heads
__snake_case : Optional[int] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
__snake_case : str = common_inputs['''attention_mask'''].dtype
__snake_case : Optional[int] = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(a_ , a_ , dtype=a_ )] , dim=1 )
__snake_case : List[str] = [
(torch.zeros(a_ ), torch.zeros(a_ )) for _ in range(a_ )
]
return common_inputs
def SCREAMING_SNAKE_CASE (self , a_ , a_ = -1 , a_ = -1 , a_ = False , a_ = None , ):
'''simple docstring'''
__snake_case : Dict = compute_effective_axis_dimension(
a_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
__snake_case : List[Any] = tokenizer.num_special_tokens_to_add(a_ )
__snake_case : Union[str, Any] = compute_effective_axis_dimension(
a_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=a_ )
# Generate dummy inputs according to compute batch and sequence
__snake_case : Optional[int] = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
__snake_case : Union[str, Any] = dict(tokenizer(a_ , return_tensors=a_ ) )
return common_inputs
def SCREAMING_SNAKE_CASE (self , a_ , a_ = -1 , a_ = -1 , a_ = False , a_ = None , ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__snake_case : str = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
a_ , batch_size=a_ , seq_length=a_ , is_pair=a_ , framework=a_ )
elif self.task == "causal-lm":
__snake_case : str = self._generate_dummy_inputs_for_causal_lm(
a_ , batch_size=a_ , seq_length=a_ , is_pair=a_ , framework=a_ )
else:
__snake_case : int = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
a_ , batch_size=a_ , seq_length=a_ , is_pair=a_ , framework=a_ )
return common_inputs
def SCREAMING_SNAKE_CASE (self , a_ , a_ , a_ , a_ ):
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
__snake_case : Tuple = super()._flatten_past_key_values_(a_ , a_ , a_ , a_ )
else:
__snake_case : str = super(a_ , self )._flatten_past_key_values_(
a_ , a_ , a_ , a_ )
| 102 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class _UpperCAmelCase :
'''simple docstring'''
def __init__(self , a_ ):
'''simple docstring'''
__snake_case : List[str] = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__snake_case : Optional[Any] = len(a_ ) - 1
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__snake_case : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , a_ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(a_ ) , 5 ) == 1
return output_values
def SCREAMING_SNAKE_CASE (self , a_ ):
'''simple docstring'''
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__snake_case : List[str] = self.basis_function(a_ )
__snake_case : str = 0.0
__snake_case : Union[str, Any] = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def SCREAMING_SNAKE_CASE (self , a_ = 0.01 ):
'''simple docstring'''
from matplotlib import pyplot as plt # type: ignore
__snake_case : list[float] = [] # x coordinates of points to plot
__snake_case : list[float] = [] # y coordinates of points to plot
__snake_case : int = 0.0
while t <= 1:
__snake_case : Union[str, Any] = self.bezier_curve_function(a_ )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__snake_case : List[Any] = [i[0] for i in self.list_of_points]
__snake_case : Any = [i[1] for i in self.list_of_points]
plt.plot(
a_ , a_ , color='''blue''' , label='''Curve of Degree ''' + str(self.degree ) , )
plt.scatter(a_ , a_ , color='''red''' , label='''Control Points''' )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 102 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
__snake_case = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ):
'''simple docstring'''
warnings.warn(
'''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use PerceiverImageProcessor instead.''' , __a , )
super().__init__(*__a , **__a )
| 350 |
'''simple docstring'''
from math import ceil
def a ( __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :str = list(range(0 , __a ) )
UpperCamelCase__ :Optional[int] = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
UpperCamelCase__ :Optional[int] = []
for i in device_map_blocks:
if device_map_blocks.count(__a ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(__a )
# Missing blocks
UpperCamelCase__ :List[str] = [i for i in blocks if i not in device_map_blocks]
UpperCamelCase__ :Optional[Any] = [i for i in device_map_blocks if i not in blocks]
if len(__a ) != 0:
raise ValueError(
'''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
''' These attention blocks were specified more than once: ''' + str(__a ) )
if len(__a ) != 0:
raise ValueError(
'''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
'''blocks to a device on the device_map: ''' + str(__a ) )
if len(__a ) != 0:
raise ValueError(
'''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
+ str(__a ) )
def a ( __a , __a ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = list(range(__a ) )
UpperCamelCase__ :Any = int(ceil(n_layers / len(__a ) ) )
UpperCamelCase__ :List[Any] = [layers[i : i + n_blocks] for i in range(0 , __a , __a )]
return dict(zip(__a , __a ) ) | 219 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase ( lowercase , unittest.TestCase ):
UpperCAmelCase : Dict = GPTSanJapaneseTokenizer
UpperCAmelCase : Dict = False
UpperCAmelCase : Union[str, Any] = {"""do_clean_text""": False, """add_prefix_space""": False}
def _lowercase (self : str) -> str:
super().setUp()
# fmt: off
__snake_case : Any = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
__snake_case : int = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
__snake_case : Union[str, Any] = {'unk_token': '<unk>'}
__snake_case : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
__snake_case : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'])
with open(self.vocab_file , 'w' , encoding='utf-8') as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
with open(self.emoji_file , 'w') as emoji_writer:
emoji_writer.write(json.dumps(_A))
def _lowercase (self : List[Any] , **_A : Union[str, Any]) -> int:
kwargs.update(self.special_tokens_map)
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **_A)
def _lowercase (self : List[str] , _A : Tuple) -> Tuple:
__snake_case : List[str] = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
__snake_case : Dict = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def _lowercase (self : int , _A : Dict) -> Any:
__snake_case , __snake_case : List[Any] = self.get_input_output_texts(_A)
__snake_case : str = tokenizer.encode(_A , add_special_tokens=_A)
__snake_case : int = tokenizer.decode(_A , clean_up_tokenization_spaces=_A)
return text, ids
def _lowercase (self : str) -> Tuple:
pass # TODO add if relevant
def _lowercase (self : int) -> Union[str, Any]:
pass # TODO add if relevant
def _lowercase (self : List[str]) -> List[Any]:
pass # TODO add if relevant
def _lowercase (self : Union[str, Any]) -> List[str]:
__snake_case : Any = self.get_tokenizer()
# Testing tokenization
__snake_case : Optional[int] = 'こんにちは、世界。 こんばんは、㔺界。'
__snake_case : str = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
__snake_case : str = tokenizer.tokenize(_A)
self.assertListEqual(_A , _A)
# Testing conversion to ids without special tokens
__snake_case : Any = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
__snake_case : List[Any] = tokenizer.convert_tokens_to_ids(_A)
self.assertListEqual(_A , _A)
# Testing conversion to ids with special tokens
__snake_case : Optional[int] = tokens + [tokenizer.unk_token]
__snake_case : str = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
__snake_case : Any = tokenizer.convert_tokens_to_ids(_A)
self.assertListEqual(_A , _A)
def _lowercase (self : Optional[int]) -> Optional[Any]:
__snake_case : Tuple = self.get_tokenizer()
# Testing tokenization
__snake_case : Any = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
__snake_case : Optional[Any] = 'こんにちは、、、、世界。こんばんは、、、、世界。'
__snake_case : List[Any] = tokenizer.encode(_A)
__snake_case : Optional[Any] = tokenizer.decode(_A)
self.assertEqual(_A , _A)
@slow
def _lowercase (self : int) -> Optional[int]:
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')
# Testing tokenization
__snake_case : str = 'こんにちは、世界。'
__snake_case : Dict = 'こんばんは、㔺界。😀'
__snake_case : List[str] = 'こんにちは、世界。こんばんは、世界。😀'
__snake_case : Dict = tokenizer.encode(prefix_text + input_text)
__snake_case : List[str] = tokenizer.encode('' , prefix_text=prefix_text + input_text)
__snake_case : int = tokenizer.encode(_A , prefix_text=_A)
__snake_case : Dict = tokenizer.decode(_A)
__snake_case : Union[str, Any] = tokenizer.decode(_A)
__snake_case : List[Any] = tokenizer.decode(_A)
self.assertEqual(_A , _A)
self.assertEqual(_A , _A)
self.assertEqual(_A , _A)
@slow
def _lowercase (self : Optional[Any]) -> int:
__snake_case : Dict = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')
# Testing tokenization
__snake_case : List[str] = 'こんにちは、世界。'
__snake_case : Dict = 'こんばんは、㔺界。😀'
__snake_case : int = len(tokenizer.encode(_A)) - 2
__snake_case : str = len(tokenizer.encode(_A)) - 2
__snake_case : List[str] = [1] + [0] * (len_prefix + len_text + 1)
__snake_case : Union[str, Any] = [1] * (len_prefix + len_text + 1) + [0]
__snake_case : List[Any] = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
__snake_case : Dict = tokenizer(prefix_text + input_text).token_type_ids
__snake_case : Optional[int] = tokenizer('' , prefix_text=prefix_text + input_text).token_type_ids
__snake_case : str = tokenizer(_A , prefix_text=_A).token_type_ids
self.assertListEqual(_A , _A)
self.assertListEqual(_A , _A)
self.assertListEqual(_A , _A)
@slow
def _lowercase (self : int) -> Optional[Any]:
__snake_case : str = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')
__snake_case : int = tokenizer.encode('あンいワ')
__snake_case : Dict = tokenizer.encode('' , prefix_text='あンいワ')
__snake_case : Dict = tokenizer.encode('いワ' , prefix_text='あン')
self.assertEqual(tokenizer.decode(_A) , tokenizer.decode(_A))
self.assertEqual(tokenizer.decode(_A) , tokenizer.decode(_A))
self.assertNotEqual(_A , _A)
self.assertNotEqual(_A , _A)
self.assertEqual(x_token_a[1] , x_token_a[-1]) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3]) # SEG token
@slow
def _lowercase (self : Tuple) -> List[str]:
__snake_case : Optional[Any] = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')
__snake_case : Dict = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
__snake_case : List[Any] = tokenizer(_A , padding=_A)
__snake_case : List[str] = tokenizer.batch_encode_plus(_A , padding=_A)
# fmt: off
__snake_case : List[Any] = [[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
__snake_case : int = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
__snake_case : List[str] = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , _A)
self.assertListEqual(x_token.token_type_ids , _A)
self.assertListEqual(x_token.attention_mask , _A)
self.assertListEqual(x_token_a.input_ids , _A)
self.assertListEqual(x_token_a.token_type_ids , _A)
self.assertListEqual(x_token_a.attention_mask , _A)
def _lowercase (self : Any) -> int:
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def _lowercase (self : List[str]) -> Tuple:
# tokenizer has no padding token
pass
| 172 | """simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : Tuple ) -> Optional[int]:
'''simple docstring'''
__snake_case : List[str] = []
__snake_case : Optional[Any] = set({'(', '[', '{'} )
__snake_case : Union[str, Any] = set({')', ']', '}'} )
__snake_case : Tuple = {'{': '}', '[': ']', '(': ')'}
for i in range(len(UpperCAmelCase_ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(UpperCAmelCase_ ) == 0 or (len(UpperCAmelCase_ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(UpperCAmelCase_ ) == 0
def __UpperCAmelCase ( ) -> Any:
'''simple docstring'''
__snake_case : Optional[Any] = input('Enter sequence of brackets: ' )
if is_balanced(UpperCAmelCase_ ):
print(UpperCAmelCase_ , 'is balanced' )
else:
print(UpperCAmelCase_ , 'is not balanced' )
if __name__ == "__main__":
main()
| 172 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class _snake_case ( snake_case ):
UpperCamelCase__ = 'transfo-xl'
UpperCamelCase__ = ['mems']
UpperCamelCase__ = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , _a=267_735 , _a=[20_000, 40_000, 200_000] , _a=1_024 , _a=1_024 , _a=16 , _a=64 , _a=4_096 , _a=4 , _a=False , _a=18 , _a=1_600 , _a=1_000 , _a=True , _a=True , _a=0 , _a=-1 , _a=True , _a=0.1 , _a=0.0 , _a=True , _a="normal" , _a=0.01 , _a=0.01 , _a=0.02 , _a=1e-5 , _a=0 , **_a , ):
__magic_name__ : List[Any] = vocab_size
__magic_name__ : Dict = []
self.cutoffs.extend(_a )
if proj_share_all_but_first:
__magic_name__ : List[str] = [False] + [True] * len(self.cutoffs )
else:
__magic_name__ : Optional[Any] = [False] + [False] * len(self.cutoffs )
__magic_name__ : Optional[int] = d_model
__magic_name__ : str = d_embed
__magic_name__ : Optional[Any] = d_head
__magic_name__ : Optional[int] = d_inner
__magic_name__ : List[str] = div_val
__magic_name__ : List[str] = pre_lnorm
__magic_name__ : Union[str, Any] = n_layer
__magic_name__ : Optional[int] = n_head
__magic_name__ : str = mem_len
__magic_name__ : int = same_length
__magic_name__ : Dict = attn_type
__magic_name__ : int = clamp_len
__magic_name__ : Optional[int] = sample_softmax
__magic_name__ : List[Any] = adaptive
__magic_name__ : Optional[int] = dropout
__magic_name__ : Optional[int] = dropatt
__magic_name__ : Optional[Any] = untie_r
__magic_name__ : List[str] = init
__magic_name__ : Any = init_range
__magic_name__ : Optional[int] = proj_init_std
__magic_name__ : List[Any] = init_std
__magic_name__ : List[Any] = layer_norm_epsilon
super().__init__(eos_token_id=_a , **_a )
@property
def SCREAMING_SNAKE_CASE ( self ):
# Message copied from Transformer-XL documentation
logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
return -1
@max_position_embeddings.setter
def SCREAMING_SNAKE_CASE ( self , _a ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 41 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class _snake_case ( snake_case ):
UpperCamelCase__ = 'transfo-xl'
UpperCamelCase__ = ['mems']
UpperCamelCase__ = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , _a=267_735 , _a=[20_000, 40_000, 200_000] , _a=1_024 , _a=1_024 , _a=16 , _a=64 , _a=4_096 , _a=4 , _a=False , _a=18 , _a=1_600 , _a=1_000 , _a=True , _a=True , _a=0 , _a=-1 , _a=True , _a=0.1 , _a=0.0 , _a=True , _a="normal" , _a=0.01 , _a=0.01 , _a=0.02 , _a=1e-5 , _a=0 , **_a , ):
__magic_name__ : List[Any] = vocab_size
__magic_name__ : Dict = []
self.cutoffs.extend(_a )
if proj_share_all_but_first:
__magic_name__ : List[str] = [False] + [True] * len(self.cutoffs )
else:
__magic_name__ : Optional[Any] = [False] + [False] * len(self.cutoffs )
__magic_name__ : Optional[int] = d_model
__magic_name__ : str = d_embed
__magic_name__ : Optional[Any] = d_head
__magic_name__ : Optional[int] = d_inner
__magic_name__ : List[str] = div_val
__magic_name__ : List[str] = pre_lnorm
__magic_name__ : Union[str, Any] = n_layer
__magic_name__ : Optional[int] = n_head
__magic_name__ : str = mem_len
__magic_name__ : int = same_length
__magic_name__ : Dict = attn_type
__magic_name__ : int = clamp_len
__magic_name__ : Optional[int] = sample_softmax
__magic_name__ : List[Any] = adaptive
__magic_name__ : Optional[int] = dropout
__magic_name__ : Optional[int] = dropatt
__magic_name__ : Optional[Any] = untie_r
__magic_name__ : List[str] = init
__magic_name__ : Any = init_range
__magic_name__ : Optional[int] = proj_init_std
__magic_name__ : List[Any] = init_std
__magic_name__ : List[Any] = layer_norm_epsilon
super().__init__(eos_token_id=_a , **_a )
@property
def SCREAMING_SNAKE_CASE ( self ):
# Message copied from Transformer-XL documentation
logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
return -1
@max_position_embeddings.setter
def SCREAMING_SNAKE_CASE ( self , _a ):
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 41 | 1 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
lowerCamelCase__ = """python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("""importlib_metadata""")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase=None ):
require_version(deps[pkg] , _UpperCamelCase ) | 86 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class A__ ( _lowerCamelCase , unittest.TestCase):
A_ : Union[str, Any] = BarthezTokenizer
A_ : Tuple = BarthezTokenizerFast
A_ : Dict = True
A_ : List[str] = True
def __lowerCamelCase ( self ):
super().setUp()
__lowerCAmelCase : str = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = tokenizer
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[Any] = '<pad>'
__lowerCAmelCase : Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , 10_11_22 )
def __lowerCamelCase ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[str] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__lowerCAmelCase : Optional[Any] = [0, 57, 30_18, 7_03_07, 91, 2]
__lowerCAmelCase : Optional[int] = self.tokenizer(
_SCREAMING_SNAKE_CASE , max_length=len(_SCREAMING_SNAKE_CASE ) , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
__lowerCAmelCase : List[str] = batch.input_ids.tolist()[0]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
if not self.test_rust_tokenizer:
return
__lowerCAmelCase : Tuple = self.get_tokenizer()
__lowerCAmelCase : Optional[int] = self.get_rust_tokenizer()
__lowerCAmelCase : List[str] = 'I was born in 92000, and this is falsé.'
__lowerCAmelCase : Optional[int] = tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = self.get_rust_tokenizer()
__lowerCAmelCase : List[Any] = tokenizer.encode(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
# fmt: off
__lowerCAmelCase : str = {'input_ids': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
__lowerCAmelCase : Union[str, Any] = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=_SCREAMING_SNAKE_CASE , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=_SCREAMING_SNAKE_CASE , ) | 86 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a__ = '''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
'''
a__ = '''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''
a__ = '''
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric("code_eval")
>>> test_cases = ["assert add(2,3)==5"]
>>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{\'pass@1\': 0.5, \'pass@2\': 1.0}
'''
a__ = '''
################################################################################
!!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:
>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
################################################################################\
'''
a__ = '''The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
"""simple docstring"""
def __lowercase ( self ) -> Optional[Any]:
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Value('''string''' ),
} ) , homepage='''https://github.com/openai/human-eval''' , codebase_urls=['''https://github.com/openai/human-eval'''] , reference_urls=['''https://github.com/openai/human-eval'''] , license=_LICENSE , )
def __lowercase ( self , _a , _a , _a=[1, 1_0, 1_0_0] , _a=4 , _a=3.0 ) -> Tuple:
if os.getenv('''HF_ALLOW_CODE_EVAL''' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('''This metric is currently not supported on Windows.''' )
with ThreadPoolExecutor(max_workers=_a ) as executor:
_a : Dict = []
_a : str = Counter()
_a : Any = 0
_a : List[str] = defaultdict(_a )
for task_id, (candidates, test_case) in enumerate(zip(_a , _a ) ):
for candidate in candidates:
_a : Any = candidate + '''\n''' + test_case
_a : Optional[Any] = (test_program, timeout, task_id, completion_id[task_id])
_a : Dict = executor.submit(_a , *_a )
futures.append(_a )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(_a ):
_a : Any = future.result()
results[result["task_id"]].append((result['''completion_id'''], result) )
_a , _a : Dict = [], []
for result in results.values():
result.sort()
_a : str = [r[1]['''passed'''] for r in result]
total.append(len(_a ) )
correct.append(sum(_a ) )
_a : str = np.array(_a )
_a : int = np.array(_a )
_a : List[str] = k
_a : Dict = {F"""pass@{k}""": estimate_pass_at_k(_a , _a , _a ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def __UpperCAmelCase ( __a : Tuple ,__a : List[str] ,__a : Any ) -> Tuple:
"""simple docstring"""
def estimator(__a : int ,__a : int ,__a : int ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 ,n + 1 ) )
if isinstance(__a ,__a ):
_a : List[str] = itertools.repeat(__a ,len(__a ) )
else:
assert len(__a ) == len(__a )
_a : Optional[Any] = iter(__a )
return np.array([estimator(int(__a ) ,int(__a ) ,__a ) for n, c in zip(__a ,__a )] )
| 15 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
a__ = logging.get_logger(__name__)
@add_end_docstrings(
__lowercase , r"\n top_k (`int`, defaults to 5):\n The number of predictions to return.\n targets (`str` or `List[str]`, *optional*):\n When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n token will be used (with a warning, and that might be slower).\n\n " , )
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
def __lowercase ( self , _a ) -> np.ndarray:
if self.framework == "tf":
_a : List[str] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
_a : Tuple = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=_a )
else:
raise ValueError('''Unsupported framework''' )
return masked_index
def __lowercase ( self , _a ) -> np.ndarray:
_a : int = self.get_masked_index(_a )
_a : Tuple = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
'''fill-mask''' , self.model.base_model_prefix , F"""No mask_token ({self.tokenizer.mask_token}) found on the input""" , )
def __lowercase ( self , _a ) -> Optional[int]:
if isinstance(_a , _a ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(_a )
def __lowercase ( self , _a , _a=None , **_a ) -> Dict[str, GenericTensor]:
if return_tensors is None:
_a : Union[str, Any] = self.framework
_a : str = self.tokenizer(_a , return_tensors=_a )
self.ensure_exactly_one_mask_token(_a )
return model_inputs
def __lowercase ( self , _a ) -> Optional[Any]:
_a : List[str] = self.model(**_a )
_a : Any = model_inputs['''input_ids''']
return model_outputs
def __lowercase ( self , _a , _a=5 , _a=None ) -> str:
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
_a : List[Any] = target_ids.shape[0]
_a : Any = model_outputs['''input_ids'''][0]
_a : List[str] = model_outputs['''logits''']
if self.framework == "tf":
_a : Tuple = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
_a : List[str] = outputs.numpy()
_a : Dict = outputs[0, masked_index, :]
_a : str = stable_softmax(_a , axis=-1 )
if target_ids is not None:
_a : Any = tf.gather_nd(tf.squeeze(_a , 0 ) , target_ids.reshape(-1 , 1 ) )
_a : Union[str, Any] = tf.expand_dims(_a , 0 )
_a : Optional[int] = tf.math.top_k(_a , k=_a )
_a , _a : Optional[Any] = topk.values.numpy(), topk.indices.numpy()
else:
_a : Optional[Any] = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=_a ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
_a : List[str] = outputs[0, masked_index, :]
_a : List[Any] = logits.softmax(dim=-1 )
if target_ids is not None:
_a : List[Any] = probs[..., target_ids]
_a , _a : Optional[Any] = probs.topk(_a )
_a : Dict = []
_a : List[Any] = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
_a : Optional[Any] = []
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
_a : Optional[int] = input_ids.numpy().copy()
if target_ids is not None:
_a : Tuple = target_ids[p].tolist()
_a : List[str] = p
# Filter padding out:
_a : List[Any] = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
_a : List[str] = self.tokenizer.decode(_a , skip_special_tokens=_a )
_a : List[Any] = {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p] ), '''sequence''': sequence}
row.append(_a )
result.append(_a )
if single_mask:
return result[0]
return result
def __lowercase ( self , _a , _a=None ) -> Dict:
if isinstance(_a , _a ):
_a : Tuple = [targets]
try:
_a : int = self.tokenizer.get_vocab()
except Exception:
_a : Any = {}
_a : List[Any] = []
for target in targets:
_a : List[Any] = vocab.get(_a , _a )
if id_ is None:
_a : Tuple = self.tokenizer(
_a , add_special_tokens=_a , return_attention_mask=_a , return_token_type_ids=_a , max_length=1 , truncation=_a , )['''input_ids''']
if len(_a ) == 0:
logger.warning(
F"""The specified target token `{target}` does not exist in the model vocabulary. """
'''We cannot replace it with anything meaningful, ignoring it''' )
continue
_a : Tuple = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
F"""The specified target token `{target}` does not exist in the model vocabulary. """
F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.""" )
target_ids.append(id_ )
_a : List[str] = list(set(_a ) )
if len(_a ) == 0:
raise ValueError('''At least one target must be provided when passed.''' )
_a : int = np.array(_a )
return target_ids
def __lowercase ( self , _a=None , _a=None ) -> Tuple:
_a : str = {}
if targets is not None:
_a : List[Any] = self.get_target_ids(_a , _a )
_a : Optional[Any] = target_ids
if top_k is not None:
_a : Union[str, Any] = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'''fill-mask''' , self.model.base_model_prefix , '''The tokenizer does not define a `mask_token`.''' )
return {}, {}, postprocess_params
def __call__( self , _a , *_a , **_a ) -> int:
_a : Optional[Any] = super().__call__(_a , **_a )
if isinstance(_a , _a ) and len(_a ) == 1:
return outputs[0]
return outputs
| 15 | 1 |
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :Optional[int] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
SCREAMING_SNAKE_CASE :Tuple = '\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results["exact_match"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 33.3\n\n'
SCREAMING_SNAKE_CASE :str = '\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : List[Any] ):
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" ),
} ) ,reference_urls=[] ,)
def UpperCamelCase_ ( self : List[Any] ,A : List[Any] ,A : List[Any] ,A : Any=None ,A : List[Any]=False ,A : Any=False ,A : Dict=False ,):
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
__A = np.array([re.sub(A ,"" ,A ) for x in predictions] )
__A = np.array([re.sub(A ,"" ,A ) for x in references] )
else:
__A = np.asarray(A )
__A = np.asarray(A )
if ignore_case:
__A = np.char.lower(A )
__A = np.char.lower(A )
if ignore_punctuation:
__A = string.punctuation.maketrans("" ,"" ,string.punctuation )
__A = np.char.translate(A ,table=A )
__A = np.char.translate(A ,table=A )
if ignore_numbers:
__A = string.digits.maketrans("" ,"" ,string.digits )
__A = np.char.translate(A ,table=A )
__A = np.char.translate(A ,table=A )
__A = predictions == references
return {"exact_match": np.mean(A ) * 1_00}
| 15 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 211 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def UpperCamelCase ( __lowerCamelCase : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int] ):
for attribute in key.split("." ):
snake_case : str = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
snake_case : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
snake_case : str = hf_pointer.shape
assert hf_shape == value.shape, (
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
snake_case : Optional[int] = value
elif weight_type == "weight_g":
snake_case : List[Any] = value
elif weight_type == "weight_v":
snake_case : Union[str, Any] = value
elif weight_type == "bias":
snake_case : Optional[int] = value
else:
snake_case : str = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def UpperCamelCase ( __lowerCamelCase : List[Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : int ):
snake_case : Dict = []
snake_case : Optional[int] = fairseq_model.state_dict()
snake_case : Optional[int] = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
snake_case : Dict = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == "group" , )
snake_case : str = True
else:
for key, mapped_key in MAPPING.items():
snake_case : Tuple = """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or (key.split("w2v_model." )[-1] == name.split("." )[0] and not is_finetuned):
snake_case : Optional[Any] = True
if "*" in mapped_key:
snake_case : Dict = name.split(_lowerCAmelCase )[0].split("." )[-2]
snake_case : Any = mapped_key.replace("*" , _lowerCAmelCase )
if "weight_g" in name:
snake_case : List[Any] = """weight_g"""
elif "weight_v" in name:
snake_case : Optional[Any] = """weight_v"""
elif "weight" in name:
snake_case : str = """weight"""
elif "bias" in name:
snake_case : List[str] = """bias"""
else:
snake_case : str = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def UpperCamelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : Dict ):
snake_case : Optional[int] = full_name.split("conv_layers." )[-1]
snake_case : List[str] = name.split("." )
snake_case : List[str] = int(items[0] )
snake_case : List[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
snake_case : Optional[int] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
snake_case : Dict = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
snake_case : List[str] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
snake_case : List[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def UpperCamelCase ( __lowerCamelCase : List[Any] , __lowerCamelCase : str , __lowerCamelCase : Any=None , __lowerCamelCase : List[Any]=None , __lowerCamelCase : int=True ):
if config_path is not None:
snake_case : Optional[Any] = HubertConfig.from_pretrained(_lowerCAmelCase )
else:
snake_case : str = HubertConfig()
if is_finetuned:
if dict_path:
snake_case : Optional[int] = Dictionary.load(_lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case : Optional[int] = target_dict.pad_index
snake_case : int = target_dict.bos_index
snake_case : Tuple = target_dict.eos_index
snake_case : List[Any] = len(target_dict.symbols )
snake_case : int = os.path.join(_lowerCAmelCase , "vocab.json" )
if not os.path.isdir(_lowerCAmelCase ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCAmelCase ) )
return
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(target_dict.indices , _lowerCAmelCase )
snake_case : List[Any] = WavaVecaCTCTokenizer(
_lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=_lowerCAmelCase , )
snake_case : List[str] = True if config.feat_extract_norm == """layer""" else False
snake_case : str = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , )
snake_case : List[Any] = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
snake_case : Optional[Any] = HubertForCTC(_lowerCAmelCase )
else:
snake_case : List[str] = HubertModel(_lowerCAmelCase )
if is_finetuned:
snake_case : Optional[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
snake_case : Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
snake_case : List[Any] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
__lowerCamelCase = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 370 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
__lowerCamelCase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def UpperCamelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Any ):
for attribute in key.split("." ):
snake_case : Tuple = getattr(__lowerCamelCase , __lowerCamelCase )
if weight_type is not None:
snake_case : int = getattr(__lowerCamelCase , __lowerCamelCase ).shape
else:
snake_case : Dict = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
snake_case : Dict = value
elif weight_type == "weight_g":
snake_case : Optional[int] = value
elif weight_type == "weight_v":
snake_case : Optional[int] = value
elif weight_type == "bias":
snake_case : Tuple = value
else:
snake_case : Optional[int] = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : List[str] ):
snake_case : int = []
snake_case : List[Any] = fairseq_model.state_dict()
snake_case : int = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
snake_case : List[str] = False
if "conv_layers" in name:
load_conv_layer(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , hf_model.config.feat_extract_norm == "group" , )
snake_case : str = True
else:
for key, mapped_key in MAPPING.items():
snake_case : Tuple = "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split("." )[:-1] ) != key):
# special case since naming is very similar
continue
snake_case : Tuple = True
if "*" in mapped_key:
snake_case : Union[str, Any] = name.split(__lowerCamelCase )[0].split("." )[-2]
snake_case : Any = mapped_key.replace("*" , __lowerCamelCase )
if "weight_g" in name:
snake_case : Optional[int] = "weight_g"
elif "weight_v" in name:
snake_case : Tuple = "weight_v"
elif "bias" in name:
snake_case : Dict = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case : str = "weight"
else:
snake_case : str = None
set_recursively(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
continue
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def UpperCamelCase ( __lowerCamelCase : Any , __lowerCamelCase : Any , __lowerCamelCase : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Any ):
snake_case : str = full_name.split("conv_layers." )[-1]
snake_case : int = name.split("." )
snake_case : Optional[int] = int(items[0] )
snake_case : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
snake_case : Union[str, Any] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
snake_case : List[str] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
snake_case : Dict = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
snake_case : Optional[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__lowerCamelCase )
@torch.no_grad()
def UpperCamelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : Dict , __lowerCamelCase : List[Any]=None , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : Dict=True ):
if config_path is not None:
snake_case : str = UniSpeechSatConfig.from_pretrained(__lowerCamelCase )
else:
snake_case : str = UniSpeechSatConfig()
snake_case : Tuple = ""
if is_finetuned:
snake_case : Tuple = UniSpeechSatForCTC(__lowerCamelCase )
else:
snake_case : List[Any] = UniSpeechSatForPreTraining(__lowerCamelCase )
snake_case , snake_case , snake_case : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
snake_case : Dict = model[0].eval()
recursively_load_weights(__lowerCamelCase , __lowerCamelCase )
hf_wavavec.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
__lowerCamelCase = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 10 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class a_ ( unittest.TestCase ):
'''simple docstring'''
@property
def snake_case_( self ) -> Any:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def snake_case_( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = ort.SessionOptions()
_SCREAMING_SNAKE_CASE = False
return options
def snake_case_( self ) -> int:
_SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
_SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
_SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy""" )
# using the PNDM scheduler by default
_SCREAMING_SNAKE_CASE = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=A , feature_extractor=A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A )
_SCREAMING_SNAKE_CASE = """A red cat sitting on a park bench"""
_SCREAMING_SNAKE_CASE = np.random.RandomState(0 )
_SCREAMING_SNAKE_CASE = pipe(
prompt=A , image=A , mask_image=A , strength=0.75 , guidance_scale=7.5 , num_inference_steps=15 , generator=A , output_type="""np""" , )
_SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1e-2
| 58 |
'''simple docstring'''
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
lowercase_ = None
lowercase_ = """<""" 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
lowercase_ = [
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'''
UpperCamelCase = True
UpperCamelCase = None
# Automatically constructed
UpperCamelCase = "PIL.Image.Image"
UpperCamelCase = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
UpperCamelCase = field(default='''Image''' , init=snake_case_ , repr=snake_case_ )
def __call__( self ) -> Tuple:
return self.pa_type
def snake_case_( self , A ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(A , A ):
_SCREAMING_SNAKE_CASE = np.array(A )
if isinstance(A , A ):
return {"path": value, "bytes": None}
elif isinstance(A , A ):
return {"path": None, "bytes": value}
elif isinstance(A , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(A )
elif isinstance(A , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(A )
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 snake_case_( self , A , A=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:
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 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(A ):
_SCREAMING_SNAKE_CASE = PIL.Image.open(A )
else:
_SCREAMING_SNAKE_CASE = path.split("""::""" )[-1]
try:
_SCREAMING_SNAKE_CASE = string_to_dict(A , config.HUB_DATASETS_URL )["""repo_id"""]
_SCREAMING_SNAKE_CASE = token_per_repo_id.get(A )
except ValueError:
_SCREAMING_SNAKE_CASE = None
with xopen(A , """rb""" , use_auth_token=A ) as f:
_SCREAMING_SNAKE_CASE = BytesIO(f.read() )
_SCREAMING_SNAKE_CASE = PIL.Image.open(bytes_ )
else:
_SCREAMING_SNAKE_CASE = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def snake_case_( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def snake_case_( self , A ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A ) , type=pa.binary() )
_SCREAMING_SNAKE_CASE = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A ) , type=pa.string() )
_SCREAMING_SNAKE_CASE = 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:
_SCREAMING_SNAKE_CASE = storage.field("""bytes""" )
else:
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
_SCREAMING_SNAKE_CASE = storage.field("""path""" )
else:
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A ) , type=pa.string() )
_SCREAMING_SNAKE_CASE = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
_SCREAMING_SNAKE_CASE = pa.array(
[encode_np_array(np.array(A ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A ) , type=pa.string() )
_SCREAMING_SNAKE_CASE = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(A , self.pa_type )
def snake_case_( self , A ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(A ):
with xopen(A , """rb""" ) as f:
_SCREAMING_SNAKE_CASE = f.read()
return bytes_
_SCREAMING_SNAKE_CASE = 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() , )
_SCREAMING_SNAKE_CASE = pa.array(
[os.path.basename(A ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
_SCREAMING_SNAKE_CASE = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(A , self.pa_type )
def lowerCamelCase ( ) ->List[str]:
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()
_SCREAMING_SNAKE_CASE = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( __lowerCamelCase : "PIL.Image.Image" ) ->bytes:
_SCREAMING_SNAKE_CASE = BytesIO()
if image.format in list_image_compression_formats():
_SCREAMING_SNAKE_CASE = image.format
else:
_SCREAMING_SNAKE_CASE = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(__lowerCamelCase , format=__lowerCamelCase )
return buffer.getvalue()
def lowerCamelCase ( __lowerCamelCase : "PIL.Image.Image" ) ->dict:
if hasattr(__lowerCamelCase , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(__lowerCamelCase )}
def lowerCamelCase ( __lowerCamelCase : np.ndarray ) ->dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
_SCREAMING_SNAKE_CASE = array.dtype
_SCREAMING_SNAKE_CASE = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
_SCREAMING_SNAKE_CASE = dtype.kind
_SCREAMING_SNAKE_CASE = dtype.itemsize
_SCREAMING_SNAKE_CASE = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
_SCREAMING_SNAKE_CASE = 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:
_SCREAMING_SNAKE_CASE = 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:
_SCREAMING_SNAKE_CASE = dtype_byteorder + dtype_kind + str(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = np.dtype(__lowerCamelCase )
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}' )
_SCREAMING_SNAKE_CASE = PIL.Image.fromarray(array.astype(__lowerCamelCase ) )
return {"path": None, "bytes": image_to_bytes(__lowerCamelCase )}
def lowerCamelCase ( __lowerCamelCase : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) ->List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = first_non_null_value(__lowerCamelCase )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(__lowerCamelCase , np.ndarray ):
_SCREAMING_SNAKE_CASE = no_op_if_value_is_null(__lowerCamelCase )
return [obj_to_image_dict_func(__lowerCamelCase ) for obj in objs]
elif isinstance(__lowerCamelCase , PIL.Image.Image ):
_SCREAMING_SNAKE_CASE = no_op_if_value_is_null(__lowerCamelCase )
return [obj_to_image_dict_func(__lowerCamelCase ) for obj in objs]
else:
return objs
else:
return objs
| 58 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase__ :Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase__ :Dict = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __a ( UpperCAmelCase ):
_a : Optional[int] = 'van'
def __init__( self , _SCREAMING_SNAKE_CASE=224 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=[7, 3, 3, 3] , _SCREAMING_SNAKE_CASE=[4, 2, 2, 2] , _SCREAMING_SNAKE_CASE=[64, 128, 320, 512] , _SCREAMING_SNAKE_CASE=[3, 3, 12, 3] , _SCREAMING_SNAKE_CASE=[8, 8, 4, 4] , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1e-6 , _SCREAMING_SNAKE_CASE=1e-2 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , **_SCREAMING_SNAKE_CASE , ) -> Dict:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = image_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = strides
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = depths
_UpperCAmelCase = mlp_ratios
_UpperCAmelCase = hidden_act
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = dropout_rate | 364 |
from __future__ import annotations
from PIL import Image
# Define glider example
lowerCAmelCase__ :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
lowerCAmelCase__ :Any = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def lowerCAmelCase__ ( a__: list[list[int]] ) -> list[list[int]]:
'''simple docstring'''
_UpperCAmelCase = []
for i in range(len(a__ ) ):
_UpperCAmelCase = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
_UpperCAmelCase = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(a__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(a__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(a__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
_UpperCAmelCase = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(a__ )
return next_generation
def lowerCAmelCase__ ( a__: list[list[int]] , a__: int ) -> list[Image.Image]:
'''simple docstring'''
_UpperCAmelCase = []
for _ in range(a__ ):
# Create output image
_UpperCAmelCase = Image.new('RGB' , (len(cells[0] ), len(a__ )) )
_UpperCAmelCase = img.load()
# Save cells to image
for x in range(len(a__ ) ):
for y in range(len(cells[0] ) ):
_UpperCAmelCase = 2_5_5 - cells[y][x] * 2_5_5
_UpperCAmelCase = (colour, colour, colour)
# Save image
images.append(a__ )
_UpperCAmelCase = new_generation(a__ )
return images
if __name__ == "__main__":
lowerCAmelCase__ :Tuple = generate_images(GLIDER, 1_6)
images[0].save('''out.gif''', save_all=True, append_images=images[1:])
| 185 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
snake_case_ : str = logging.getLogger(__name__)
def A__ ( ):
_UpperCamelCase : List[Any] = argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=UpperCAmelCase_ , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=UpperCAmelCase_ , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=UpperCAmelCase_ , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=UpperCAmelCase_ , default='data/dump' , help='The dump file prefix.' )
_UpperCamelCase : Any = parser.parse_args()
logger.info(f'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
_UpperCamelCase : Optional[int] = BertTokenizer.from_pretrained(args.tokenizer_name )
_UpperCamelCase : Optional[int] = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
_UpperCamelCase : Dict = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
_UpperCamelCase : List[Any] = RobertaTokenizer.from_pretrained(args.tokenizer_name )
_UpperCamelCase : Any = tokenizer.special_tokens_map['cls_token'] # `<s>`
_UpperCamelCase : int = tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
_UpperCamelCase : Optional[int] = GPTaTokenizer.from_pretrained(args.tokenizer_name )
_UpperCamelCase : Optional[Any] = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
_UpperCamelCase : Any = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(f'Loading text from {args.file_path}' )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
_UpperCamelCase : List[Any] = fp.readlines()
logger.info('Start encoding' )
logger.info(f'{len(UpperCAmelCase_ )} examples to process.' )
_UpperCamelCase : int = []
_UpperCamelCase : Any = 0
_UpperCamelCase : Any = 1_0_0_0_0
_UpperCamelCase : Optional[Any] = time.time()
for text in data:
_UpperCamelCase : List[Any] = f'{bos} {text.strip()} {sep}'
_UpperCamelCase : Any = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
rslt.append(UpperCAmelCase_ )
iter += 1
if iter % interval == 0:
_UpperCamelCase : Union[str, Any] = time.time()
logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
_UpperCamelCase : Tuple = time.time()
logger.info('Finished binarization' )
logger.info(f'{len(UpperCAmelCase_ )} examples processed.' )
_UpperCamelCase : Optional[int] = f'{args.dump_file}.{args.tokenizer_name}.pickle'
_UpperCamelCase : List[str] = tokenizer.vocab_size
if vocab_size < (1 << 1_6):
_UpperCamelCase : List[Any] = [np.uintaa(UpperCAmelCase_ ) for d in rslt]
else:
_UpperCamelCase : Any = [np.intaa(UpperCAmelCase_ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(f'Dump to {dp_file}' )
with open(UpperCAmelCase_ , 'wb' ) as handle:
pickle.dump(rslt_ , UpperCAmelCase_ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 83 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowercase__ ( lowercase ):
@require_torch
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_UpperCamelCase : Any = '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n '
_UpperCamelCase : Dict = '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n '
_UpperCamelCase : Optional[Any] = '\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n '
# Force fetching the files so that we can use the cache
_UpperCamelCase : Optional[Any] = 'hf-internal-testing/tiny-random-bert'
BertConfig.from_pretrained(lowerCamelCase__ )
BertModel.from_pretrained(lowerCamelCase__ )
BertTokenizer.from_pretrained(lowerCamelCase__ )
pipeline(task='fill-mask' ,model=lowerCamelCase__ )
# baseline - just load from_pretrained with normal network
_UpperCamelCase : Optional[int] = [sys.executable, '-c', '\n'.join([load, run, mock] )]
# should succeed
_UpperCamelCase : Dict = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_UpperCamelCase : str = '1'
_UpperCamelCase : Union[str, Any] = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_UpperCamelCase : Any = '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n '
_UpperCamelCase : Any = '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n '
_UpperCamelCase : Any = '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n '
# Force fetching the files so that we can use the cache
_UpperCamelCase : List[Any] = 'hf-internal-testing/tiny-random-bert'
BertConfig.from_pretrained(lowerCamelCase__ )
BertModel.from_pretrained(lowerCamelCase__ )
BertTokenizer.from_pretrained(lowerCamelCase__ )
pipeline(task='fill-mask' ,model=lowerCamelCase__ )
# baseline - just load from_pretrained with normal network
_UpperCamelCase : Union[str, Any] = [sys.executable, '-c', '\n'.join([load, run, mock] )]
# should succeed
_UpperCamelCase : List[Any] = self.get_env()
_UpperCamelCase : Dict = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
_UpperCamelCase : Optional[Any] = '\nfrom transformers import BertConfig, BertModel, BertTokenizer\n '
_UpperCamelCase : str = '\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n '
_UpperCamelCase : Any = '\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n '
# baseline - just load from_pretrained with normal network
_UpperCamelCase : Optional[int] = [sys.executable, '-c', '\n'.join([load, run] )]
# should succeed
_UpperCamelCase : Optional[Any] = self.get_env()
_UpperCamelCase : int = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
# next emulate no network
_UpperCamelCase : Dict = [sys.executable, '-c', '\n'.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_UpperCamelCase : Dict = '1'
_UpperCamelCase : Dict = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
@require_torch
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : int = '\nfrom transformers import pipeline\n '
_UpperCamelCase : str = '\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n '
_UpperCamelCase : Optional[Any] = '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n '
_UpperCamelCase : Union[str, Any] = self.get_env()
_UpperCamelCase : List[Any] = '1'
_UpperCamelCase : Tuple = [sys.executable, '-c', '\n'.join([load, mock, run] )]
_UpperCamelCase : int = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,1 ,result.stderr )
self.assertIn(
'You cannot infer task automatically within `pipeline` when using offline mode' ,result.stderr.decode().replace('\n' ,'' ) ,)
@require_torch
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = '\nfrom transformers import AutoModel\n '
_UpperCamelCase : int = '\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n '
# baseline - just load from_pretrained with normal network
_UpperCamelCase : Any = [sys.executable, '-c', '\n'.join([load, run] )]
# should succeed
_UpperCamelCase : Optional[Any] = self.get_env()
_UpperCamelCase : Optional[int] = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_UpperCamelCase : List[Any] = '1'
_UpperCamelCase : Dict = subprocess.run(lowerCamelCase__ ,env=lowerCamelCase__ ,check=lowerCamelCase__ ,capture_output=lowerCamelCase__ )
self.assertEqual(result.returncode ,0 ,result.stderr )
self.assertIn('success' ,result.stdout.decode() )
| 83 | 1 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
__A : Tuple = "sshleifer/mar_enro_6_3_student"
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
def __lowercase ( self : List[Any] ) -> Optional[Any]:
super().setUp()
lowerCAmelCase_ : Any = cached_path(
"""https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz""" , extract_compressed_file=lowerCamelCase , )
lowerCAmelCase_ : Optional[Any] = F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
@slow
@require_torch_gpu
def __lowercase ( self : str ) -> str:
MarianMTModel.from_pretrained(lowerCamelCase )
@slow
@require_torch_gpu
def __lowercase ( self : List[Any] ) -> Union[str, Any]:
lowerCAmelCase_ : str = {
"""$MAX_LEN""": 64,
"""$BS""": 64,
"""$GAS""": 1,
"""$ENRO_DIR""": self.data_dir,
"""facebook/mbart-large-cc25""": MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
"""--learning_rate=3e-5""": """--learning_rate 3e-4""",
"""--num_train_epochs 6""": """--num_train_epochs 1""",
}
# Clean up bash script
lowerCAmelCase_ : Dict = (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py""" )[1].strip()
lowerCAmelCase_ : Optional[int] = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
for k, v in env_vars_to_replace.items():
lowerCAmelCase_ : Optional[int] = bash_script.replace(lowerCamelCase , str(lowerCamelCase ) )
lowerCAmelCase_ : Optional[Any] = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
lowerCAmelCase_ : Tuple = F'\n --output_dir {output_dir}\n --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n --sortish_sampler\n --do_predict\n --gpus 1\n --freeze_encoder\n --n_train 40000\n --n_val 500\n --n_test 500\n --fp16_opt_level O1\n --num_sanity_val_steps 0\n --eval_beams 2\n '.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
lowerCAmelCase_ : Tuple = ["""finetune.py"""] + bash_script.split() + args
with patch.object(lowerCamelCase , """argv""" , lowerCamelCase ):
lowerCAmelCase_ : Optional[Any] = argparse.ArgumentParser()
lowerCAmelCase_ : Any = pl.Trainer.add_argparse_args(lowerCamelCase )
lowerCAmelCase_ : List[str] = SummarizationModule.add_model_specific_args(lowerCamelCase , os.getcwd() )
lowerCAmelCase_ : Tuple = parser.parse_args()
lowerCAmelCase_ : Dict = main(lowerCamelCase )
# Check metrics
lowerCAmelCase_ : int = load_json(model.metrics_save_path )
lowerCAmelCase_ : Optional[Any] = metrics["""val"""][0]
lowerCAmelCase_ : Tuple = metrics["""val"""][-1]
self.assertEqual(len(metrics["""val"""] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'val_avg_{model.val_metric}'] , lowerCamelCase )
self.assertGreater(last_step_stats["""val_avg_gen_time"""] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats["""val_avg_gen_time"""] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats["""val_avg_bleu"""] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
lowerCAmelCase_ : Union[str, Any] = os.listdir(lowerCamelCase )
lowerCAmelCase_ : Any = [x for x in contents if x.endswith(""".ckpt""" )][0]
lowerCAmelCase_ : Union[str, Any] = os.path.join(args.output_dir , lowerCamelCase )
lowerCAmelCase_ : int = torch.load(lowerCamelCase , map_location="""cpu""" )
lowerCAmelCase_ : List[str] = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
lowerCAmelCase_ : List[str] = {os.path.basename(lowerCamelCase ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
@timeout_decorator.timeout(6_00 )
@slow
@require_torch_gpu
def __lowercase ( self : Optional[Any] ) -> Dict:
lowerCAmelCase_ : List[str] = F'{self.test_file_dir_str}/test_data/wmt_en_ro'
lowerCAmelCase_ : Dict = {
"""--fp16_opt_level=O1""": """""",
"""$MAX_LEN""": 1_28,
"""$BS""": 16,
"""$GAS""": 1,
"""$ENRO_DIR""": data_dir,
"""$m""": """sshleifer/student_marian_en_ro_6_1""",
"""val_check_interval=0.25""": """val_check_interval=1.0""",
}
# Clean up bash script
lowerCAmelCase_ : int = (
(self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py""" )[1].strip()
)
lowerCAmelCase_ : str = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" )
lowerCAmelCase_ : Tuple = bash_script.replace("""--fp16 """ , """ """ )
for k, v in env_vars_to_replace.items():
lowerCAmelCase_ : Optional[int] = bash_script.replace(lowerCamelCase , str(lowerCamelCase ) )
lowerCAmelCase_ : int = self.get_auto_remove_tmp_dir()
lowerCAmelCase_ : Optional[Any] = bash_script.replace("""--fp16""" , """""" )
lowerCAmelCase_ : Dict = 6
lowerCAmelCase_ : List[Any] = (
["""distillation.py"""]
+ bash_script.split()
+ [
F'--output_dir={output_dir}',
"""--gpus=1""",
"""--learning_rate=1e-3""",
F'--num_train_epochs={epochs}',
"""--warmup_steps=10""",
"""--val_check_interval=1.0""",
"""--do_predict""",
]
)
with patch.object(lowerCamelCase , """argv""" , lowerCamelCase ):
lowerCAmelCase_ : Dict = argparse.ArgumentParser()
lowerCAmelCase_ : int = pl.Trainer.add_argparse_args(lowerCamelCase )
lowerCAmelCase_ : List[str] = SummarizationDistiller.add_model_specific_args(lowerCamelCase , os.getcwd() )
lowerCAmelCase_ : List[Any] = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
lowerCAmelCase_ : str = distill_main(lowerCamelCase )
# Check metrics
lowerCAmelCase_ : Union[str, Any] = load_json(model.metrics_save_path )
lowerCAmelCase_ : Union[str, Any] = metrics["""val"""][0]
lowerCAmelCase_ : Union[str, Any] = metrics["""val"""][-1]
assert len(metrics["""val"""] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'val_avg_{model.val_metric}'] , lowerCamelCase )
# check lightning ckpt can be loaded and has a reasonable statedict
lowerCAmelCase_ : Union[str, Any] = os.listdir(lowerCamelCase )
lowerCAmelCase_ : Dict = [x for x in contents if x.endswith(""".ckpt""" )][0]
lowerCAmelCase_ : Optional[int] = os.path.join(args.output_dir , lowerCamelCase )
lowerCAmelCase_ : int = torch.load(lowerCamelCase , map_location="""cpu""" )
lowerCAmelCase_ : Tuple = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
lowerCAmelCase_ : Union[str, Any] = {os.path.basename(lowerCamelCase ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics["""test"""] ) == 1
| 89 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase : List[Any] , lowerCamelCase : List[Any]=2 , lowerCamelCase : int=True , lowerCamelCase : str=False , lowerCamelCase : List[str]=10 , lowerCamelCase : Dict=3 , lowerCamelCase : str=32 * 4 , lowerCamelCase : Tuple=32 * 6 , lowerCamelCase : int=4 , lowerCamelCase : Optional[int]=32 , ) -> List[Any]:
lowerCAmelCase_ : Tuple = parent
lowerCAmelCase_ : int = batch_size
lowerCAmelCase_ : Tuple = is_training
lowerCAmelCase_ : str = use_auxiliary_loss
lowerCAmelCase_ : Optional[Any] = num_queries
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : Optional[Any] = min_size
lowerCAmelCase_ : Dict = max_size
lowerCAmelCase_ : List[Any] = num_labels
lowerCAmelCase_ : List[Any] = mask_feature_size
def __lowercase ( self : str ) -> List[Any]:
lowerCAmelCase_ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase )
lowerCAmelCase_ : int = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowerCamelCase )
lowerCAmelCase_ : List[Any] = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowerCamelCase ) > 0.5
).float()
lowerCAmelCase_ : Union[str, Any] = (torch.rand((self.batch_size, self.num_labels) , device=lowerCamelCase ) > 0.5).long()
lowerCAmelCase_ : List[Any] = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def __lowercase ( self : Optional[int] ) -> Optional[int]:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_28 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def __lowercase ( self : Tuple ) -> Optional[Any]:
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : List[Any] = self.prepare_config_and_inputs()
lowerCAmelCase_ : Union[str, Any] = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def __lowercase ( self : Tuple , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any] ) -> Union[str, Any]:
lowerCAmelCase_ : Any = output.encoder_hidden_states
lowerCAmelCase_ : Dict = output.pixel_decoder_hidden_states
lowerCAmelCase_ : str = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase ) , config.decoder_config.decoder_layers )
def __lowercase ( self : List[Any] , lowerCamelCase : Any , lowerCamelCase : int , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple=False ) -> List[Any]:
with torch.no_grad():
lowerCAmelCase_ : int = MaskFormerModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowerCAmelCase_ : List[Any] = model(pixel_values=lowerCamelCase , pixel_mask=lowerCamelCase )
lowerCAmelCase_ : Union[str, Any] = model(lowerCamelCase , output_hidden_states=lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase , lowerCamelCase )
def __lowercase ( self : Any , lowerCamelCase : Dict , lowerCamelCase : Any , lowerCamelCase : Optional[int] , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any] ) -> Dict:
lowerCAmelCase_ : Tuple = MaskFormerForInstanceSegmentation(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
def comm_check_on_output(lowerCamelCase : Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
lowerCAmelCase_ : Dict = model(pixel_values=lowerCamelCase , pixel_mask=lowerCamelCase )
lowerCAmelCase_ : List[str] = model(lowerCamelCase )
comm_check_on_output(lowerCamelCase )
lowerCAmelCase_ : Union[str, Any] = model(
pixel_values=lowerCamelCase , pixel_mask=lowerCamelCase , mask_labels=lowerCamelCase , class_labels=lowerCamelCase )
comm_check_on_output(lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __snake_case ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,unittest.TestCase):
"""simple docstring"""
lowercase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowercase = (
{'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def __lowercase ( self : Dict ) -> Optional[Any]:
lowerCAmelCase_ : Tuple = MaskFormerModelTester(self )
lowerCAmelCase_ : str = ConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase )
def __lowercase ( self : List[Any] ) -> Optional[Any]:
self.config_tester.run_common_tests()
def __lowercase ( self : Tuple ) -> List[Any]:
lowerCAmelCase_, lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(lowerCamelCase , **lowerCamelCase , output_hidden_states=lowerCamelCase )
def __lowercase ( self : Union[str, Any] ) -> str:
lowerCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowerCamelCase )
@unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
def __lowercase ( self : Any ) -> Optional[Any]:
pass
@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
def __lowercase ( self : Union[str, Any] ) -> Optional[Any]:
pass
@unittest.skip(reason="""MaskFormer is not a generative model""" )
def __lowercase ( self : Any ) -> str:
pass
@unittest.skip(reason="""MaskFormer does not use token embeddings""" )
def __lowercase ( self : List[Any] ) -> Any:
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def __lowercase ( self : List[str] ) -> Optional[int]:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __lowercase ( self : str ) -> Optional[int]:
pass
def __lowercase ( self : Tuple ) -> List[str]:
lowerCAmelCase_, lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[Any] = model_class(lowerCamelCase )
lowerCAmelCase_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : List[str] = [*signature.parameters.keys()]
lowerCAmelCase_ : int = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
@slow
def __lowercase ( self : Optional[int] ) -> List[str]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
lowerCAmelCase_ : List[str] = MaskFormerModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def __lowercase ( self : Dict ) -> int:
lowerCAmelCase_ : Any = (self.model_tester.min_size,) * 2
lowerCAmelCase_ : Dict = {
"""pixel_values""": torch.randn((2, 3, *size) , device=lowerCamelCase ),
"""mask_labels""": torch.randn((2, 10, *size) , device=lowerCamelCase ),
"""class_labels""": torch.zeros(2 , 10 , device=lowerCamelCase ).long(),
}
lowerCAmelCase_ : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(lowerCamelCase )
lowerCAmelCase_ : str = model(**lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def __lowercase ( self : int ) -> int:
lowerCAmelCase_, lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(lowerCamelCase , **lowerCamelCase , output_hidden_states=lowerCamelCase )
def __lowercase ( self : Dict ) -> List[str]:
lowerCAmelCase_, lowerCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = model_class(lowerCamelCase ).to(lowerCamelCase )
lowerCAmelCase_ : Tuple = model(**lowerCamelCase , output_attentions=lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def __lowercase ( self : int ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
lowerCAmelCase_ : Optional[int] = self.all_model_classes[1]
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase_ : Union[str, Any] = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.train()
lowerCAmelCase_ : str = model(lowerCamelCase , mask_labels=lowerCamelCase , class_labels=lowerCamelCase ).loss
loss.backward()
def __lowercase ( self : Any ) -> Union[str, Any]:
# only MaskFormerForInstanceSegmentation has the loss
lowerCAmelCase_ : List[str] = self.all_model_classes[1]
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : List[str] = True
lowerCAmelCase_ : Dict = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.train()
lowerCAmelCase_ : Union[str, Any] = model(lowerCamelCase , mask_labels=lowerCamelCase , class_labels=lowerCamelCase )
lowerCAmelCase_ : Optional[Any] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
lowerCAmelCase_ : List[str] = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
lowerCAmelCase_ : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
lowerCAmelCase_ : Tuple = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A : List[Any] = 1E-4
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __snake_case ( unittest.TestCase):
"""simple docstring"""
@cached_property
def __lowercase ( self : Union[str, Any] ) -> List[Any]:
return (
MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
if is_vision_available()
else None
)
def __lowercase ( self : List[Any] ) -> Union[str, Any]:
lowerCAmelCase_ : Optional[int] = MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(lowerCamelCase )
lowerCAmelCase_ : Dict = self.default_image_processor
lowerCAmelCase_ : Optional[int] = prepare_img()
lowerCAmelCase_ : Dict = image_processor(lowerCamelCase , return_tensors="""pt""" ).to(lowerCamelCase )
lowerCAmelCase_ : Union[str, Any] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**lowerCamelCase )
lowerCAmelCase_ : Any = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
lowerCAmelCase_ : Union[str, Any] = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
lowerCAmelCase_ : Optional[Any] = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
def __lowercase ( self : Optional[int] ) -> Optional[int]:
lowerCAmelCase_ : Union[str, Any] = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(lowerCamelCase )
.eval()
)
lowerCAmelCase_ : Union[str, Any] = self.default_image_processor
lowerCAmelCase_ : Optional[Any] = prepare_img()
lowerCAmelCase_ : Optional[int] = image_processor(lowerCamelCase , return_tensors="""pt""" ).to(lowerCamelCase )
lowerCAmelCase_ : Optional[int] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowerCAmelCase_ : List[str] = model(**lowerCamelCase )
# masks_queries_logits
lowerCAmelCase_ : Dict = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
lowerCAmelCase_ : Any = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
lowerCAmelCase_ : Optional[int] = torch.tensor(lowerCamelCase ).to(lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
# class_queries_logits
lowerCAmelCase_ : Optional[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowerCAmelCase_ : Tuple = torch.tensor(
[
[1.6_512E00, -5.2_572E00, -3.3_519E00],
[3.6_169E-02, -5.9_025E00, -2.9_313E00],
[1.0_766E-04, -7.7_630E00, -5.1_263E00],
] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
def __lowercase ( self : Optional[int] ) -> List[str]:
lowerCAmelCase_ : List[str] = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
.to(lowerCamelCase )
.eval()
)
lowerCAmelCase_ : Tuple = self.default_image_processor
lowerCAmelCase_ : int = prepare_img()
lowerCAmelCase_ : Any = image_processor(lowerCamelCase , return_tensors="""pt""" ).to(lowerCamelCase )
lowerCAmelCase_ : List[str] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase , (1, 3, 8_00, 10_88) )
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = model(**lowerCamelCase )
# masks_queries_logits
lowerCAmelCase_ : List[Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
lowerCAmelCase_ : Optional[Any] = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
lowerCAmelCase_ : Union[str, Any] = torch.tensor(lowerCamelCase ).to(lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
# class_queries_logits
lowerCAmelCase_ : Dict = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowerCAmelCase_ : int = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase , atol=lowerCamelCase ) )
def __lowercase ( self : Union[str, Any] ) -> List[str]:
lowerCAmelCase_ : Dict = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(lowerCamelCase )
.eval()
)
lowerCAmelCase_ : List[str] = self.default_image_processor
lowerCAmelCase_ : int = image_processor(
[np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )] , segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )] , return_tensors="""pt""" , )
lowerCAmelCase_ : List[str] = inputs["""pixel_values"""].to(lowerCamelCase )
lowerCAmelCase_ : Tuple = [el.to(lowerCamelCase ) for el in inputs["""mask_labels"""]]
lowerCAmelCase_ : Union[str, Any] = [el.to(lowerCamelCase ) for el in inputs["""class_labels"""]]
with torch.no_grad():
lowerCAmelCase_ : Any = model(**lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 89 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a = logging.get_logger(__name__)
a = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = 'dpt'
def __init__( self : int , lowerCAmelCase : List[str]=768 , lowerCAmelCase : Optional[int]=12 , lowerCAmelCase : Any=12 , lowerCAmelCase : str=3072 , lowerCAmelCase : Union[str, Any]="gelu" , lowerCAmelCase : Optional[int]=0.0 , lowerCAmelCase : Union[str, Any]=0.0 , lowerCAmelCase : str=0.02 , lowerCAmelCase : str=1e-12 , lowerCAmelCase : Optional[Any]=384 , lowerCAmelCase : str=16 , lowerCAmelCase : int=3 , lowerCAmelCase : Tuple=False , lowerCAmelCase : Any=True , lowerCAmelCase : Tuple=[2, 5, 8, 11] , lowerCAmelCase : Tuple="project" , lowerCAmelCase : Optional[int]=[4, 2, 1, 0.5] , lowerCAmelCase : Any=[96, 192, 384, 768] , lowerCAmelCase : int=256 , lowerCAmelCase : List[Any]=-1 , lowerCAmelCase : Any=False , lowerCAmelCase : int=True , lowerCAmelCase : List[str]=0.4 , lowerCAmelCase : Dict=255 , lowerCAmelCase : int=0.1 , lowerCAmelCase : List[Any]=[1, 1024, 24, 24] , lowerCAmelCase : str=[0, 1] , lowerCAmelCase : str=None , **lowerCAmelCase : Optional[Any] , ):
super().__init__(**lowerCAmelCase )
lowerCAmelCase = hidden_size
lowerCAmelCase = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("""Initializing the config with a `BiT` backbone.""" )
lowerCAmelCase = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
}
lowerCAmelCase = BitConfig(**lowerCAmelCase )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
logger.info("""Initializing the config with a `BiT` backbone.""" )
lowerCAmelCase = BitConfig(**lowerCAmelCase )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
lowerCAmelCase = backbone_config
else:
raise ValueError(
f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )
lowerCAmelCase = backbone_featmap_shape
lowerCAmelCase = neck_ignore_stages
if readout_type != "project":
raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""" )
else:
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = []
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = image_size
lowerCAmelCase = patch_size
lowerCAmelCase = num_channels
lowerCAmelCase = qkv_bias
lowerCAmelCase = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""" )
lowerCAmelCase = readout_type
lowerCAmelCase = reassemble_factors
lowerCAmelCase = neck_hidden_sizes
lowerCAmelCase = fusion_hidden_size
lowerCAmelCase = head_in_index
lowerCAmelCase = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
lowerCAmelCase = use_auxiliary_head
lowerCAmelCase = auxiliary_loss_weight
lowerCAmelCase = semantic_loss_ignore_index
lowerCAmelCase = semantic_classifier_dropout
def __lowercase ( self : Any ):
lowerCAmelCase = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
lowerCAmelCase = self.backbone_config.to_dict()
lowerCAmelCase = self.__class__.model_type
return output
| 155 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_a )
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = field(default='audio-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
_a = Features({'audio': Audio()} )
_a = Features({'labels': ClassLabel} )
_a = "audio"
_a = "labels"
def __lowercase ( self : List[str] , lowerCAmelCase : Optional[int] ):
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , lowerCAmelCase ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
lowerCAmelCase = copy.deepcopy(self )
lowerCAmelCase = self.label_schema.copy()
lowerCAmelCase = features[self.label_column]
lowerCAmelCase = label_schema
return task_template
@property
def __lowercase ( self : List[str] ):
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 155 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : int = '''new-model'''
if is_tf_available():
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = NewModelConfig
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Optional[int] = "bert-base-cased"
SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Tuple ):
SCREAMING_SNAKE_CASE : List[str] = "bert-base-cased"
SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Dict = TFAutoModelForPreTraining.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : List[str] ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForCausalLM.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Optional[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Dict = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : int ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Dict = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForMaskedLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Tuple ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
SCREAMING_SNAKE_CASE : Tuple = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = TFAutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
def _A ( self : List[str] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModelForQuestionAnswering.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
@slow
@require_tensorflow_probability
def _A ( self : Optional[Any] ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForTableQuestionAnswering.from_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForTableQuestionAnswering.from_pretrained(
UpperCAmelCase_ , output_loading_info=UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Any ):
SCREAMING_SNAKE_CASE : List[str] = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCAmelCase_ ) , 1_4410 )
def _A ( self : int ):
SCREAMING_SNAKE_CASE : int = TFAutoModelWithLMHead.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=UpperCAmelCase_ ) , 1_4410 )
def _A ( self : int ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
SCREAMING_SNAKE_CASE : Tuple = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny" )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = copy.deepcopy(model.config )
SCREAMING_SNAKE_CASE : Optional[Any] = ["FunnelBaseModel"]
SCREAMING_SNAKE_CASE : str = TFAutoModel.from_config(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = TFAutoModel.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def _A ( self : Optional[int] ):
try:
AutoConfig.register("new-model" , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(UpperCAmelCase_ ):
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_ )
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCAmelCase_ ):
auto_class.register(UpperCAmelCase_ , UpperCAmelCase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE : Tuple = BertModelTester(self ).get_config()
SCREAMING_SNAKE_CASE : Optional[int] = NewModelConfig(**tiny_config.to_dict() )
SCREAMING_SNAKE_CASE : Optional[Any] = auto_class.from_config(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Any = auto_class.from_pretrained(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def _A ( self : Optional[int] ):
with self.assertRaisesRegex(
UpperCAmelCase_ , "bert-base is not a local folder and is not a valid model identifier" ):
SCREAMING_SNAKE_CASE : Dict = TFAutoModel.from_pretrained("bert-base" )
def _A ( self : Dict ):
with self.assertRaisesRegex(
UpperCAmelCase_ , r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModel.from_pretrained(UpperCAmelCase_ , revision="aaaaaa" )
def _A ( self : Tuple ):
with self.assertRaisesRegex(
UpperCAmelCase_ , "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin" , ):
SCREAMING_SNAKE_CASE : Dict = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model" )
def _A ( self : Optional[int] ):
with self.assertRaisesRegex(UpperCAmelCase_ , "Use `from_pt=True` to load this model" ):
SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only" )
def _A ( self : Tuple ):
# Make sure we have cached the model.
SCREAMING_SNAKE_CASE : int = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert" )
with RequestCounter() as counter:
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
SCREAMING_SNAKE_CASE : Any = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded" )
with RequestCounter() as counter:
SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 319 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 319 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
snake_case : str = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
snake_case : Union[str, Any] = parser.parse_args()
snake_case : Dict = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
snake_case : Tuple = CLIPImageProcessor()
snake_case : Any = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
snake_case : List[str] = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 281 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class _snake_case :
@staticmethod
def SCREAMING_SNAKE_CASE ( *_a , **_a ):
pass
def lowerCAmelCase_ ( _snake_case : Image ) -> str:
'''simple docstring'''
__magic_name__ : Optional[int] = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def lowerCAmelCase_ ( _snake_case : Image ) -> Dict:
'''simple docstring'''
__magic_name__ : List[Any] = np.array(_snake_case )
__magic_name__ : Optional[int] = npimg.shape
return {"hash": hashimage(_snake_case ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class _snake_case ( unittest.TestCase ):
UpperCamelCase__ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
UpperCamelCase__ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
__magic_name__ : Dict = MaskGenerationPipeline(model=_a , image_processor=_a )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def SCREAMING_SNAKE_CASE ( self ):
pass
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = pipeline("mask-generation" , model="facebook/sam-vit-huge" )
__magic_name__ : str = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Dict = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
{"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
{"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
{"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
{"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
{"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
{"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
{"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
{"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
{"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
{"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
{"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
{"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
{"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
{"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
{"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
{"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
{"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
{"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
{"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
{"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
{"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
{"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
{"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
] , )
# fmt: on
@require_torch
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : str = "facebook/sam-vit-huge"
__magic_name__ : str = pipeline("mask-generation" , model=_a )
__magic_name__ : Tuple = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
__magic_name__ : Any = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(_a ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(_a , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
] , )
| 281 | 1 |
'''simple docstring'''
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 import BertTokenizer
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : List[Any] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_lowercase : Optional[Any] = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
_lowercase : Any = {
"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 : Optional[Any] = {
"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 : Optional[int] = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
_lowercase : Optional[int] = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
_lowercase : Tuple = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
_lowercase : List[Any] = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowercase : Dict = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowercase : List[str] = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class __magic_name__ ( _UpperCAmelCase ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class __magic_name__ ( _UpperCAmelCase ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_lowercase : Optional[int] = collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
_lowercase : Union[str, Any] = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
_lowercase : int = r"\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `'tf'`: Return TensorFlow `tf.constant` objects.\n - `'pt'`: Return PyTorch `torch.Tensor` objects.\n - `'np'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer's default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n "
@add_start_docstrings(_UpperCAmelCase )
class __magic_name__ :
def __call__( self : int , lowercase_ : int , lowercase_ : Optional[str] = None , lowercase_ : Optional[str] = None , lowercase_ : Union[bool, str] = False , lowercase_ : Union[bool, str] = False , lowercase_ : Optional[int] = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : Optional[bool] = None , **lowercase_ : int , ):
if titles is None and texts is None:
return super().__call__(
lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , return_tensors=lowercase_ , return_attention_mask=lowercase_ , **lowercase_ , )
elif titles is None or texts is None:
lowercase_ : Tuple = titles if texts is None else texts
return super().__call__(
lowercase_ , lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , return_tensors=lowercase_ , return_attention_mask=lowercase_ , **lowercase_ , )
lowercase_ : List[Any] = titles if not isinstance(lowercase_ , lowercase_ ) else [titles]
lowercase_ : Dict = texts if not isinstance(lowercase_ , lowercase_ ) else [texts]
lowercase_ : List[Any] = len(lowercase_ )
lowercase_ : Optional[Any] = questions if not isinstance(lowercase_ , lowercase_ ) else [questions] * n_passages
if len(lowercase_ ) != len(lowercase_ ):
raise ValueError(
f'''There should be as many titles than texts but got {len(lowercase_ )} titles and {len(lowercase_ )} texts.''' )
lowercase_ : Any = super().__call__(lowercase_ , lowercase_ , padding=lowercase_ , truncation=lowercase_ )["""input_ids"""]
lowercase_ : str = super().__call__(lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ )["""input_ids"""]
lowercase_ : List[Any] = {
"""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(lowercase_ , lowercase_ )
]
}
if return_attention_mask is not False:
lowercase_ : int = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
lowercase_ : int = attention_mask
return self.pad(lowercase_ , padding=lowercase_ , max_length=lowercase_ , return_tensors=lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : BatchEncoding , lowercase_ : DPRReaderOutput , lowercase_ : int = 16 , lowercase_ : int = 64 , lowercase_ : int = 4 , ):
lowercase_ : Optional[int] = reader_input["""input_ids"""]
lowercase_ : Optional[int] = reader_output[:3]
lowercase_ : Union[str, Any] = len(lowercase_ )
lowercase_ : List[Any] = sorted(range(lowercase_ ) , reverse=lowercase_ , key=relevance_logits.__getitem__ )
lowercase_ : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
lowercase_ : Optional[Any] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
lowercase_ : Any = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
lowercase_ : Any = sequence_ids.index(self.pad_token_id )
else:
lowercase_ : Dict = len(lowercase_ )
lowercase_ : Any = 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=lowercase_ , top_spans=lowercase_ , )
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=lowercase_ , start_index=lowercase_ , end_index=lowercase_ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(lowercase_ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[int] , lowercase_ : List[int] , lowercase_ : int , lowercase_ : int , ):
lowercase_ : Tuple = []
for start_index, start_score in enumerate(lowercase_ ):
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) )
lowercase_ : str = sorted(lowercase_ , key=lambda lowercase_ : x[1] , reverse=lowercase_ )
lowercase_ : Dict = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]''' )
lowercase_ : Optional[int] = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(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(lowercase_ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(_UpperCAmelCase )
class __magic_name__ ( _UpperCAmelCase, _UpperCAmelCase ):
UpperCamelCase__ = VOCAB_FILES_NAMES
UpperCamelCase__ = READER_PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ = READER_PRETRAINED_INIT_CONFIGURATION
UpperCamelCase__ = ['''input_ids''', '''attention_mask''']
| 362 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowercase_ : int = """""".join(UpperCAmelCase__ )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution())
| 21 | 0 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_A : List[str] =datasets.utils.logging.get_logger(__name__)
class _lowercase ( folder_based_builder.FolderBasedBuilderConfig ):
a = None
a = None
class _lowercase ( folder_based_builder.FolderBasedBuilder ):
a = datasets.Audio()
a = """audio"""
a = AudioFolderConfig
a = 42 # definition at the bottom of the script
a = AudioClassification(audio_column="""audio""" , label_column="""label""" )
_A : Any =[
'''.aiff''',
'''.au''',
'''.avr''',
'''.caf''',
'''.flac''',
'''.htk''',
'''.svx''',
'''.mat4''',
'''.mat5''',
'''.mpc2k''',
'''.ogg''',
'''.paf''',
'''.pvf''',
'''.raw''',
'''.rf64''',
'''.sd2''',
'''.sds''',
'''.ircam''',
'''.voc''',
'''.w64''',
'''.wav''',
'''.nist''',
'''.wavex''',
'''.wve''',
'''.xi''',
'''.mp3''',
'''.opus''',
]
_A : Optional[int] =AUDIO_EXTENSIONS
| 41 |
'''simple docstring'''
from __future__ import annotations
_A : Any ={
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
class _lowercase :
def __init__( self: Tuple , UpperCamelCase__: dict[str, list[str]] , UpperCamelCase__: str ):
lowerCamelCase__ : str = graph
# mapping node to its parent in resulting breadth first tree
lowerCamelCase__ : dict[str, str | None] = {}
lowerCamelCase__ : Any = source_vertex
def lowerCamelCase_ ( self: List[str] ):
lowerCamelCase__ : List[str] = {self.source_vertex}
lowerCamelCase__ : List[str] = None
lowerCamelCase__ : Tuple = [self.source_vertex] # first in first out queue
while queue:
lowerCamelCase__ : Tuple = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(UpperCamelCase__ )
lowerCamelCase__ : List[str] = vertex
queue.append(UpperCamelCase__ )
def lowerCamelCase_ ( self: str , UpperCamelCase__: str ):
if target_vertex == self.source_vertex:
return self.source_vertex
lowerCamelCase__ : Tuple = self.parent.get(UpperCamelCase__ )
if target_vertex_parent is None:
lowerCamelCase__ : int = (
F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
)
raise ValueError(UpperCamelCase__ )
return self.shortest_path(UpperCamelCase__ ) + F'''->{target_vertex}'''
if __name__ == "__main__":
_A : int =Graph(graph, '''G''')
g.breath_first_search()
print(g.shortest_path('''D'''))
print(g.shortest_path('''G'''))
print(g.shortest_path('''Foo'''))
| 41 | 1 |
def __UpperCamelCase ( lowercase__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = abs(lowercase__ )
lowerCAmelCase_ : int = 0
while n > 0:
res += n % 10
n //= 10
return res
def __UpperCamelCase ( lowercase__ : int ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = abs(lowercase__ )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def __UpperCamelCase ( lowercase__ : int ) -> int:
'''simple docstring'''
return sum(int(lowercase__ ) for c in str(abs(lowercase__ ) ) )
def __UpperCamelCase ( ) -> None:
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(lowercase__ : Callable , lowercase__ : int ) -> None:
lowerCAmelCase_ : Union[str, Any] = f'{func.__name__}({value})'
lowerCAmelCase_ : Any = timeit(f'__main__.{call}' , setup="""import __main__""" )
print(f'{call:56} = {func(lowercase__ )} -- {timing:.4f} seconds' )
for value in (262144, 1125899906842624, 1267650600228229401496703205376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(lowercase__ , lowercase__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 28 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __a ( unittest.TestCase ):
def A ( self : List[Any] ):
lowerCAmelCase_ : Dict = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
lowerCAmelCase_ : Optional[Any] = Vector()
def A ( self : List[str] ):
lowerCAmelCase_ : Tuple = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(UpperCAmelCase ) , """(0,0,0,0,0,1)""" )
def A ( self : Any ):
lowerCAmelCase_ : Union[str, Any] = Vector([1, 2, 3, 4] )
self.assertEqual(len(UpperCAmelCase ) , 4 )
def A ( self : Dict ):
lowerCAmelCase_ : Dict = Vector([1, 2] )
lowerCAmelCase_ : str = Vector([1, 2, 3, 4, 5] )
lowerCAmelCase_ : Optional[int] = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
lowerCAmelCase_ : Dict = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def A ( self : Optional[Any] ):
lowerCAmelCase_ : Optional[int] = Vector([1, 2, 3] )
lowerCAmelCase_ : Union[str, Any] = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def A ( self : Optional[Any] ):
lowerCAmelCase_ : Optional[Any] = Vector([1, 2, 3] )
lowerCAmelCase_ : Dict = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def A ( self : Union[str, Any] ):
lowerCAmelCase_ : Dict = Vector([1, 2, 3] )
lowerCAmelCase_ : Optional[int] = Vector([2, -1, 4] ) # for test of dot product
lowerCAmelCase_ : str = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , """(3.0,6.0,9.0)""" )
self.assertEqual((a * b) , 0 )
def A ( self : List[str] ):
self.assertEqual(str(zero_vector(10 ) ).count("""0""" ) , 10 )
def A ( self : Tuple ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , """(0,1,0)""" )
def A ( self : Optional[Any] ):
lowerCAmelCase_ : Optional[Any] = Vector([1, 2, 3] )
lowerCAmelCase_ : Union[str, Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , UpperCAmelCase , UpperCAmelCase ) ) , """(3,4,7)""" )
def A ( self : Optional[int] ):
lowerCAmelCase_ : List[Any] = Vector([1, 0, 0, 0, 0, 0] )
lowerCAmelCase_ : int = x.copy()
self.assertEqual(str(UpperCAmelCase ) , str(UpperCAmelCase ) )
def A ( self : Union[str, Any] ):
lowerCAmelCase_ : Union[str, Any] = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(UpperCAmelCase ) , """(0,1,0)""" )
def A ( self : Any ):
lowerCAmelCase_ : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(UpperCAmelCase ) )
def A ( self : Optional[int] ):
lowerCAmelCase_ : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCAmelCase_ : List[str] = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(UpperCAmelCase , UpperCAmelCase ) )
def A ( self : Tuple ):
lowerCAmelCase_ : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCAmelCase_ : Union[str, Any] = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(UpperCAmelCase , UpperCAmelCase ) )
def A ( self : Optional[int] ):
lowerCAmelCase_ : Optional[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def A ( self : Optional[int] ):
lowerCAmelCase_ : Dict = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
lowerCAmelCase_ : Any = Vector([1, 2, 3] )
self.assertEqual("""(14,32,50)""" , str(a * x ) )
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2 ) )
def A ( self : Tuple ):
lowerCAmelCase_ : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(UpperCAmelCase ) )
def A ( self : Optional[int] ):
lowerCAmelCase_ : str = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def A ( self : Dict ):
lowerCAmelCase_ : Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCAmelCase_ : Optional[int] = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b ) )
def A ( self : Union[str, Any] ):
lowerCAmelCase_ : str = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
lowerCAmelCase_ : Optional[int] = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b ) )
def A ( self : Optional[int] ):
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 28 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
SCREAMING_SNAKE_CASE :Dict = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n'
SCREAMING_SNAKE_CASE :List[Any] = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n'
SCREAMING_SNAKE_CASE :Dict = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n'
SCREAMING_SNAKE_CASE :Union[str, Any] = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n'
SCREAMING_SNAKE_CASE :List[Any] = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : Optional[Any] ):
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" ) ),
"references": datasets.Value("string" ),
} ) ,homepage="https://github.com/openai/human-eval" ,codebase_urls=["https://github.com/openai/human-eval"] ,reference_urls=["https://github.com/openai/human-eval"] ,license=_LICENSE ,)
def UpperCamelCase_ ( self : str ,A : int ,A : Union[str, Any] ,A : Optional[int]=[1, 10, 1_00] ,A : Union[str, Any]=4 ,A : Dict=3.0 ):
if os.getenv("HF_ALLOW_CODE_EVAL" ,0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError("This metric is currently not supported on Windows." )
with ThreadPoolExecutor(max_workers=A ) as executor:
__A = []
__A = Counter()
__A = 0
__A = defaultdict(A )
for task_id, (candidates, test_case) in enumerate(zip(A ,A ) ):
for candidate in candidates:
__A = candidate + "\n" + test_case
__A = (test_program, timeout, task_id, completion_id[task_id])
__A = executor.submit(A ,*A )
futures.append(A )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(A ):
__A = future.result()
results[result["task_id"]].append((result["completion_id"], result) )
__A , __A = [], []
for result in results.values():
result.sort()
__A = [r[1]["passed"] for r in result]
total.append(len(A ) )
correct.append(sum(A ) )
__A = np.array(A )
__A = np.array(A )
__A = k
__A = {f'''pass@{k}''': estimate_pass_at_k(A ,A ,A ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def UpperCAmelCase ( a_ , a_ , a_ ) -> Union[str, Any]:
"""simple docstring"""
def estimator(a_ , a_ , a_ ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(a_ , a_ ):
__A = itertools.repeat(a_ , len(a_ ) )
else:
assert len(a_ ) == len(a_ )
__A = iter(a_ )
return np.array([estimator(int(a_ ) , int(a_ ) , a_ ) for n, c in zip(a_ , a_ )] )
| 15 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , a_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
__A , __A = arr[k - 1], arr[i]
else: # k is odd
__A , __A = arr[k - 1], arr[0]
generate(k - 1 , a_ )
generate(len(a_ ) , a_ )
return res
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :int = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :Dict = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 15 | 1 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 104 |
'''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 _A (lowerCAmelCase__ :int , lowerCAmelCase__ :Any , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Dict ) -> Optional[int]:
'''simple docstring'''
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_a = np.full((len(lowerCAmelCase__ ), sequence_length, 2) , lowerCAmelCase__ )
else:
_a = np.full((len(lowerCAmelCase__ ), sequence_length) , lowerCAmelCase__ )
for i, tensor in enumerate(lowerCAmelCase__ ):
if padding_side == "right":
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_a = tensor[:sequence_length]
else:
_a = tensor[:sequence_length]
else:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_a = tensor[:sequence_length]
else:
_a = tensor[:sequence_length]
return out_tensor.tolist()
def _A (lowerCAmelCase__ :Any ) -> Union[str, Any]:
'''simple docstring'''
_a = ord(lowerCAmelCase__ )
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
_a = unicodedata.category(lowerCAmelCase__ )
if cat.startswith('P' ):
return True
return False
@dataclass
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = 42
_lowerCAmelCase = True
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = -1_0_0
_lowerCAmelCase = "pt"
def __UpperCAmelCase ( self , __magic_name__ ) -> Any:
import torch
_a = 'label' if 'label' in features[0].keys() else 'labels'
_a = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
_a = self.tokenizer.pad(
__magic_name__ , 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
_a = torch.tensor(batch['entity_ids'] ).shape[1]
_a = self.tokenizer.padding_side
if padding_side == "right":
_a = [
list(__magic_name__ ) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__ )) for label in labels
]
else:
_a = [
[self.label_pad_token_id] * (sequence_length - len(__magic_name__ )) + list(__magic_name__ ) for label in labels
]
_a = [feature['ner_tags'] for feature in features]
_a = padding_tensor(__magic_name__ , -1 , __magic_name__ , __magic_name__ )
_a = [feature['original_entity_spans'] for feature in features]
_a = padding_tensor(__magic_name__ , (-1, -1) , __magic_name__ , __magic_name__ )
_a = {k: torch.tensor(__magic_name__ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 104 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def lowerCamelCase ( SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE=100 , SCREAMING_SNAKE_CASE=1_026 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl" , SCREAMING_SNAKE_CASE="igf_context_pairs.jbl" , ):
'''simple docstring'''
set_seed(3 )
# generate train_data and objective_set
__UpperCamelCase , __UpperCamelCase :Optional[Any] = generate_datasets(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , number=SCREAMING_SNAKE_CASE , min_len=1_026 , trim=SCREAMING_SNAKE_CASE )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
__UpperCamelCase :List[Any] = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
# load pretrained model
__UpperCamelCase :str = load_gpta('''gpt2''' ).to(SCREAMING_SNAKE_CASE )
print('''computing perplexity on objective set''' )
__UpperCamelCase :List[str] = compute_perplexity(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).item()
print('''perplexity on objective set:''' , SCREAMING_SNAKE_CASE )
# collect igf pairs and save to file demo.jbl
collect_objective_set(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=15 , SCREAMING_SNAKE_CASE=128 , SCREAMING_SNAKE_CASE=100 , SCREAMING_SNAKE_CASE="igf_model.pt" , ):
'''simple docstring'''
set_seed(42 )
# Load pre-trained model
__UpperCamelCase :str = GPTaLMHeadModel.from_pretrained('''gpt2''' )
# Initialize secondary learner to use embedding weights of model
__UpperCamelCase :List[str] = SecondaryLearner(SCREAMING_SNAKE_CASE )
# Train secondary learner
__UpperCamelCase :Tuple = train_secondary_learner(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , max_epochs=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE , eval_freq=100 , igf_model_path=SCREAMING_SNAKE_CASE , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=1_000 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=recopy_gpta , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE="gpt2_finetuned.pt" , ):
'''simple docstring'''
__UpperCamelCase :List[Any] = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
__UpperCamelCase :Tuple = RandomSampler(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Union[str, Any] = DataLoader(SCREAMING_SNAKE_CASE , sampler=SCREAMING_SNAKE_CASE )
__UpperCamelCase :List[Any] = max_steps // (len(SCREAMING_SNAKE_CASE )) + 1
__UpperCamelCase :Optional[int] = 0
__UpperCamelCase :int = torch.zeros((1, context_len) , dtype=torch.long , device=SCREAMING_SNAKE_CASE )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase :List[str] = recopy_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
model.train()
if secondary_learner is not None:
secondary_learner.to(SCREAMING_SNAKE_CASE )
secondary_learner.eval()
__UpperCamelCase :List[str] = []
__UpperCamelCase :str = 0
__UpperCamelCase :int = []
__UpperCamelCase :int = []
# Compute the performance of the transformer model at the beginning
__UpperCamelCase :List[str] = compute_perplexity(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
test_perps.append(SCREAMING_SNAKE_CASE )
print('''Test perplexity, step''' , SCREAMING_SNAKE_CASE , ''':''' , SCREAMING_SNAKE_CASE )
for epoch in range(int(SCREAMING_SNAKE_CASE ) ):
for step, example in enumerate(SCREAMING_SNAKE_CASE ):
torch.cuda.empty_cache()
__UpperCamelCase :Optional[Any] = random.randint(0 , example.size(2 ) - context_len - 1 )
__UpperCamelCase :Tuple = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
__UpperCamelCase :List[str] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
__UpperCamelCase :Any = True
if secondary_learner is not None:
__UpperCamelCase :List[Any] = secondary_learner.forward(
torch.tensor(SCREAMING_SNAKE_CASE , dtype=torch.long , device=SCREAMING_SNAKE_CASE ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(SCREAMING_SNAKE_CASE ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
__UpperCamelCase :List[Any] = -1
if predicted_q < threshold:
__UpperCamelCase :List[str] = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
__UpperCamelCase :int = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
__UpperCamelCase :Any = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
__UpperCamelCase :Tuple = compute_perplexity(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
test_perps.append(SCREAMING_SNAKE_CASE )
print('''Test perplexity, step''' , SCREAMING_SNAKE_CASE , ''':''' , SCREAMING_SNAKE_CASE )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :List[str] = argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task''' )
# Required parameters
parser.add_argument(
'''--data_dir''' , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help='''The input data dir. Should contain data files for WikiText.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--data_file''' , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help=(
'''A jbl file containing tokenized data which can be split as objective dataset, '''
'''train_dataset and test_dataset.'''
) , )
parser.add_argument(
'''--igf_data_file''' , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help='''A jbl file containing the context and information gain pairs to train secondary learner.''' , )
parser.add_argument(
'''--output_dir''' , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help='''The output directory where the final fine-tuned model is stored.''' , )
parser.add_argument(
'''--tokenizer_name''' , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument('''--seed''' , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help='''A seed for reproducible training.''' )
parser.add_argument(
'''--context_len''' , default=32 , type=SCREAMING_SNAKE_CASE , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--size_objective_set''' , default=100 , type=SCREAMING_SNAKE_CASE , help='''number of articles that are long enough to be used as our objective set''' , )
parser.add_argument(
'''--eval_freq''' , default=100 , type=SCREAMING_SNAKE_CASE , help='''secondary model evaluation is triggered at eval_freq''' )
parser.add_argument('''--max_steps''' , default=1_000 , type=SCREAMING_SNAKE_CASE , help='''To calculate training epochs''' )
parser.add_argument(
'''--secondary_learner_batch_size''' , default=128 , type=SCREAMING_SNAKE_CASE , help='''batch size of training data for secondary learner''' , )
parser.add_argument(
'''--batch_size''' , default=16 , type=SCREAMING_SNAKE_CASE , help='''batch size of training data of language model(gpt2) ''' )
parser.add_argument(
'''--eval_interval''' , default=10 , type=SCREAMING_SNAKE_CASE , help=(
'''decay the selectivity of our secondary learner filter from'''
'''1 standard deviation above average to 1 below average after 10 batches'''
) , )
parser.add_argument(
'''--number''' , default=100 , type=SCREAMING_SNAKE_CASE , help='''The number of examples split to be used as objective_set/test_data''' )
parser.add_argument(
'''--min_len''' , default=1_026 , type=SCREAMING_SNAKE_CASE , help='''The minimum length of the article to be used as objective set''' )
parser.add_argument(
'''--secondary_learner_max_epochs''' , default=15 , type=SCREAMING_SNAKE_CASE , help='''number of epochs to train secondary learner''' )
parser.add_argument('''--trim''' , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , help='''truncate the example if it exceeds context length''' )
parser.add_argument(
'''--threshold''' , default=1.0 , type=SCREAMING_SNAKE_CASE , help=(
'''The threshold value used by secondary learner to filter the train_data and allow only'''
''' informative data as input to the model'''
) , )
parser.add_argument('''--finetuned_model_name''' , default='''gpt2_finetuned.pt''' , type=SCREAMING_SNAKE_CASE , help='''finetuned_model_name''' )
parser.add_argument(
'''--recopy_model''' , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , help='''Reset the model to the original pretrained GPT-2 weights after each iteration''' , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=100 , min_len=1_026 , trim=SCREAMING_SNAKE_CASE , data_file='''data/tokenized_stories_train_wikitext103.jbl''' , igf_data_file='''igf_context_pairs.jbl''' , )
# Load train data for secondary learner
__UpperCamelCase :Optional[Any] = joblib.load('''data/IGF_values.jbl''' )
# Train secondary learner
__UpperCamelCase :str = training_secondary_learner(
SCREAMING_SNAKE_CASE , secondary_learner_max_epochs=15 , secondary_learner_batch_size=128 , eval_freq=100 , igf_model_path='''igf_model.pt''' , )
# load pretrained gpt2 model
__UpperCamelCase :Union[str, Any] = GPTaLMHeadModel.from_pretrained('''gpt2''' )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
__UpperCamelCase , __UpperCamelCase :Dict = generate_datasets(
context_len=32 , file='''data/tokenized_stories_train_wikitext103.jbl''' , number=100 , min_len=1_026 , trim=SCREAMING_SNAKE_CASE )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , context_len=32 , max_steps=1_000 , batch_size=16 , threshold=1.0 , recopy_model=SCREAMING_SNAKE_CASE , secondary_learner=SCREAMING_SNAKE_CASE , eval_interval=10 , finetuned_model_name='''gpt2_finetuned.pt''' , )
if __name__ == "__main__":
main()
| 43 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
__A = logging.get_logger(__name__)
@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__(self : Tuple , **UpperCAmelCase_ : Tuple) ->Any:
'''simple docstring'''
super().__init__(**UpperCAmelCase_)
if self.framework == "tf":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""")
requires_backends(self , "vision")
self.check_model_type(UpperCAmelCase_)
def __call__(self : Optional[int] , UpperCAmelCase_ : Union[str, "Image.Image", List[Dict[str, Any]]] , UpperCAmelCase_ : Union[str, List[str]] = None , **UpperCAmelCase_ : List[str] , ) ->Union[str, Any]:
'''simple docstring'''
if "text_queries" in kwargs:
lowerCamelCase__: Any =kwargs.pop("text_queries")
if isinstance(UpperCAmelCase_ , (str, Image.Image)):
lowerCamelCase__: List[Any] ={"image": image, "candidate_labels": candidate_labels}
else:
lowerCamelCase__: Any =image
lowerCamelCase__: Dict =super().__call__(UpperCAmelCase_ , **UpperCAmelCase_)
return results
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , **UpperCAmelCase_ : Union[str, Any]) ->Dict:
'''simple docstring'''
lowerCamelCase__: List[str] ={}
if "threshold" in kwargs:
lowerCamelCase__: List[Any] =kwargs["threshold"]
if "top_k" in kwargs:
lowerCamelCase__: Any =kwargs["top_k"]
return {}, {}, postprocess_params
def SCREAMING_SNAKE_CASE_ (self : str , UpperCAmelCase_ : List[Any]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: List[Any] =load_image(inputs["image"])
lowerCamelCase__: Dict =inputs["candidate_labels"]
if isinstance(UpperCAmelCase_ , UpperCAmelCase_):
lowerCamelCase__: Any =candidate_labels.split(",")
lowerCamelCase__: Optional[int] =torch.tensor([[image.height, image.width]] , dtype=torch.intaa)
for i, candidate_label in enumerate(UpperCAmelCase_):
lowerCamelCase__: Dict =self.tokenizer(UpperCAmelCase_ , return_tensors=self.framework)
lowerCamelCase__: Union[str, Any] =self.image_processor(UpperCAmelCase_ , return_tensors=self.framework)
yield {
"is_last": i == len(UpperCAmelCase_) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def SCREAMING_SNAKE_CASE_ (self : Optional[Any] , UpperCAmelCase_ : Tuple) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Dict =model_inputs.pop("target_size")
lowerCamelCase__: Dict =model_inputs.pop("candidate_label")
lowerCamelCase__: Dict =model_inputs.pop("is_last")
lowerCamelCase__: Union[str, Any] =self.model(**UpperCAmelCase_)
lowerCamelCase__: Dict ={"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def SCREAMING_SNAKE_CASE_ (self : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : Any=0.1 , UpperCAmelCase_ : str=None) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =[]
for model_output in model_outputs:
lowerCamelCase__: Optional[Any] =model_output["candidate_label"]
lowerCamelCase__: Tuple =BaseModelOutput(UpperCAmelCase_)
lowerCamelCase__: Dict =self.image_processor.post_process_object_detection(
outputs=UpperCAmelCase_ , threshold=UpperCAmelCase_ , target_sizes=model_output["target_size"])[0]
for index in outputs["scores"].nonzero():
lowerCamelCase__: Dict =outputs["scores"][index].item()
lowerCamelCase__: Dict =self._get_bounding_box(outputs["boxes"][index][0])
lowerCamelCase__: Optional[Any] ={"score": score, "label": label, "box": box}
results.append(UpperCAmelCase_)
lowerCamelCase__: List[str] =sorted(UpperCAmelCase_ , key=lambda UpperCAmelCase_: x["score"] , reverse=UpperCAmelCase_)
if top_k:
lowerCamelCase__: Dict =results[:top_k]
return results
def SCREAMING_SNAKE_CASE_ (self : str , UpperCAmelCase_ : "torch.Tensor") ->Dict[str, int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Optional[Any] =box.int().tolist()
lowerCamelCase__: Optional[int] ={
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 10 | 0 |
def a__ ( _SCREAMING_SNAKE_CASE : int = 3 , _SCREAMING_SNAKE_CASE : int = 7 , _SCREAMING_SNAKE_CASE : int = 1_00_00_00 ) -> int:
"""simple docstring"""
UpperCAmelCase_ : Any = 0
UpperCAmelCase_ : str = 1
for current_denominator in range(1 , limit + 1 ):
UpperCAmelCase_ : Tuple = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
UpperCAmelCase_ : Optional[int] = current_numerator
UpperCAmelCase_ : Tuple = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=100_0000))
| 356 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_lowerCamelCase = datasets.utils.logging.get_logger(__name__)
class _snake_case (folder_based_builder.FolderBasedBuilderConfig):
__A : bool =None
__A : bool =None
class _snake_case (folder_based_builder.FolderBasedBuilder):
__A : Union[str, Any] =datasets.Audio()
__A : Optional[int] ="audio"
__A : Any =AudioFolderConfig
__A : List[str] # definition at the bottom of the script
__A : Optional[int] =AudioClassification(audio_column="audio" , label_column="label")
_lowerCamelCase = [
""".aiff""",
""".au""",
""".avr""",
""".caf""",
""".flac""",
""".htk""",
""".svx""",
""".mat4""",
""".mat5""",
""".mpc2k""",
""".ogg""",
""".paf""",
""".pvf""",
""".raw""",
""".rf64""",
""".sd2""",
""".sds""",
""".ircam""",
""".voc""",
""".w64""",
""".wav""",
""".nist""",
""".wavex""",
""".wve""",
""".xi""",
""".mp3""",
""".opus""",
]
_lowerCamelCase = AUDIO_EXTENSIONS
| 67 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : str ) -> None:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
def UpperCAmelCase__ ( self : List[str] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : float ) -> None:
"""simple docstring"""
if nodea not in self.connections:
self.add_node(__SCREAMING_SNAKE_CASE )
if nodea not in self.connections:
self.add_node(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = probability
def UpperCAmelCase__ ( self : str ) -> list[str]:
"""simple docstring"""
return list(self.connections )
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def a__ ( a__ , a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(a__ , a__ , a__ )
__SCREAMING_SNAKE_CASE = Counter(graph.get_nodes() )
__SCREAMING_SNAKE_CASE = start
for _ in range(a__ ):
__SCREAMING_SNAKE_CASE = graph.transition(a__ )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 267 |
'''simple docstring'''
def a__ ( a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = int(a__ )
# Initialize Result
__SCREAMING_SNAKE_CASE = []
# Traverse through all denomination
for denomination in reversed(a__ ):
# Find denominations
while int(a__ ) >= int(a__ ):
total_value -= int(a__ )
answer.append(a__ ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
UpperCAmelCase : Dict = []
UpperCAmelCase : List[str] = '0'
if (
input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
== "y"
):
UpperCAmelCase : List[str] = int(input('Enter the number of denominations you want to add: ').strip())
for i in range(0, n):
denominations.append(int(input(f"""Denomination {i}: """).strip()))
UpperCAmelCase : str = input('Enter the change you want to make in Indian Currency: ').strip()
else:
# All denominations of Indian Currency if user does not enter
UpperCAmelCase : int = [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
UpperCAmelCase : Any = input('Enter the change you want to make: ').strip()
if int(value) == 0 or int(value) < 0:
print('The total value cannot be zero or negative.')
else:
print(f"""Following is minimal change for {value}: """)
UpperCAmelCase : Any = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=' ')
| 267 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class lowercase_ ( A ):
"""simple docstring"""
lowerCamelCase_ = '''poolformer'''
def __init__( self : Union[str, Any] , __lowerCamelCase : Optional[int]=3 , __lowerCamelCase : Union[str, Any]=1_6 , __lowerCamelCase : Any=1_6 , __lowerCamelCase : int=3 , __lowerCamelCase : Optional[int]=4.0 , __lowerCamelCase : Optional[int]=[2, 2, 6, 2] , __lowerCamelCase : Tuple=[6_4, 1_2_8, 3_2_0, 5_1_2] , __lowerCamelCase : str=[7, 3, 3, 3] , __lowerCamelCase : str=[4, 2, 2, 2] , __lowerCamelCase : str=[2, 1, 1, 1] , __lowerCamelCase : Any=4 , __lowerCamelCase : str=0.0 , __lowerCamelCase : Tuple="gelu" , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Dict=1e-5 , __lowerCamelCase : List[str]=0.0_2 , **__lowerCamelCase : Dict , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = patch_size
_SCREAMING_SNAKE_CASE = stride
_SCREAMING_SNAKE_CASE = padding
_SCREAMING_SNAKE_CASE = pool_size
_SCREAMING_SNAKE_CASE = hidden_sizes
_SCREAMING_SNAKE_CASE = mlp_ratio
_SCREAMING_SNAKE_CASE = depths
_SCREAMING_SNAKE_CASE = patch_sizes
_SCREAMING_SNAKE_CASE = strides
_SCREAMING_SNAKE_CASE = num_encoder_blocks
_SCREAMING_SNAKE_CASE = drop_path_rate
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = use_layer_scale
_SCREAMING_SNAKE_CASE = layer_scale_init_value
_SCREAMING_SNAKE_CASE = initializer_range
super().__init__(**__lowerCamelCase )
class lowercase_ ( A ):
"""simple docstring"""
lowerCamelCase_ = version.parse('''1.11''' )
@property
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
return 2e-3
| 111 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase_ ( self : List[Any] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(__lowerCamelCase ) )
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(__lowerCamelCase ) )
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(__lowerCamelCase ) )
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(__lowerCamelCase ) )
def lowerCAmelCase_ ( self : int ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(__lowerCamelCase ) )
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
_SCREAMING_SNAKE_CASE = "fp16"
self.assertTrue(is_safetensors_compatible(__lowerCamelCase , variant=__lowerCamelCase ) )
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
_SCREAMING_SNAKE_CASE = "fp16"
self.assertTrue(is_safetensors_compatible(__lowerCamelCase , variant=__lowerCamelCase ) )
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
# pass variant but use the non-variant filenames
_SCREAMING_SNAKE_CASE = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
_SCREAMING_SNAKE_CASE = "fp16"
self.assertTrue(is_safetensors_compatible(__lowerCamelCase , variant=__lowerCamelCase ) )
def lowerCAmelCase_ ( self : int ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
_SCREAMING_SNAKE_CASE = "fp16"
self.assertFalse(is_safetensors_compatible(__lowerCamelCase , variant=__lowerCamelCase ) )
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
_SCREAMING_SNAKE_CASE = "fp16"
self.assertTrue(is_safetensors_compatible(__lowerCamelCase , variant=__lowerCamelCase ) )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
# pass variant but use the non-variant filenames
_SCREAMING_SNAKE_CASE = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
_SCREAMING_SNAKE_CASE = "fp16"
self.assertTrue(is_safetensors_compatible(__lowerCamelCase , variant=__lowerCamelCase ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
_SCREAMING_SNAKE_CASE = "fp16"
self.assertFalse(is_safetensors_compatible(__lowerCamelCase , variant=__lowerCamelCase ) )
| 111 | 1 |
"""simple docstring"""
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 UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCAmelCase (unittest.TestCase ):
"""simple docstring"""
@property
def _snake_case ( self ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _snake_case ( self ):
lowercase__: Any = ort.SessionOptions()
lowercase__: Optional[Any] = False
return options
def _snake_case ( self ):
lowercase__: int = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
lowercase__: Tuple = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
lowercase__: Tuple = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__: List[Any] = '''A red cat sitting on a park bench'''
lowercase__: int = np.random.RandomState(0 )
lowercase__: List[str] = pipe(
prompt=_UpperCAmelCase , image=_UpperCAmelCase , mask_image=_UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=10 , generator=_UpperCAmelCase , output_type='''np''' , )
lowercase__: Tuple = output.images
lowercase__: int = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
lowercase__: Union[str, Any] = np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _snake_case ( self ):
lowercase__: Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
lowercase__: int = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
lowercase__: Union[str, Any] = LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
lowercase__: List[str] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=_UpperCAmelCase , safety_checker=_UpperCAmelCase , feature_extractor=_UpperCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__: Any = '''A red cat sitting on a park bench'''
lowercase__: List[str] = np.random.RandomState(0 )
lowercase__: Dict = pipe(
prompt=_UpperCAmelCase , image=_UpperCAmelCase , mask_image=_UpperCAmelCase , guidance_scale=7.5 , num_inference_steps=20 , generator=_UpperCAmelCase , output_type='''np''' , )
lowercase__: Union[str, Any] = output.images
lowercase__: Any = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
lowercase__: Union[str, Any] = np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 177 | """simple docstring"""
__A = [
(1_0_0_0, "M"),
(9_0_0, "CM"),
(5_0_0, "D"),
(4_0_0, "CD"),
(1_0_0, "C"),
(9_0, "XC"),
(5_0, "L"),
(4_0, "XL"),
(1_0, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> int:
lowercase__: Dict = {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
lowercase__: List[str] = 0
lowercase__: List[Any] = 0
while place < len(__UpperCAmelCase ):
if (place + 1 < len(__UpperCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> str:
lowercase__: Optional[Any] = []
for arabic, roman in ROMAN:
((lowercase__), (lowercase__)): Tuple = divmod(__UpperCAmelCase , __UpperCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177 | 1 |
from collections.abc import Callable
class a__ :
def __init__( self , UpperCAmelCase = None ) -> None:
# Stores actual heap items.
__a = []
# Stores indexes of each item for supporting updates and deletion.
__a = {}
# Stores current size of heap.
__a = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
__a = key or (lambda UpperCAmelCase : x)
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> int | None:
return int((i - 1) / 2 ) if i > 0 else None
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> int | None:
__a = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> int | None:
__a = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> None:
__a , __a = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
__a , __a = self.arr[j], self.arr[i]
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> bool:
return self.arr[i][1] < self.arr[j][1]
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> int:
__a = self._left(UpperCAmelCase )
__a = self._right(UpperCAmelCase )
__a = i
if left is not None and not self._cmp(UpperCAmelCase , UpperCAmelCase ):
__a = left
if right is not None and not self._cmp(UpperCAmelCase , UpperCAmelCase ):
__a = right
return valid_parent
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> None:
__a = self._parent(UpperCAmelCase )
while parent is not None and not self._cmp(UpperCAmelCase , UpperCAmelCase ):
self._swap(UpperCAmelCase , UpperCAmelCase )
__a , __a = parent, self._parent(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> None:
__a = self._get_valid_parent(UpperCAmelCase )
while valid_parent != index:
self._swap(UpperCAmelCase , UpperCAmelCase )
__a , __a = valid_parent, self._get_valid_parent(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> None:
if item not in self.pos_map:
return
__a = self.pos_map[item]
__a = [item, self.key(UpperCAmelCase )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(UpperCAmelCase )
self._heapify_down(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase ) -> None:
if item not in self.pos_map:
return
__a = self.pos_map[item]
del self.pos_map[item]
__a = self.arr[self.size - 1]
__a = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(UpperCAmelCase )
self._heapify_down(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase ) -> None:
__a = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(UpperCAmelCase )] )
else:
__a = [item, self.key(UpperCAmelCase )]
__a = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __SCREAMING_SNAKE_CASE ( self ) -> tuple | None:
return self.arr[0] if self.size else None
def __SCREAMING_SNAKE_CASE ( self ) -> tuple | None:
__a = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def lowerCAmelCase( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 353 | from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__snake_case ):
A__ : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> List[str]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Dict:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Dict = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Optional[Any]:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Dict = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Optional[int]:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> str:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class a__ ( metaclass=__snake_case ):
A__ : Tuple = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> int:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> List[str]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 197 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : Tuple = """new-model"""
if is_tf_available():
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : str = NewModelConfig
@require_tf
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _snake_case ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
A: int = '''bert-base-cased'''
A: Optional[int] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: Tuple = TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : str ) -> str:
'''simple docstring'''
A: Tuple = '''bert-base-cased'''
A: str = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: Optional[int] = TFAutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : Tuple ) -> Dict:
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A: Optional[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: Optional[Any] = TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_ )
A , A: Dict = TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : str ) -> List[Any]:
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A: List[str] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: List[Any] = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A: List[str] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: str = TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE_ )
A , A: int = TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : Any ) -> Tuple:
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A: Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: str = TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_ )
A , A: Any = TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
A: Any = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: Tuple = TFAutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def _snake_case ( self : Optional[int] ) -> Any:
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
A: Any = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: Optional[int] = TFAutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
@require_tensorflow_probability
def _snake_case ( self : List[str] ) -> Any:
'''simple docstring'''
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
A: int = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: Tuple = TFAutoModelForTableQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE_ )
A , A: Optional[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
SCREAMING_SNAKE_CASE_ , output_loading_info=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : Optional[Any] ) -> Any:
'''simple docstring'''
A: Optional[Any] = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE_ ) , 1_44_10 )
def _snake_case ( self : Tuple ) -> List[Any]:
'''simple docstring'''
A: List[str] = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE_ ) , 1_44_10 )
def _snake_case ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
A: str = TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny''' )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
A: int = copy.deepcopy(model.config )
A: Optional[Any] = ['''FunnelBaseModel''']
A: Union[str, Any] = TFAutoModel.from_config(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
A: Any = TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _snake_case ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
try:
AutoConfig.register('''new-model''' , SCREAMING_SNAKE_CASE_ )
A: Union[str, Any] = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
auto_class.register(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
auto_class.register(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
auto_class.register(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Now that the config is registered, it can be used as any other config with the auto-API
A: Optional[int] = BertModelTester(self ).get_config()
A: List[str] = NewModelConfig(**tiny_config.to_dict() )
A: Any = auto_class.from_config(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
A: Dict = auto_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def _snake_case ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE_ , '''bert-base is not a local folder and is not a valid model identifier''' ):
A: List[Any] = TFAutoModel.from_pretrained('''bert-base''' )
def _snake_case ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE_ , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
A: int = TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , revision='''aaaaaa''' )
def _snake_case ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
SCREAMING_SNAKE_CASE_ , '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin''' , ):
A: Dict = TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )
def _snake_case ( self : Union[str, Any] ) -> int:
'''simple docstring'''
with self.assertRaisesRegex(SCREAMING_SNAKE_CASE_ , '''Use `from_pt=True` to load this model''' ):
A: Optional[int] = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )
def _snake_case ( self : Dict ) -> int:
'''simple docstring'''
A: Optional[int] = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
with RequestCounter() as counter:
A: Union[str, Any] = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
A: List[str] = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
with RequestCounter() as counter:
A: str = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 319 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = 0 ) -> list:
A: Dict = length or len(__lowercase )
A: Dict = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
A , A: Tuple = list_data[i + 1], list_data[i]
A: Union[str, Any] = True
return list_data if not swapped else bubble_sort(__lowercase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=1_3 , snake_case_=3_2 , snake_case_=2 , snake_case_=3 , snake_case_=1_6 , snake_case_=[1, 2, 1] , snake_case_=[2, 2, 4] , snake_case_=2 , snake_case_=2.0 , snake_case_=True , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.1 , snake_case_="gelu" , snake_case_=False , snake_case_=True , snake_case_=0.02 , snake_case_=1E-5 , snake_case_=True , snake_case_=None , snake_case_=True , snake_case_=1_0 , snake_case_=8 , snake_case_=["stage1", "stage2", "stage3"] , snake_case_=[1, 2, 3] , ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = parent
UpperCAmelCase_ : Any = batch_size
UpperCAmelCase_ : Optional[Any] = image_size
UpperCAmelCase_ : int = patch_size
UpperCAmelCase_ : str = num_channels
UpperCAmelCase_ : Optional[int] = embed_dim
UpperCAmelCase_ : Optional[Any] = depths
UpperCAmelCase_ : Union[str, Any] = num_heads
UpperCAmelCase_ : List[Any] = window_size
UpperCAmelCase_ : str = mlp_ratio
UpperCAmelCase_ : Union[str, Any] = qkv_bias
UpperCAmelCase_ : Optional[Any] = hidden_dropout_prob
UpperCAmelCase_ : int = attention_probs_dropout_prob
UpperCAmelCase_ : List[str] = drop_path_rate
UpperCAmelCase_ : List[str] = hidden_act
UpperCAmelCase_ : Union[str, Any] = use_absolute_embeddings
UpperCAmelCase_ : List[str] = patch_norm
UpperCAmelCase_ : Dict = layer_norm_eps
UpperCAmelCase_ : Union[str, Any] = initializer_range
UpperCAmelCase_ : Union[str, Any] = is_training
UpperCAmelCase_ : int = scope
UpperCAmelCase_ : Optional[int] = use_labels
UpperCAmelCase_ : Tuple = type_sequence_label_size
UpperCAmelCase_ : Tuple = encoder_stride
UpperCAmelCase_ : List[Any] = out_features
UpperCAmelCase_ : Union[str, Any] = out_indices
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase_ : Dict = None
if self.use_labels:
UpperCAmelCase_ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ : str = self.get_config()
return config, pixel_values, labels
def _UpperCamelCase ( self ):
'''simple docstring'''
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Dict = MaskFormerSwinModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCAmelCase_ : Optional[int] = model(snake_case_ )
UpperCAmelCase_ : List[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
UpperCAmelCase_ : Dict = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Any = MaskFormerSwinBackbone(config=snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCAmelCase_ : Union[str, Any] = model(snake_case_ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [1_3, 1_6, 1_6, 1_6] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [1_6, 3_2, 6_4] )
# verify ValueError
with self.parent.assertRaises(snake_case_ ):
UpperCAmelCase_ : str = ['stem']
UpperCAmelCase_ : Tuple = MaskFormerSwinBackbone(config=snake_case_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : int = self.prepare_config_and_inputs()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = config_and_inputs
UpperCAmelCase_ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ :Any = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
lowerCamelCase_ :Optional[int] = {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
lowerCamelCase_ :Optional[int] = False
lowerCamelCase_ :Optional[int] = False
lowerCamelCase_ :List[str] = False
lowerCamelCase_ :Dict = False
lowerCamelCase_ :str = False
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = MaskFormerSwinModelTester(self )
UpperCAmelCase_ : Union[str, Any] = ConfigTester(self , config_class=snake_case_ , embed_dim=3_7 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
' `nn.DataParallel`'
) )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''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 _UpperCamelCase ( self ):
'''simple docstring'''
return
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*snake_case_ )
@unittest.skip('Swin does not use inputs_embeds' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('Swin does not support feedforward chunking' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''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(snake_case_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase_ : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case_ , nn.Linear ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : int = model_class(snake_case_ )
UpperCAmelCase_ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase_ : Any = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case_ )
@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : str = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ : Tuple = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
UpperCAmelCase_ : Dict = outputs.hidden_states
UpperCAmelCase_ : str = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(snake_case_ ) , snake_case_ )
# Swin has a different seq_length
UpperCAmelCase_ : Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCAmelCase_ : Union[str, Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ : str = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
UpperCAmelCase_ : Union[str, Any] = True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase_ : List[Any] = True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ : str = 3
UpperCAmelCase_ : str = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
UpperCAmelCase_ : Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
UpperCAmelCase_ : Dict = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
UpperCAmelCase_ : Dict = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
UpperCAmelCase_ : Optional[int] = True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase_ : Tuple = True
self.check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ , (padded_height, padded_width) )
@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(snake_case_ ):
UpperCAmelCase_ : Union[str, Any] = 0
return t
def check_equivalence(snake_case_ , snake_case_ , snake_case_ , snake_case_={} ):
with torch.no_grad():
UpperCAmelCase_ : Any = model(**snake_case_ , return_dict=snake_case_ , **snake_case_ )
UpperCAmelCase_ : List[str] = model(**snake_case_ , return_dict=snake_case_ , **snake_case_ ).to_tuple()
def recursive_check(snake_case_ , snake_case_ ):
if isinstance(snake_case_ , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(snake_case_ , snake_case_ ):
recursive_check(snake_case_ , snake_case_ )
elif isinstance(snake_case_ , snake_case_ ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(snake_case_ , snake_case_ )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(snake_case_ ) , set_nan_tensor_to_zero(snake_case_ ) , atol=1E-5 ) , msg=(
'Tuple and dict output are not equal. Difference:'
F''' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'''
F''' {torch.isnan(snake_case_ ).any()} and `inf`: {torch.isinf(snake_case_ )}. Dict has'''
F''' `nan`: {torch.isnan(snake_case_ ).any()} and `inf`: {torch.isinf(snake_case_ )}.'''
) , )
recursive_check(snake_case_ , snake_case_ )
for model_class in self.all_model_classes:
UpperCAmelCase_ : List[str] = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCAmelCase_ : int = self._prepare_for_class(snake_case_ , snake_case_ )
UpperCAmelCase_ : str = self._prepare_for_class(snake_case_ , snake_case_ )
check_equivalence(snake_case_ , snake_case_ , snake_case_ )
UpperCAmelCase_ : List[str] = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
UpperCAmelCase_ : Optional[int] = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
check_equivalence(snake_case_ , snake_case_ , snake_case_ )
UpperCAmelCase_ : Dict = self._prepare_for_class(snake_case_ , snake_case_ )
UpperCAmelCase_ : Union[str, Any] = self._prepare_for_class(snake_case_ , snake_case_ )
check_equivalence(snake_case_ , snake_case_ , snake_case_ , {'output_hidden_states': True} )
UpperCAmelCase_ : str = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
UpperCAmelCase_ : List[str] = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
check_equivalence(snake_case_ , snake_case_ , snake_case_ , {'output_hidden_states': True} )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase , lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase_ :Union[str, Any] = (MaskFormerSwinBackbone,) if is_torch_available() else ()
lowerCamelCase_ :List[Any] = MaskFormerSwinConfig
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = MaskFormerSwinModelTester(self )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ : Optional[int] = inputs_dict['pixel_values'].shape[0]
for backbone_class in self.all_model_classes:
UpperCAmelCase_ : Optional[Any] = backbone_class(snake_case_ )
backbone.to(snake_case_ )
backbone.eval()
UpperCAmelCase_ : Any = backbone(**snake_case_ )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , snake_case_ )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
UpperCAmelCase_ : List[str] = backbone(**snake_case_ , output_hidden_states=snake_case_ )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
UpperCAmelCase_ : Union[str, Any] = backbone(**snake_case_ , output_attentions=snake_case_ )
self.assertIsNotNone(outputs.attentions )
| 274 | '''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase_ :List[str] = '''autoformer'''
lowerCamelCase_ :Optional[Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self , snake_case_ = None , snake_case_ = None , snake_case_ = "student_t" , snake_case_ = "nll" , snake_case_ = 1 , snake_case_ = [1, 2, 3, 4, 5, 6, 7] , snake_case_ = True , snake_case_ = 0 , snake_case_ = 0 , snake_case_ = 0 , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = 6_4 , snake_case_ = 2 , snake_case_ = 2 , snake_case_ = 2 , snake_case_ = 2 , snake_case_ = 3_2 , snake_case_ = 3_2 , snake_case_ = "gelu" , snake_case_ = 0.1 , snake_case_ = 0.1 , snake_case_ = 0.1 , snake_case_ = 0.1 , snake_case_ = 0.1 , snake_case_ = 1_0_0 , snake_case_ = 0.02 , snake_case_ = True , snake_case_=True , snake_case_ = 1_0 , snake_case_ = 2_5 , snake_case_ = 3 , **snake_case_ , ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = prediction_length
UpperCAmelCase_ : List[str] = context_length if context_length is not None else prediction_length
UpperCAmelCase_ : Optional[int] = distribution_output
UpperCAmelCase_ : Optional[int] = loss
UpperCAmelCase_ : Union[str, Any] = input_size
UpperCAmelCase_ : int = num_time_features
UpperCAmelCase_ : List[str] = lags_sequence
UpperCAmelCase_ : Any = scaling
UpperCAmelCase_ : Any = num_dynamic_real_features
UpperCAmelCase_ : int = num_static_real_features
UpperCAmelCase_ : Optional[Any] = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(snake_case_ ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
UpperCAmelCase_ : List[Any] = cardinality
else:
UpperCAmelCase_ : Optional[int] = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(snake_case_ ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
UpperCAmelCase_ : List[str] = embedding_dimension
else:
UpperCAmelCase_ : List[Any] = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
UpperCAmelCase_ : List[str] = num_parallel_samples
# Transformer architecture configuration
UpperCAmelCase_ : Union[str, Any] = input_size * len(self.lags_sequence ) + self._number_of_features
UpperCAmelCase_ : str = d_model
UpperCAmelCase_ : str = encoder_attention_heads
UpperCAmelCase_ : str = decoder_attention_heads
UpperCAmelCase_ : str = encoder_ffn_dim
UpperCAmelCase_ : str = decoder_ffn_dim
UpperCAmelCase_ : str = encoder_layers
UpperCAmelCase_ : str = decoder_layers
UpperCAmelCase_ : str = dropout
UpperCAmelCase_ : Optional[int] = attention_dropout
UpperCAmelCase_ : Tuple = activation_dropout
UpperCAmelCase_ : Any = encoder_layerdrop
UpperCAmelCase_ : Tuple = decoder_layerdrop
UpperCAmelCase_ : List[str] = activation_function
UpperCAmelCase_ : Tuple = init_std
UpperCAmelCase_ : Union[str, Any] = use_cache
# Autoformer
UpperCAmelCase_ : Any = label_length
UpperCAmelCase_ : Union[str, Any] = moving_average
UpperCAmelCase_ : Tuple = autocorrelation_factor
super().__init__(is_encoder_decoder=snake_case_ , **snake_case_ )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
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
)
| 274 | 1 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowerCAmelCase_ ( snake_case_ : Union[str, Any] , snake_case_ : Dict ) ->Tuple:
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
lowerCamelCase__ : Tuple =flax_key_tuple[:-1] + ('weight',)
lowerCamelCase__ : int =torch.permute(lowerCamelCase_ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_ ):
# linear layer
lowerCamelCase__ : Any =flax_key_tuple[:-1] + ('weight',)
lowerCamelCase__ : int =flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowerCamelCase__ : List[str] =flax_key_tuple[:-1] + ('weight',)
return flax_key_tuple, flax_tensor
def lowerCAmelCase_ ( snake_case_ : Optional[Any] , snake_case_ : str , snake_case_ : int ) ->List[str]:
if "metadata" in layer:
lowerCamelCase__ : Any =layer.split('metadata' )
lowerCamelCase__ : Union[str, Any] =''.join(split_layer[0] )[:-1]
lowerCamelCase__ : List[str] =[tuple(('metadata' + split_layer[1]).split('/' ) )]
elif "kvstore" in layer:
lowerCamelCase__ : str =layer.split('kvstore' )
lowerCamelCase__ : Union[str, Any] =''.join(split_layer[0] )[:-1]
lowerCamelCase__ : Optional[Any] =[tuple(('kvstore' + split_layer[1]).split('/' ) )]
else:
lowerCamelCase__ : int =layer.split('/' )
lowerCamelCase__ : Tuple ='/'.join(split_layer[:-1] )
lowerCamelCase__ : Optional[Any] =(split_layer[-1],)
if "kvstore/path" in layer:
lowerCamelCase__ : str =f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
elif "kvstore/driver" in layer:
lowerCamelCase__ : List[str] ='file'
else:
lowerCamelCase__ : Optional[int] =checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowerCAmelCase_ ( snake_case_ : Any , snake_case_ : int ) ->Dict:
lowerCamelCase__ : Tuple =rename_keys(lowerCamelCase_ )
lowerCamelCase__ : List[str] ={}
for k, v in current_block.items():
lowerCamelCase__ : Dict =v
lowerCamelCase__ : Any =new_current_block
torch.save(lowerCamelCase_ , lowerCamelCase_ )
def lowerCAmelCase_ ( snake_case_ : Tuple , snake_case_ : Optional[int] , snake_case_ : Tuple , snake_case_ : Optional[int] , snake_case_ : Tuple = WEIGHTS_NAME ) ->Dict:
lowerCamelCase__ : Optional[Any] =convert_file_size_to_int(lowerCamelCase_ )
lowerCamelCase__ : Any =[]
lowerCamelCase__ : Optional[Any] ={}
lowerCamelCase__ : Any =0
lowerCamelCase__ : str =0
os.makedirs(lowerCamelCase_ , exist_ok=lowerCamelCase_ )
with gfile.GFile(switch_checkpoint_path + '/checkpoint' , 'rb' ) as fp:
lowerCamelCase__ : Union[str, Any] =serialization.msgpack_restore(fp.read() )['optimizer']['target']
lowerCamelCase__ : Optional[int] =flatten_dict(lowerCamelCase_ , sep='/' )
lowerCamelCase__ : str ={}
for layer in checkpoint_info.keys():
lowerCamelCase__ : List[Any] =get_key_and_tensorstore_dict(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
if curr_real_layer_name in all_layers:
lowerCamelCase__ : List[Any] =content
else:
lowerCamelCase__ : Dict ={split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
lowerCamelCase__ : Dict =ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
lowerCamelCase__ : Tuple =torch.tensor(lowerCamelCase_ )
lowerCamelCase__ : Dict =raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
lowerCamelCase__ : str =rename_base_flax_keys(tuple(key.split('/' ) ) , lowerCamelCase_ )
lowerCamelCase__ : Any ='/'.join(lowerCamelCase_ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
lowerCamelCase__ : List[str] =os.path.join(
lowerCamelCase_ , weights_name.replace('.bin' , f"""-{len(lowerCamelCase_ )+1:05d}-of-???.bin""" ) )
rename_and_save_block(lowerCamelCase_ , lowerCamelCase_ )
sharded_state_dicts.append(current_block.keys() )
del current_block
lowerCamelCase__ : Any ={}
lowerCamelCase__ : str =0
lowerCamelCase__ : List[str] =raw_weights.to(getattr(lowerCamelCase_ , lowerCamelCase_ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
lowerCamelCase__ : List[Any] =os.path.join(lowerCamelCase_ , weights_name.replace('.bin' , f"""-{len(lowerCamelCase_ )+1:05d}-of-???.bin""" ) )
rename_and_save_block(lowerCamelCase_ , lowerCamelCase_ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(lowerCamelCase_ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
lowerCamelCase__ : int ={}
lowerCamelCase__ : Optional[Any] ={}
for idx, shard in enumerate(lowerCamelCase_ ):
lowerCamelCase__ : str =weights_name.replace(
'.bin' , f"""-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin""" ) # len(sharded_state_dicts):05d}
lowerCamelCase__ : List[str] =os.path.join(lowerCamelCase_ , weights_name.replace('.bin' , f"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(lowerCamelCase_ , os.path.join(lowerCamelCase_ , lowerCamelCase_ ) )
lowerCamelCase__ : int =shard
for key in shard:
lowerCamelCase__ : Optional[int] =shard_file
# Add the metadata
lowerCamelCase__ : Any ={'total_size': total_size}
lowerCamelCase__ : Tuple ={'metadata': metadata, 'weight_map': weight_map}
with open(os.path.join(lowerCamelCase_ , lowerCamelCase_ ) , 'w' , encoding='utf-8' ) as f:
lowerCamelCase__ : Tuple =json.dumps(lowerCamelCase_ , indent=2 , sort_keys=lowerCamelCase_ ) + '\n'
f.write(lowerCamelCase_ )
return metadata, index
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
lowerCAmelCase = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowerCAmelCase_ ( ) ->str:
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
lowerCamelCase__ : Optional[Any] =SwitchTransformersConfig.from_pretrained('google/switch-base-8' )
config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted' )
lowerCamelCase__ : str =SwitchTransformersForConditionalGeneration.from_pretrained(
'/home/arthur_huggingface_co/transformers/switch_converted' , device_map='auto' )
lowerCamelCase__ : str =TaTokenizer.from_pretrained('t5-small' )
lowerCamelCase__ : Tuple ='A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'
lowerCamelCase__ : Optional[int] =tokenizer(lowerCamelCase_ , return_tensors='pt' ).input_ids
lowerCamelCase__ : Tuple =model.generate(lowerCamelCase_ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) ) | 126 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _lowerCamelCase( _a, unittest.TestCase ):
lowercase_ : Union[str, Any] = ConsistencyModelPipeline
lowercase_ : Tuple = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
lowercase_ : List[str] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
lowercase_ : List[str] = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
@property
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : Tuple = UNetaDModel.from_pretrained(
'diffusers/consistency-models-test', subfolder='test_unet', )
return unet
@property
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : Tuple = UNetaDModel.from_pretrained(
'diffusers/consistency-models-test', subfolder='test_unet_class_cond', )
return unet
def UpperCamelCase ( self, lowerCamelCase=False) -> Dict:
"""simple docstring"""
if class_cond:
_lowercase : Union[str, Any] = self.dummy_cond_unet
else:
_lowercase : Union[str, Any] = self.dummy_uncond_unet
# Default to CM multistep sampler
_lowercase : List[str] = CMStochasticIterativeScheduler(
num_train_timesteps=40, sigma_min=0.0_0_2, sigma_max=8_0.0, )
_lowercase : Optional[Any] = {
'unet': unet,
'scheduler': scheduler,
}
return components
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase=0) -> Tuple:
"""simple docstring"""
if str(lowerCamelCase).startswith('mps'):
_lowercase : str = torch.manual_seed(lowerCamelCase)
else:
_lowercase : int = torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
_lowercase : Tuple = {
'batch_size': 1,
'num_inference_steps': None,
'timesteps': [22, 0],
'generator': generator,
'output_type': 'np',
}
return inputs
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_lowercase : Optional[int] = self.get_dummy_components()
_lowercase : str = ConsistencyModelPipeline(**lowerCamelCase)
_lowercase : Dict = pipe.to(lowerCamelCase)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : Tuple = self.get_dummy_inputs(lowerCamelCase)
_lowercase : Optional[int] = pipe(**lowerCamelCase).images
assert image.shape == (1, 32, 32, 3)
_lowercase : int = image[0, -3:, -3:, -1]
_lowercase : Dict = np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : Union[str, Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_lowercase : Dict = self.get_dummy_components(class_cond=lowerCamelCase)
_lowercase : Any = ConsistencyModelPipeline(**lowerCamelCase)
_lowercase : str = pipe.to(lowerCamelCase)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : Any = self.get_dummy_inputs(lowerCamelCase)
_lowercase : Any = 0
_lowercase : List[str] = pipe(**lowerCamelCase).images
assert image.shape == (1, 32, 32, 3)
_lowercase : Any = image[0, -3:, -3:, -1]
_lowercase : Union[str, Any] = np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_lowercase : Any = self.get_dummy_components()
_lowercase : Optional[Any] = ConsistencyModelPipeline(**lowerCamelCase)
_lowercase : List[str] = pipe.to(lowerCamelCase)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : Optional[Any] = self.get_dummy_inputs(lowerCamelCase)
_lowercase : Union[str, Any] = 1
_lowercase : Tuple = None
_lowercase : Tuple = pipe(**lowerCamelCase).images
assert image.shape == (1, 32, 32, 3)
_lowercase : str = image[0, -3:, -3:, -1]
_lowercase : List[str] = np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
def UpperCamelCase ( self) -> str:
"""simple docstring"""
_lowercase : Union[str, Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_lowercase : Dict = self.get_dummy_components(class_cond=lowerCamelCase)
_lowercase : Dict = ConsistencyModelPipeline(**lowerCamelCase)
_lowercase : Optional[Any] = pipe.to(lowerCamelCase)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : Tuple = self.get_dummy_inputs(lowerCamelCase)
_lowercase : Tuple = 1
_lowercase : int = None
_lowercase : Tuple = 0
_lowercase : Dict = pipe(**lowerCamelCase).images
assert image.shape == (1, 32, 32, 3)
_lowercase : List[str] = image[0, -3:, -3:, -1]
_lowercase : Any = np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
@slow
@require_torch_gpu
class _lowerCamelCase( unittest.TestCase ):
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self, lowerCamelCase=0, lowerCamelCase=False, lowerCamelCase="cpu", lowerCamelCase=torch.floataa, lowerCamelCase=(1, 3, 64, 64)) -> Optional[Any]:
"""simple docstring"""
_lowercase : List[Any] = torch.manual_seed(lowerCamelCase)
_lowercase : str = {
'num_inference_steps': None,
'timesteps': [22, 0],
'class_labels': 0,
'generator': generator,
'output_type': 'np',
}
if get_fixed_latents:
_lowercase : Optional[Any] = self.get_fixed_latents(seed=lowerCamelCase, device=lowerCamelCase, dtype=lowerCamelCase, shape=lowerCamelCase)
_lowercase : Tuple = latents
return inputs
def UpperCamelCase ( self, lowerCamelCase=0, lowerCamelCase="cpu", lowerCamelCase=torch.floataa, lowerCamelCase=(1, 3, 64, 64)) -> Any:
"""simple docstring"""
if type(lowerCamelCase) == str:
_lowercase : Union[str, Any] = torch.device(lowerCamelCase)
_lowercase : int = torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
_lowercase : List[str] = randn_tensor(lowerCamelCase, generator=lowerCamelCase, device=lowerCamelCase, dtype=lowerCamelCase)
return latents
def UpperCamelCase ( self) -> str:
"""simple docstring"""
_lowercase : Tuple = UNetaDModel.from_pretrained('diffusers/consistency_models', subfolder='diffusers_cd_imagenet64_l2')
_lowercase : Optional[int] = CMStochasticIterativeScheduler(
num_train_timesteps=40, sigma_min=0.0_0_2, sigma_max=8_0.0, )
_lowercase : Any = ConsistencyModelPipeline(unet=lowerCamelCase, scheduler=lowerCamelCase)
pipe.to(torch_device=lowerCamelCase)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : str = self.get_inputs()
_lowercase : Optional[int] = pipe(**lowerCamelCase).images
assert image.shape == (1, 64, 64, 3)
_lowercase : str = image[0, -3:, -3:, -1]
_lowercase : Optional[Any] = np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4])
assert np.abs(image_slice.flatten() - expected_slice).max() < 2E-2
def UpperCamelCase ( self) -> str:
"""simple docstring"""
_lowercase : List[str] = UNetaDModel.from_pretrained('diffusers/consistency_models', subfolder='diffusers_cd_imagenet64_l2')
_lowercase : List[str] = CMStochasticIterativeScheduler(
num_train_timesteps=40, sigma_min=0.0_0_2, sigma_max=8_0.0, )
_lowercase : Union[str, Any] = ConsistencyModelPipeline(unet=lowerCamelCase, scheduler=lowerCamelCase)
pipe.to(torch_device=lowerCamelCase)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : List[Any] = self.get_inputs()
_lowercase : int = 1
_lowercase : Optional[Any] = None
_lowercase : str = pipe(**lowerCamelCase).images
assert image.shape == (1, 64, 64, 3)
_lowercase : List[Any] = image[0, -3:, -3:, -1]
_lowercase : List[str] = np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7])
assert np.abs(image_slice.flatten() - expected_slice).max() < 2E-2
@require_torch_a
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : str = UNetaDModel.from_pretrained('diffusers/consistency_models', subfolder='diffusers_cd_imagenet64_l2')
_lowercase : Optional[int] = CMStochasticIterativeScheduler(
num_train_timesteps=40, sigma_min=0.0_0_2, sigma_max=8_0.0, )
_lowercase : Optional[int] = ConsistencyModelPipeline(unet=lowerCamelCase, scheduler=lowerCamelCase)
pipe.to(torch_device=lowerCamelCase, torch_dtype=torch.floataa)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : Any = self.get_inputs(get_fixed_latents=lowerCamelCase, device=lowerCamelCase)
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=lowerCamelCase, enable_math=lowerCamelCase, enable_mem_efficient=lowerCamelCase):
_lowercase : Dict = pipe(**lowerCamelCase).images
assert image.shape == (1, 64, 64, 3)
_lowercase : Any = image[0, -3:, -3:, -1]
_lowercase : Union[str, Any] = np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
@require_torch_a
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Dict = UNetaDModel.from_pretrained('diffusers/consistency_models', subfolder='diffusers_cd_imagenet64_l2')
_lowercase : Optional[Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40, sigma_min=0.0_0_2, sigma_max=8_0.0, )
_lowercase : int = ConsistencyModelPipeline(unet=lowerCamelCase, scheduler=lowerCamelCase)
pipe.to(torch_device=lowerCamelCase, torch_dtype=torch.floataa)
pipe.set_progress_bar_config(disable=lowerCamelCase)
_lowercase : List[Any] = self.get_inputs(get_fixed_latents=lowerCamelCase, device=lowerCamelCase)
_lowercase : int = 1
_lowercase : str = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=lowerCamelCase, enable_math=lowerCamelCase, enable_mem_efficient=lowerCamelCase):
_lowercase : Union[str, Any] = pipe(**lowerCamelCase).images
assert image.shape == (1, 64, 64, 3)
_lowercase : Any = image[0, -3:, -3:, -1]
_lowercase : int = np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
| 21 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_A : List[Any] =logging.get_logger(__name__)
_A : Optional[int] =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_A : Union[str, Any] =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _lowercase :
a = field(
default=_lowercase , metadata={"""help""": """Model type selected in the list: """ + """, """.join(_lowercase )} )
a = field(
default=_lowercase , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
a = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a = field(
default=128 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
a = field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
a = field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
a = field(
default=_lowercase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a = field(
default=_lowercase , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
a = field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a = field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a = field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
a = field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class _lowercase ( _lowercase ):
a = """train"""
a = """dev"""
class _lowercase ( _lowercase ):
a = 42
a = 42
a = 42
a = 42
def __init__( self: Union[str, Any] , UpperCamelCase__: SquadDataTrainingArguments , UpperCamelCase__: PreTrainedTokenizer , UpperCamelCase__: Optional[int] = None , UpperCamelCase__: Union[str, Split] = Split.train , UpperCamelCase__: Optional[bool] = False , UpperCamelCase__: Optional[str] = None , UpperCamelCase__: Optional[str] = "pt" , ):
lowerCamelCase__ : str = args
lowerCamelCase__ : Optional[int] = is_language_sensitive
lowerCamelCase__ : Union[str, Any] = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
try:
lowerCamelCase__ : List[str] = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
lowerCamelCase__ : int = mode
# Load data features from cache or dataset file
lowerCamelCase__ : List[str] = """v2""" if args.version_2_with_negative else """v1"""
lowerCamelCase__ : Optional[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCamelCase__ : Dict = cached_features_file + """.lock"""
with FileLock(UpperCamelCase__ ):
if os.path.exists(UpperCamelCase__ ) and not args.overwrite_cache:
lowerCamelCase__ : List[Any] = time.time()
lowerCamelCase__ : Optional[int] = torch.load(UpperCamelCase__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowerCamelCase__ : int = self.old_features["""features"""]
lowerCamelCase__ : int = self.old_features.get("""dataset""" , UpperCamelCase__ )
lowerCamelCase__ : Dict = self.old_features.get("""examples""" , UpperCamelCase__ )
logger.info(
F'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
""" future run""" )
else:
if mode == Split.dev:
lowerCamelCase__ : Optional[Any] = self.processor.get_dev_examples(args.data_dir )
else:
lowerCamelCase__ : Union[str, Any] = self.processor.get_train_examples(args.data_dir )
lowerCamelCase__ , lowerCamelCase__ : List[Any] = squad_convert_examples_to_features(
examples=self.examples , tokenizer=UpperCamelCase__ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=UpperCamelCase__ , )
lowerCamelCase__ : Dict = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , UpperCamelCase__ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self: Union[str, Any] ):
return len(self.features )
def __getitem__( self: List[str] , UpperCamelCase__: List[str] ):
# Convert to Tensors and build dataset
lowerCamelCase__ : str = self.features[i]
lowerCamelCase__ : str = torch.tensor(feature.input_ids , dtype=torch.long )
lowerCamelCase__ : int = torch.tensor(feature.attention_mask , dtype=torch.long )
lowerCamelCase__ : List[Any] = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowerCamelCase__ : List[Any] = torch.tensor(feature.cls_index , dtype=torch.long )
lowerCamelCase__ : Dict = torch.tensor(feature.p_mask , dtype=torch.float )
lowerCamelCase__ : Dict = torch.tensor(feature.is_impossible , dtype=torch.float )
lowerCamelCase__ : int = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowerCamelCase__ : Dict = torch.tensor(feature.start_position , dtype=torch.long )
lowerCamelCase__ : Dict = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 129 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class _lowercase ( unittest.TestCase ):
def lowerCamelCase_ ( self: Union[str, Any] ):
lowerCamelCase__ : Any = tempfile.mkdtemp()
lowerCamelCase__ : Any = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""的""",
"""价""",
"""格""",
"""是""",
"""15""",
"""便""",
"""alex""",
"""##andra""",
"""，""",
"""。""",
"""-""",
"""t""",
"""shirt""",
]
lowerCamelCase__ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
lowerCamelCase__ : Union[str, Any] = {
"""do_resize""": True,
"""size""": {"""height""": 224, """width""": 224},
"""do_center_crop""": True,
"""crop_size""": {"""height""": 18, """width""": 18},
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
"""do_convert_rgb""": True,
}
lowerCamelCase__ : Optional[int] = os.path.join(self.tmpdirname , UpperCamelCase__ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( self: List[Any] , **UpperCamelCase__: Optional[int] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def lowerCamelCase_ ( self: Any , **UpperCamelCase__: List[str] ):
return BertTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def lowerCamelCase_ ( self: str , **UpperCamelCase__: int ):
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def lowerCamelCase_ ( self: Optional[int] ):
shutil.rmtree(self.tmpdirname )
def lowerCamelCase_ ( self: List[str] ):
lowerCamelCase__ : int = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCamelCase__ : List[str] = [Image.fromarray(np.moveaxis(UpperCamelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase_ ( self: List[str] ):
lowerCamelCase__ : Tuple = self.get_tokenizer()
lowerCamelCase__ : Any = self.get_rust_tokenizer()
lowerCamelCase__ : str = self.get_image_processor()
lowerCamelCase__ : str = ChineseCLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
lowerCamelCase__ : List[Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCamelCase__ )
lowerCamelCase__ : Any = ChineseCLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
lowerCamelCase__ : List[Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , UpperCamelCase__ )
self.assertIsInstance(processor_fast.tokenizer , UpperCamelCase__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , UpperCamelCase__ )
self.assertIsInstance(processor_fast.image_processor , UpperCamelCase__ )
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ : Optional[int] = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ : str = self.get_tokenizer(cls_token="""(CLS)""" , sep_token="""(SEP)""" )
lowerCamelCase__ : Optional[int] = self.get_image_processor(do_normalize=UpperCamelCase__ )
lowerCamelCase__ : Optional[int] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token="""(CLS)""" , sep_token="""(SEP)""" , do_normalize=UpperCamelCase__ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase__ )
def lowerCamelCase_ ( self: Tuple ):
lowerCamelCase__ : Optional[Any] = self.get_image_processor()
lowerCamelCase__ : Optional[int] = self.get_tokenizer()
lowerCamelCase__ : int = ChineseCLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
lowerCamelCase__ : Tuple = self.prepare_image_inputs()
lowerCamelCase__ : str = image_processor(UpperCamelCase__ , return_tensors="""np""" )
lowerCamelCase__ : Optional[int] = processor(images=UpperCamelCase__ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCamelCase_ ( self: Tuple ):
lowerCamelCase__ : int = self.get_image_processor()
lowerCamelCase__ : Optional[int] = self.get_tokenizer()
lowerCamelCase__ : Union[str, Any] = ChineseCLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
lowerCamelCase__ : Union[str, Any] = """Alexandra，T-shirt的价格是15便士。"""
lowerCamelCase__ : Optional[Any] = processor(text=UpperCamelCase__ )
lowerCamelCase__ : Optional[Any] = tokenizer(UpperCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase_ ( self: Optional[Any] ):
lowerCamelCase__ : Optional[int] = self.get_image_processor()
lowerCamelCase__ : List[Any] = self.get_tokenizer()
lowerCamelCase__ : Optional[Any] = ChineseCLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
lowerCamelCase__ : Dict = """Alexandra，T-shirt的价格是15便士。"""
lowerCamelCase__ : Any = self.prepare_image_inputs()
lowerCamelCase__ : Optional[int] = processor(text=UpperCamelCase__ , images=UpperCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase__ ):
processor()
def lowerCamelCase_ ( self: Dict ):
lowerCamelCase__ : Dict = self.get_image_processor()
lowerCamelCase__ : Any = self.get_tokenizer()
lowerCamelCase__ : Union[str, Any] = ChineseCLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
lowerCamelCase__ : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCamelCase__ : Any = processor.batch_decode(UpperCamelCase__ )
lowerCamelCase__ : int = tokenizer.batch_decode(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( self: Optional[Any] ):
lowerCamelCase__ : Dict = self.get_image_processor()
lowerCamelCase__ : int = self.get_tokenizer()
lowerCamelCase__ : Optional[Any] = ChineseCLIPProcessor(tokenizer=UpperCamelCase__ , image_processor=UpperCamelCase__ )
lowerCamelCase__ : Optional[Any] = """Alexandra，T-shirt的价格是15便士。"""
lowerCamelCase__ : Tuple = self.prepare_image_inputs()
lowerCamelCase__ : str = processor(text=UpperCamelCase__ , images=UpperCamelCase__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 129 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue_model_parallelism.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
] )
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def A ( self : str ):
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding='utf-8' , check=UpperCamelCase__ , )
assert hasattr(self , 'env' )
def A ( self : Optional[Any] , UpperCamelCase__ : Any ):
"""simple docstring"""
UpperCamelCase = {
'enabled': True,
'processes_per_host': 8,
}
UpperCamelCase = {
'enabled': True,
'parameters': {
'microbatches': 4,
'placement_strategy': 'spread',
'pipeline': 'interleaved',
'optimize': 'speed',
'partitions': 4,
'ddp': True,
},
}
UpperCamelCase = {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}
UpperCamelCase = 'trainer' if self.script == 'run_glue.py' else 'smtrainer'
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=UpperCamelCase__ , instance_type=self.instance_type , debugger_hook_config=UpperCamelCase__ , hyperparameters={
**self.env.hyperparameters,
'model_name_or_path': self.model_name_or_path,
'max_steps': 5_0_0,
} , metric_definitions=self.env.metric_definitions , distribution=UpperCamelCase__ , py_version='py36' , )
def A ( self : Optional[int] , UpperCamelCase__ : int ):
"""simple docstring"""
TrainingJobAnalytics(UpperCamelCase__ ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def A ( self : List[str] , UpperCamelCase__ : Tuple ):
"""simple docstring"""
UpperCamelCase = self.create_estimator(UpperCamelCase__ )
# run training
estimator.fit()
# result dataframe
UpperCamelCase = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
UpperCamelCase = list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
UpperCamelCase = list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
UpperCamelCase = (
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds' , 9_9_9_9_9_9 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , 'w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} , UpperCamelCase__ )
| 28 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_lowerCamelCase : List[str] = "\\n@inproceedings{lin-2004-rouge,\n title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n author = \"Lin, Chin-Yew\",\n booktitle = \"Text Summarization Branches Out\",\n month = jul,\n year = \"2004\",\n address = \"Barcelona, Spain\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W04-1013\",\n pages = \"74--81\",\n}\n"
_lowerCamelCase : Optional[int] = "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"
_lowerCamelCase : str = "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n `\"rougeL\"`: Longest common subsequence based scoring.\n `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric('rouge')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n >>> print(results[\"rouge1\"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results[\"rouge1\"].mid.fmeasure)\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class SCREAMING_SNAKE_CASE ( datasets.Metric ):
"""simple docstring"""
def A ( self : Union[str, Any] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge'] , reference_urls=[
'https://en.wikipedia.org/wiki/ROUGE_(metric)',
'https://github.com/google-research/google-research/tree/master/rouge',
] , )
def A ( self : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : Optional[Any]=False ):
"""simple docstring"""
if rouge_types is None:
UpperCamelCase = ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
UpperCamelCase = rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__ , use_stemmer=UpperCamelCase__ )
if use_aggregator:
UpperCamelCase = scoring.BootstrapAggregator()
else:
UpperCamelCase = []
for ref, pred in zip(UpperCamelCase__ , UpperCamelCase__ ):
UpperCamelCase = scorer.score(UpperCamelCase__ , UpperCamelCase__ )
if use_aggregator:
aggregator.add_scores(UpperCamelCase__ )
else:
scores.append(UpperCamelCase__ )
if use_aggregator:
UpperCamelCase = aggregator.aggregate()
else:
UpperCamelCase = {}
for key in scores[0]:
UpperCamelCase = [score[key] for score in scores]
return result
| 28 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : str = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCAmelCase__ : Union[str, Any] = {
'vocab_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
},
'added_tokens.json': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
},
'merges_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
},
'tokenizer_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
},
}
UpperCAmelCase__ : Any = {
'RUCAIBox/mvp': 1024,
}
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__UpperCamelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : int = ['''input_ids''', '''attention_mask''']
__UpperCamelCase : int = MvpTokenizer
def __init__( self : Optional[Any] , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Union[str, Any]="replace" , lowerCAmelCase_ : Optional[int]="<s>" , lowerCAmelCase_ : Union[str, Any]="</s>" , lowerCAmelCase_ : int="</s>" , lowerCAmelCase_ : Dict="<s>" , lowerCAmelCase_ : Optional[int]="<unk>" , lowerCAmelCase_ : Any="<pad>" , lowerCAmelCase_ : List[str]="<mask>" , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : Optional[int]=True , **lowerCAmelCase_ : Any , ):
"""simple docstring"""
super().__init__(
lowerCAmelCase_ , lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , errors=lowerCAmelCase_ , bos_token=lowerCAmelCase_ , eos_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , add_prefix_space=lowerCAmelCase_ , trim_offsets=lowerCAmelCase_ , **lowerCAmelCase_ , )
_A: Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , lowerCAmelCase_ ) != add_prefix_space:
_A: str = getattr(lowerCAmelCase_ , pre_tok_state.pop('''type''' ) )
_A: List[str] = add_prefix_space
_A: Optional[int] = pre_tok_class(**lowerCAmelCase_ )
_A: List[Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_A: Union[str, Any] = '''post_processor'''
_A: Tuple = getattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_ )
if tokenizer_component_instance:
_A: str = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_A: int = tuple(state['''sep'''] )
if "cls" in state:
_A: Any = tuple(state['''cls'''] )
_A: Union[str, Any] = False
if state.get('''add_prefix_space''' , lowerCAmelCase_ ) != add_prefix_space:
_A: Optional[Any] = add_prefix_space
_A: str = True
if state.get('''trim_offsets''' , lowerCAmelCase_ ) != trim_offsets:
_A: str = trim_offsets
_A: int = True
if changes_to_apply:
_A: Optional[int] = getattr(lowerCAmelCase_ , state.pop('''type''' ) )
_A: Union[str, Any] = component_class(**lowerCAmelCase_ )
setattr(self.backend_tokenizer , lowerCAmelCase_ , lowerCAmelCase_ )
@property
def __magic_name__ ( self : Dict ):
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __magic_name__ ( self : Dict , lowerCAmelCase_ : int ):
"""simple docstring"""
_A: Dict = AddedToken(lowerCAmelCase_ , lstrip=lowerCAmelCase_ , rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) else value
_A: Optional[int] = value
def __magic_name__ ( self : List[str] , *lowerCAmelCase_ : Any , **lowerCAmelCase_ : List[str] ):
"""simple docstring"""
_A: List[str] = kwargs.get('''is_split_into_words''' , lowerCAmelCase_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*lowerCAmelCase_ , **lowerCAmelCase_ )
def __magic_name__ ( self : int , *lowerCAmelCase_ : Dict , **lowerCAmelCase_ : List[str] ):
"""simple docstring"""
_A: Union[str, Any] = kwargs.get('''is_split_into_words''' , lowerCAmelCase_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*lowerCAmelCase_ , **lowerCAmelCase_ )
def __magic_name__ ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ):
"""simple docstring"""
_A: List[str] = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ )
return tuple(lowerCAmelCase_ )
def __magic_name__ ( self : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : int=None ):
"""simple docstring"""
_A: Optional[int] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __magic_name__ ( self : str , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None ):
"""simple docstring"""
_A: Optional[int] = [self.sep_token_id]
_A: List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 365 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCAmelCase :
'''simple docstring'''
__UpperCamelCase : Any = MBartConfig
__UpperCamelCase : Tuple = {}
__UpperCamelCase : Dict = '''gelu'''
def __init__( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any]=1_3 , lowerCAmelCase_ : Dict=7 , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : Union[str, Any]=9_9 , lowerCAmelCase_ : Dict=3_2 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : Union[str, Any]=3_7 , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : List[str]=2_0 , lowerCAmelCase_ : str=2 , lowerCAmelCase_ : Optional[int]=1 , lowerCAmelCase_ : List[Any]=0 , ):
"""simple docstring"""
_A: Union[str, Any] = parent
_A: List[Any] = batch_size
_A: Dict = seq_length
_A: Dict = is_training
_A: str = use_labels
_A: int = vocab_size
_A: str = hidden_size
_A: Tuple = num_hidden_layers
_A: Optional[Any] = num_attention_heads
_A: Tuple = intermediate_size
_A: int = hidden_dropout_prob
_A: Tuple = attention_probs_dropout_prob
_A: Tuple = max_position_embeddings
_A: Dict = eos_token_id
_A: int = pad_token_id
_A: Any = bos_token_id
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A: Optional[int] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_A: Dict = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_A: List[Any] = tf.concat([input_ids, eos_tensor] , axis=1 )
_A: Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A: int = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_A: Any = prepare_mbart_inputs_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return config, inputs_dict
def __magic_name__ ( self : Union[str, Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : List[str] ):
"""simple docstring"""
_A: Tuple = TFMBartModel(config=lowerCAmelCase_ ).get_decoder()
_A: List[str] = inputs_dict['''input_ids''']
_A: Tuple = input_ids[:1, :]
_A: List[Any] = inputs_dict['''attention_mask'''][:1, :]
_A: str = inputs_dict['''head_mask''']
_A: Optional[Any] = 1
# first forward pass
_A: Any = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , head_mask=lowerCAmelCase_ , use_cache=lowerCAmelCase_ )
_A , _A: List[str] = outputs.to_tuple()
_A: Dict = past_key_values[1]
def lowerCamelCase__ ( a , a , a , a=None , a=None , a=None , a=None , a=None , ) -> Tuple:
if attention_mask is None:
_A: Union[str, Any] = tf.cast(tf.math.not_equal(a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_A: Optional[int] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_A: Tuple = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_A: Union[str, Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_A: Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
__UpperCamelCase : int = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
__UpperCamelCase : Tuple = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
__UpperCamelCase : List[Any] = True
__UpperCamelCase : int = False
__UpperCamelCase : Optional[Any] = False
def __magic_name__ ( self : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int ):
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def __magic_name__ ( self : Any ):
"""simple docstring"""
_A: Dict = TFMBartModelTester(self )
_A: Tuple = ConfigTester(self , config_class=lowerCAmelCase_ )
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __magic_name__ ( self : Optional[Any] ):
"""simple docstring"""
_A: str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_ )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
__UpperCamelCase : List[str] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
__UpperCamelCase : Union[str, Any] = '''facebook/mbart-large-en-ro'''
@cached_property
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __magic_name__ ( self : str ):
"""simple docstring"""
_A: Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __magic_name__ ( self : Union[str, Any] , **lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Optional[Any] = self.translate_src_text(**lowerCAmelCase_ )
self.assertListEqual(self.expected_text , lowerCAmelCase_ )
def __magic_name__ ( self : Dict , **lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Any = self.tokenizer(self.src_text , **lowerCAmelCase_ , return_tensors='''tf''' )
_A: Any = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
_A: Optional[Any] = self.tokenizer.batch_decode(lowerCAmelCase_ , skip_special_tokens=lowerCAmelCase_ )
return generated_words
@slow
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 301 | 0 |
'''simple docstring'''
from copy import deepcopy
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : list[int] | None = None , _UpperCAmelCase : int | None = None ):
"""simple docstring"""
if arr is None and size is not None:
UpperCAmelCase__ = size
UpperCAmelCase__ = [0] * size
elif arr is not None:
self.init(_UpperCAmelCase )
else:
raise ValueError("""Either arr or size must be specified""" )
def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : list[int] ):
"""simple docstring"""
UpperCAmelCase__ = len(_UpperCAmelCase )
UpperCAmelCase__ = deepcopy(_UpperCAmelCase )
for i in range(1 , self.size ):
UpperCAmelCase__ = self.next_(_UpperCAmelCase )
if j < self.size:
self.tree[j] += self.tree[i]
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
UpperCAmelCase__ = self.next_(_UpperCAmelCase )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase : int ):
"""simple docstring"""
return index + (index & (-index))
@staticmethod
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase : int ):
"""simple docstring"""
return index - (index & (-index))
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
"""simple docstring"""
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
UpperCAmelCase__ = self.next_(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
"""simple docstring"""
self.add(_UpperCAmelCase , value - self.get(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : int ):
"""simple docstring"""
if right == 0:
return 0
UpperCAmelCase__ = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
UpperCAmelCase__ = self.prev(_UpperCAmelCase )
return result
def SCREAMING_SNAKE_CASE__ ( self : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
"""simple docstring"""
return self.prefix(_UpperCAmelCase ) - self.prefix(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : int ):
"""simple docstring"""
return self.query(_UpperCAmelCase , index + 1 )
def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : int ):
"""simple docstring"""
value -= self.tree[0]
if value < 0:
return -1
UpperCAmelCase__ = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
UpperCAmelCase__ = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 346 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {'vocab_file': 'spiece.model'}
UpperCAmelCase_ = {
'vocab_file': {
'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
}
}
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Any=False , _UpperCAmelCase : int=True , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : int="</s>" , _UpperCAmelCase : Dict="<unk>" , _UpperCAmelCase : Tuple="<sep>" , _UpperCAmelCase : List[Any]="<pad>" , _UpperCAmelCase : int="<cls>" , _UpperCAmelCase : Union[str, Any]="<mask>" , _UpperCAmelCase : List[str]=["<eop>", "<eod>"] , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : int , ):
"""simple docstring"""
UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token
UpperCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_UpperCAmelCase , remove_space=_UpperCAmelCase , keep_accents=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , )
UpperCAmelCase__ = 3
UpperCAmelCase__ = do_lower_case
UpperCAmelCase__ = remove_space
UpperCAmelCase__ = keep_accents
UpperCAmelCase__ = vocab_file
UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_UpperCAmelCase )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
"""You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
"""See https://pypi.org/project/jieba/ for installation.""" )
UpperCAmelCase__ = jieba
UpperCAmelCase__ = str.maketrans(""" \n""" , """\u2582\u2583""" )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
return len(self.sp_model )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Dict ):
"""simple docstring"""
UpperCAmelCase__ = self.__dict__.copy()
UpperCAmelCase__ = None
return state
def __setstate__( self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCAmelCase__ = {}
UpperCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Optional[Any] ):
"""simple docstring"""
if self.remove_space:
UpperCAmelCase__ = """ """.join(inputs.strip().split() )
else:
UpperCAmelCase__ = inputs
UpperCAmelCase__ = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
UpperCAmelCase__ = unicodedata.normalize("""NFKD""" , _UpperCAmelCase )
UpperCAmelCase__ = """""".join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase )] )
if self.do_lower_case:
UpperCAmelCase__ = outputs.lower()
return outputs
def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : str ):
"""simple docstring"""
UpperCAmelCase__ = self.preprocess_text(_UpperCAmelCase )
UpperCAmelCase__ = self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase )
UpperCAmelCase__ = []
for piece in pieces:
if len(_UpperCAmelCase ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
UpperCAmelCase__ = self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
UpperCAmelCase__ = cur_pieces[1:]
else:
UpperCAmelCase__ = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_UpperCAmelCase )
else:
new_pieces.append(_UpperCAmelCase )
return new_pieces
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
return self.sp_model.PieceToId(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Any ):
"""simple docstring"""
return self.sp_model.IdToPiece(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : Dict ):
"""simple docstring"""
UpperCAmelCase__ = """""".join(_UpperCAmelCase ).replace(_UpperCAmelCase , """ """ ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
"""simple docstring"""
UpperCAmelCase__ = [self.sep_token_id]
UpperCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase )
if token_ids_a is not None:
return ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1, 1]
return ([0] * len(_UpperCAmelCase )) + [1, 1]
def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
"""simple docstring"""
UpperCAmelCase__ = [self.sep_token_id]
UpperCAmelCase__ = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(_UpperCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase__ = os.path.join(
_UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCAmelCase , """wb""" ) as fi:
UpperCAmelCase__ = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = super()._decode(*_UpperCAmelCase , **_UpperCAmelCase )
UpperCAmelCase__ = text.replace(""" """ , """""" ).replace("""\u2582""" , """ """ ).replace("""\u2583""" , """\n""" )
return text
| 346 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
snake_case__ : str = logging.get_logger(__name__)
class snake_case_( __SCREAMING_SNAKE_CASE ):
def __init__( self : List[str] , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : float , **UpperCamelCase_ : Any ):
lowerCAmelCase : Optional[Any] = feature_size
lowerCAmelCase : Dict = sampling_rate
lowerCAmelCase : List[Any] = padding_value
lowerCAmelCase : Dict = kwargs.pop('''padding_side''' , '''right''' )
lowerCAmelCase : Tuple = kwargs.pop('''return_attention_mask''' , UpperCamelCase__ )
super().__init__(**UpperCamelCase__ )
def lowerCamelCase__ ( self : Any , UpperCamelCase_ : Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , UpperCamelCase_ : Union[bool, str, PaddingStrategy] = True , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[bool] = None , UpperCamelCase_ : Optional[Union[str, TensorType]] = None , ):
if isinstance(UpperCamelCase__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
lowerCAmelCase : Any = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
F''' to this method that includes {self.model_input_names[0]}, but you provided'''
F''' {list(processed_features.keys() )}''' )
lowerCAmelCase : Any = processed_features[self.model_input_names[0]]
lowerCAmelCase : str = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(UpperCamelCase__ ) == 0:
if return_attention_mask:
lowerCAmelCase : Any = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
lowerCAmelCase : int = required_input[0]
if isinstance(UpperCamelCase__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
lowerCAmelCase : int = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(UpperCamelCase__ ):
lowerCAmelCase : Any = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(UpperCamelCase__ ):
lowerCAmelCase : Tuple = '''tf'''
elif is_torch_tensor(UpperCamelCase__ ):
lowerCAmelCase : Tuple = '''pt'''
elif isinstance(UpperCamelCase__ , (int, float, list, tuple, np.ndarray) ):
lowerCAmelCase : Any = '''np'''
else:
raise ValueError(
F'''type of {first_element} unknown: {type(UpperCamelCase__ )}. '''
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
lowerCAmelCase : Dict = to_numpy(UpperCamelCase__ )
else:
lowerCAmelCase : int = [to_numpy(UpperCamelCase__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
lowerCAmelCase : Tuple = self._get_padding_strategies(padding=UpperCamelCase__ , max_length=UpperCamelCase__ )
lowerCAmelCase : Any = processed_features[self.model_input_names[0]]
lowerCAmelCase : Dict = len(UpperCamelCase__ )
if not all(len(UpperCamelCase__ ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
lowerCAmelCase : Union[str, Any] = []
for i in range(UpperCamelCase__ ):
lowerCAmelCase : Dict = {k: v[i] for k, v in processed_features.items()}
# truncation
lowerCAmelCase : int = self._truncate(
UpperCamelCase__ , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , truncation=UpperCamelCase__ , )
truncated_inputs.append(UpperCamelCase__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
lowerCAmelCase : Optional[Any] = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
lowerCAmelCase : int = PaddingStrategy.MAX_LENGTH
lowerCAmelCase : int = {}
for i in range(UpperCamelCase__ ):
# padding
lowerCAmelCase : Optional[Any] = self._pad(
truncated_inputs[i] , max_length=UpperCamelCase__ , padding_strategy=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , )
for key, value in outputs.items():
if key not in batch_outputs:
lowerCAmelCase : str = []
if value.dtype is np.dtype(np.floataa ):
lowerCAmelCase : Dict = value.astype(np.floataa )
batch_outputs[key].append(UpperCamelCase__ )
return BatchFeature(UpperCamelCase__ , tensor_type=UpperCamelCase__ )
def lowerCamelCase__ ( self : str , UpperCamelCase_ : Union[Dict[str, np.ndarray], BatchFeature] , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[bool] = None , ):
lowerCAmelCase : Optional[int] = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
lowerCAmelCase : Tuple = len(UpperCamelCase__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowerCAmelCase : Any = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowerCAmelCase : Dict = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(UpperCamelCase__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
lowerCAmelCase : Optional[int] = np.ones(len(UpperCamelCase__ ) , dtype=np.intaa )
if needs_to_be_padded:
lowerCAmelCase : Union[str, Any] = max_length - len(UpperCamelCase__ )
if self.padding_side == "right":
if return_attention_mask:
lowerCAmelCase : str = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
lowerCAmelCase : Tuple = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
lowerCAmelCase : Optional[int] = np.pad(
UpperCamelCase__ , UpperCamelCase__ , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
lowerCAmelCase : Optional[int] = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
lowerCAmelCase : int = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
lowerCAmelCase : int = np.pad(
UpperCamelCase__ , UpperCamelCase__ , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def lowerCamelCase__ ( self : Optional[Any] , UpperCamelCase_ : Union[Dict[str, np.ndarray], BatchFeature] , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[bool] = None , ):
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
lowerCAmelCase : Union[str, Any] = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowerCAmelCase : List[str] = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowerCAmelCase : List[Any] = len(UpperCamelCase__ ) > max_length
if needs_to_be_truncated:
lowerCAmelCase : Any = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
lowerCAmelCase : List[str] = processed_features['''attention_mask'''][:max_length]
return processed_features
def lowerCamelCase__ ( self : Any , UpperCamelCase_ : Tuple=False , UpperCamelCase_ : Optional[Any]=None ):
if padding is not False:
if padding is True:
lowerCAmelCase : Tuple = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowerCAmelCase : Any = PaddingStrategy(UpperCamelCase__ )
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowerCAmelCase : Any = padding
else:
lowerCAmelCase : List[Any] = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 367 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case__ : Optional[Any] = '''
import os
'''
snake_case__ : Tuple = '''
def foo():
import os
return False
'''
snake_case__ : Any = '''
def foo():
def bar():
if True:
import os
return False
return bar()
'''
snake_case__ : Any = '''
import os
try:
import bar
except ImportError:
raise ValueError()
'''
snake_case__ : int = '''
import os
def foo():
try:
import bar
except ImportError:
raise ValueError()
'''
snake_case__ : Any = '''
import os
try:
import bar
except (ImportError, AttributeError):
raise ValueError()
'''
snake_case__ : List[str] = '''
import os
try:
import bar
except ImportError as e:
raise ValueError()
'''
snake_case__ : int = '''
import os
try:
import bar
except:
raise ValueError()
'''
snake_case__ : List[Any] = '''
import os
try:
import bar
import baz
except ImportError:
raise ValueError()
'''
snake_case__ : Optional[int] = '''
import os
try:
import bar
import baz
except ImportError:
x = 1
raise ValueError()
'''
snake_case__ : Any = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case''' , _snake_case )
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : List[str] ):
lowerCAmelCase : Dict = os.path.join(_snake_case , '''test_file.py''' )
with open(_snake_case , '''w''' ) as _tmp_file:
_tmp_file.write(_snake_case )
lowerCAmelCase : Tuple = get_imports(_snake_case )
assert parsed_imports == ["os"]
| 314 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowercase : Union[str, Any] = "Usage of script: script_name <size_of_canvas:int>"
_lowercase : Union[str, Any] = [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)
def snake_case_ ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : Union[str, Any] = [[False for i in range(__SCREAMING_SNAKE_CASE )] for j in range(__SCREAMING_SNAKE_CASE )]
return canvas
def snake_case_ ( __SCREAMING_SNAKE_CASE : list[list[bool]] ):
"""simple docstring"""
for i, row in enumerate(__SCREAMING_SNAKE_CASE ):
for j, _ in enumerate(__SCREAMING_SNAKE_CASE ):
lowercase_ : Union[str, Any] = bool(random.getrandbits(1 ) )
def snake_case_ ( __SCREAMING_SNAKE_CASE : list[list[bool]] ):
"""simple docstring"""
lowercase_ : str = np.array(__SCREAMING_SNAKE_CASE )
lowercase_ : Any = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__SCREAMING_SNAKE_CASE ):
for c, pt in enumerate(__SCREAMING_SNAKE_CASE ):
lowercase_ : List[Any] = __judge_point(
__SCREAMING_SNAKE_CASE , current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
lowercase_ : Dict = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
lowercase_ : list[list[bool]] = current_canvas.tolist()
return return_canvas
def snake_case_ ( __SCREAMING_SNAKE_CASE : bool , __SCREAMING_SNAKE_CASE : list[list[bool]] ):
"""simple docstring"""
lowercase_ : List[str] = 0
lowercase_ : Tuple = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
lowercase_ : Union[str, Any] = pt
if pt:
if alive < 2:
lowercase_ : Optional[int] = False
elif alive == 2 or alive == 3:
lowercase_ : int = True
elif alive > 3:
lowercase_ : Any = False
else:
if alive == 3:
lowercase_ : str = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
_lowercase : Tuple = int(sys.argv[1])
# main working structure of this module.
_lowercase : int = create_canvas(canvas_size)
seed(c)
_lowercase , _lowercase : List[str] = plt.subplots()
fig.show()
_lowercase : str = ListedColormap(["w", "k"])
try:
while True:
_lowercase : str = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 93 | '''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
def __lowerCAmelCase ( UpperCamelCase__=None , UpperCamelCase__=None ) -> int:
return field(default_factory=lambda: default , metadata=UpperCamelCase__ )
@dataclass
class a__ :
lowerCamelCase : List[str] =list_field(
default=[] , metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
} , )
lowerCamelCase : List[int] =list_field(
default=[8] , metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
lowerCamelCase : List[int] =list_field(
default=[8, 3_2, 1_2_8, 5_1_2] , metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} , )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} , )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} , )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Use FP16 to accelerate inference."} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Benchmark training of model"} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Verbose memory tracing"} )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} , )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} , )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Trace memory line by line"} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Save result to a CSV file"} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Save all print statements in a log file"} )
lowerCamelCase : bool =field(default=UpperCAmelCase__ , metadata={"help": "Whether to print environment information"} )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
} , )
lowerCamelCase : str =field(
default=F'''inference_time_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving time results to csv."} , )
lowerCamelCase : str =field(
default=F'''inference_memory_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving memory results to csv."} , )
lowerCamelCase : str =field(
default=F'''train_time_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving time results to csv for training."} , )
lowerCamelCase : str =field(
default=F'''train_memory_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving memory results to csv for training."} , )
lowerCamelCase : str =field(
default=F'''env_info_{round(time() )}.csv''' , metadata={"help": "CSV filename used if saving environment information."} , )
lowerCamelCase : str =field(
default=F'''log_{round(time() )}.csv''' , metadata={"help": "Log filename used if print statements are saved in log."} , )
lowerCamelCase : int =field(default=3 , metadata={"help": "Times an experiment will be run."} )
lowerCamelCase : bool =field(
default=UpperCAmelCase__ , metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} , )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
warnings.warn(
f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
''' are deprecated in general and it is advised to use external Benchmarking libraries '''
''' to benchmark Transformer models.''' , a , )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
if len(self.models ) <= 0:
raise ValueError(
'''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
''' bert-base-cased` or `args.models = [\'bert-base-cased\'].''' )
return self.models
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
if not self.multi_process:
return False
elif self.is_tpu:
logger.info('''Multiprocessing is currently not possible on TPU.''' )
return False
else:
return True
| 67 | 0 |
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
UpperCamelCase__ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 87 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
UpperCamelCase__ = random.Random()
def _UpperCamelCase (a__ :Any , a__ :Union[str, Any]=1.0 , a__ :Tuple=None , a__ :str=None ):
"""simple docstring"""
if rng is None:
UpperCamelCase__ = global_rng
UpperCamelCase__ = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=7 , __lowerCAmelCase=400 , __lowerCAmelCase=2000 , __lowerCAmelCase=10 , __lowerCAmelCase=160 , __lowerCAmelCase=8 , __lowerCAmelCase=0.0 , __lowerCAmelCase=4000 , __lowerCAmelCase=False , __lowerCAmelCase=True , ):
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = min_seq_length
UpperCamelCase__ = max_seq_length
UpperCamelCase__ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase__ = padding_value
UpperCamelCase__ = sampling_rate
UpperCamelCase__ = return_attention_mask
UpperCamelCase__ = do_normalize
UpperCamelCase__ = feature_size
UpperCamelCase__ = chunk_length
UpperCamelCase__ = hop_length
def _lowerCamelCase ( self ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _lowerCamelCase ( self , __lowerCAmelCase=False , __lowerCAmelCase=False ):
def _flatten(__lowerCAmelCase ):
return list(itertools.chain(*__lowerCAmelCase ) )
if equal_length:
UpperCamelCase__ = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase__ = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase__ = [np.asarray(__lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case : int = WhisperFeatureExtractor if is_speech_available() else None
def _lowerCamelCase ( self ):
UpperCamelCase__ = WhisperFeatureExtractionTester(self )
def _lowerCamelCase ( self ):
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase__ = feat_extract_first.save_pretrained(__lowerCAmelCase )[0]
check_json_file_has_correct_format(__lowerCAmelCase )
UpperCamelCase__ = self.feature_extraction_class.from_pretrained(__lowerCAmelCase )
UpperCamelCase__ = feat_extract_first.to_dict()
UpperCamelCase__ = feat_extract_second.to_dict()
UpperCamelCase__ = feat_extract_first.mel_filters
UpperCamelCase__ = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def _lowerCamelCase ( self ):
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase__ = os.path.join(__lowerCAmelCase , """feat_extract.json""" )
feat_extract_first.to_json_file(__lowerCAmelCase )
UpperCamelCase__ = self.feature_extraction_class.from_json_file(__lowerCAmelCase )
UpperCamelCase__ = feat_extract_first.to_dict()
UpperCamelCase__ = feat_extract_second.to_dict()
UpperCamelCase__ = feat_extract_first.mel_filters
UpperCamelCase__ = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase ) )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def _lowerCamelCase ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase__ = [np.asarray(__lowerCAmelCase ) for speech_input in speech_inputs]
# Test feature size
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , padding="""max_length""" , return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
UpperCamelCase__ = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_features
UpperCamelCase__ = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
# Test batched
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase__ = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase__ = np.asarray(__lowerCAmelCase )
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
# Test truncation required
UpperCamelCase__ = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
UpperCamelCase__ = [np.asarray(__lowerCAmelCase ) for speech_input in speech_inputs]
UpperCamelCase__ = [x[: feature_extractor.n_samples] for x in speech_inputs]
UpperCamelCase__ = [np.asarray(__lowerCAmelCase ) for speech_input in speech_inputs_truncated]
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
def _lowerCamelCase ( self ):
import torch
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ = np.random.rand(100 , 32 ).astype(np.floataa )
UpperCamelCase__ = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase__ = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
UpperCamelCase__ = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def _lowerCamelCase ( self , __lowerCAmelCase ):
UpperCamelCase__ = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
UpperCamelCase__ = ds.sort("""id""" ).select(range(__lowerCAmelCase ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def _lowerCamelCase ( self ):
# fmt: off
UpperCamelCase__ = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
UpperCamelCase__ = self._load_datasamples(1 )
UpperCamelCase__ = WhisperFeatureExtractor()
UpperCamelCase__ = feature_extractor(__lowerCAmelCase , return_tensors="""pt""" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __lowerCAmelCase , atol=1E-4 ) )
def _lowerCamelCase ( self ):
UpperCamelCase__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ = self._load_datasamples(1 )[0]
UpperCamelCase__ = ((audio - audio.min()) / (audio.max() - audio.min())) * 65535 # Rescale to [0, 65535] to show issue
UpperCamelCase__ = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__lowerCAmelCase )[0]
self.assertTrue(np.all(np.mean(__lowerCAmelCase ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(__lowerCAmelCase ) - 1 ) < 1E-3 ) )
| 87 | 1 |
import requests
A : List[Any] = '''''' # <-- Put your OpenWeatherMap appid here!
A : Dict = '''https://api.openweathermap.org/data/2.5/'''
def __lowerCamelCase ( __a :str = "Chicago" , __a :str = APPID ) -> dict:
"""simple docstring"""
return requests.get(URL_BASE + """weather""" , params=locals() ).json()
def __lowerCamelCase ( __a :str = "Kolkata, India" , __a :str = APPID ) -> dict:
"""simple docstring"""
return requests.get(URL_BASE + """forecast""" , params=locals() ).json()
def __lowerCamelCase ( __a :float = 55.68 , __a :float = 12.57 , __a :str = APPID ) -> dict:
"""simple docstring"""
return requests.get(URL_BASE + """onecall""" , params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
A : int = input('''Enter a location:''').strip()
if location:
pprint(current_weather(location))
else:
break
| 274 |
def __lowerCamelCase ( __a :str ) -> list:
"""simple docstring"""
A__ = [0] * len(__a )
for i in range(1 , len(__a ) ):
# use last results for better performance - dynamic programming
A__ = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
A__ = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
A__ = j
return prefix_result
def __lowerCamelCase ( __a :str ) -> int:
"""simple docstring"""
return max(prefix_function(__a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 274 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase__ : Any = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Union[str, Any] = ['SpeechEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = ['FlaxSpeechEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
UpperCamelCase__ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 164 | 0 |
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCAmelCase_ :
def __init__( self, __a, __a):
'''simple docstring'''
if len(__a) != degree + 1:
raise ValueError(
"The number of coefficients should be equal to the degree + 1.")
_lowerCAmelCase : list[float] = list(__a)
_lowerCAmelCase : Any = degree
def __add__( self, __a):
'''simple docstring'''
if self.degree > polynomial_a.degree:
_lowerCAmelCase : Optional[Any] = self.coefficients[:]
for i in range(polynomial_a.degree + 1):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree, __a)
else:
_lowerCAmelCase : Union[str, Any] = polynomial_a.coefficients[:]
for i in range(self.degree + 1):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree, __a)
def __sub__( self, __a):
'''simple docstring'''
return self + polynomial_a * Polynomial(0, [-1])
def __neg__( self):
'''simple docstring'''
return Polynomial(self.degree, [-c for c in self.coefficients])
def __mul__( self, __a):
'''simple docstring'''
_lowerCAmelCase : list[float] = [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 snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : int | float = 0
for i in range(self.degree + 1):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = ""
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):
'''simple docstring'''
return self.__str__()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : list[float] = [0] * self.degree
for i in range(self.degree):
_lowerCAmelCase : Optional[Any] = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1, __a)
def snake_case__ ( self, __a = 0):
'''simple docstring'''
_lowerCAmelCase : list[float] = [0] * (self.degree + 2)
_lowerCAmelCase : Dict = constant
for i in range(self.degree + 1):
_lowerCAmelCase : Tuple = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1, __a)
def __eq__( self, __a):
'''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, __a):
'''simple docstring'''
return not self.__eq__(__a)
| 36 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
__A = "path-to-your-trained-model"
__A = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
__A = "A photo of sks dog in a bucket"
__A = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
| 148 | 0 |
import string
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
A_ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
A_ = string.ascii_uppercase.find(__UpperCamelCase )
A_ = num - key
if num < 0:
A_ = num + len(string.ascii_uppercase )
A_ = translated + string.ascii_uppercase[num]
else:
A_ = translated + symbol
print(f'''Decryption using Key #{key}: {translated}''' )
def __snake_case ( ):
"""simple docstring"""
A_ = input("Encrypted message: " )
A_ = message.upper()
decrypt(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 329 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ):
A_ = tempfile.mkdtemp()
A_ = BlipImageProcessor()
A_ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
A_ = BlipProcessor(UpperCAmelCase , UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __A ( self : Optional[int] , **UpperCAmelCase : Union[str, Any] ):
return AutoProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase ).tokenizer
def __A ( self : Optional[Any] , **UpperCAmelCase : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase ).image_processor
def __A ( self : Any ):
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ):
A_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ = [Image.fromarray(np.moveaxis(UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : Any ):
A_ = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
A_ = self.get_image_processor(do_normalize=UpperCAmelCase , padding_value=1.0 )
A_ = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCAmelCase )
def __A ( self : Dict ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = self.prepare_image_inputs()
A_ = image_processor(UpperCAmelCase , return_tensors="np" )
A_ = processor(images=UpperCAmelCase , return_tensors="np" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __A ( self : int ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = processor(text=UpperCAmelCase )
A_ = tokenizer(UpperCAmelCase , return_token_type_ids=UpperCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Tuple ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = self.prepare_image_inputs()
A_ = processor(text=UpperCAmelCase , images=UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase ):
processor()
def __A ( self : Any ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ = processor.batch_decode(UpperCAmelCase )
A_ = tokenizer.batch_decode(UpperCAmelCase )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
def __A ( self : Optional[Any] ):
A_ = self.get_image_processor()
A_ = self.get_tokenizer()
A_ = BlipProcessor(tokenizer=UpperCAmelCase , image_processor=UpperCAmelCase )
A_ = "lower newer"
A_ = self.prepare_image_inputs()
A_ = processor(text=UpperCAmelCase , images=UpperCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] ) | 329 | 1 |
def __lowerCamelCase ( __a :int , __a :bool = False ) -> bool:
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
A__ = [
2_0_4_7,
1_3_7_3_6_5_3,
2_5_3_2_6_0_0_1,
3_2_1_5_0_3_1_7_5_1,
2_1_5_2_3_0_2_8_9_8_7_4_7,
3_4_7_4_7_4_9_6_6_0_3_8_3,
3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
1,
3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
1,
1,
3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
]
A__ = [2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
for idx, _p in enumerate(__a , 1 ):
if n < _p:
# then we have our last prime to check
A__ = primes[:idx]
break
A__ , A__ = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
A__ = False
for r in range(__a ):
A__ = pow(__a , d * 2**r , __a )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
A__ = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
assert not miller_rabin(5_6_1 )
assert miller_rabin(5_6_3 )
# 2047
assert not miller_rabin(8_3_8_2_0_1 )
assert miller_rabin(8_3_8_2_0_7 )
# 1_373_653
assert not miller_rabin(1_7_3_1_6_0_0_1 )
assert miller_rabin(1_7_3_1_6_0_1_7 )
# 25_326_001
assert not miller_rabin(3_0_7_8_3_8_6_6_4_1 )
assert miller_rabin(3_0_7_8_3_8_6_6_5_3 )
# 3_215_031_751
assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1 )
assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9 )
# 2_152_302_898_747
assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7 )
assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7 )
# 3_474_749_660_383
assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1 )
assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7 )
# 341_550_071_728_321
assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1 )
assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7 )
assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3 )
assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 274 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
A : str = 0
A : Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
A : Dict = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
A : Union[str, Any] = tuple[int, int]
class A :
'''simple docstring'''
def __init__( self : int , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : Node | None , ) -> None:
"""simple docstring"""
A__ = pos_x
A__ = pos_y
A__ = (pos_y, pos_x)
A__ = goal_x
A__ = goal_y
A__ = g_cost
A__ = parent
A__ = self.calculate_heuristic()
A__ = self.g_cost + self.h_cost
def a_ ( self : Dict ) -> float:
"""simple docstring"""
A__ = self.pos_x - self.goal_x
A__ = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(__lowerCAmelCase ) + abs(__lowerCAmelCase )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : int , __lowerCAmelCase : Node ) -> bool:
"""simple docstring"""
return self.f_cost < other.f_cost
class A :
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : TPosition , __lowerCAmelCase : TPosition ) -> Tuple:
"""simple docstring"""
A__ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , __lowerCAmelCase )
A__ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_99_99 , __lowerCAmelCase )
A__ = [self.start]
A__ = []
A__ = False
def a_ ( self : List[str] ) -> list[TPosition]:
"""simple docstring"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
A__ = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(__lowerCAmelCase )
self.closed_nodes.append(__lowerCAmelCase )
A__ = self.get_successors(__lowerCAmelCase )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(__lowerCAmelCase )
else:
# retrieve the best current path
A__ = self.open_nodes.pop(self.open_nodes.index(__lowerCAmelCase ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(__lowerCAmelCase )
else:
self.open_nodes.append(__lowerCAmelCase )
return [self.start.pos]
def a_ ( self : Optional[Any] , __lowerCAmelCase : Node ) -> list[Node]:
"""simple docstring"""
A__ = []
for action in delta:
A__ = parent.pos_x + action[1]
A__ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__lowerCAmelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
__lowerCAmelCase , __lowerCAmelCase , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , __lowerCAmelCase , ) )
return successors
def a_ ( self : List[Any] , __lowerCAmelCase : Node | None ) -> list[TPosition]:
"""simple docstring"""
A__ = node
A__ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
A__ = current_node.parent
path.reverse()
return path
class A :
'''simple docstring'''
def __init__( self : Optional[Any] , __lowerCAmelCase : TPosition , __lowerCAmelCase : TPosition ) -> None:
"""simple docstring"""
A__ = AStar(__lowerCAmelCase , __lowerCAmelCase )
A__ = AStar(__lowerCAmelCase , __lowerCAmelCase )
A__ = False
def a_ ( self : int ) -> list[TPosition]:
"""simple docstring"""
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
A__ = self.fwd_astar.open_nodes.pop(0 )
A__ = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
__lowerCAmelCase , __lowerCAmelCase )
self.fwd_astar.closed_nodes.append(__lowerCAmelCase )
self.bwd_astar.closed_nodes.append(__lowerCAmelCase )
A__ = current_bwd_node
A__ = current_fwd_node
A__ = {
self.fwd_astar: self.fwd_astar.get_successors(__lowerCAmelCase ),
self.bwd_astar: self.bwd_astar.get_successors(__lowerCAmelCase ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(__lowerCAmelCase )
else:
# retrieve the best current path
A__ = astar.open_nodes.pop(
astar.open_nodes.index(__lowerCAmelCase ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(__lowerCAmelCase )
else:
astar.open_nodes.append(__lowerCAmelCase )
return [self.fwd_astar.start.pos]
def a_ ( self : List[str] , __lowerCAmelCase : Node , __lowerCAmelCase : Node ) -> list[TPosition]:
"""simple docstring"""
A__ = self.fwd_astar.retrace_path(__lowerCAmelCase )
A__ = self.bwd_astar.retrace_path(__lowerCAmelCase )
bwd_path.pop()
bwd_path.reverse()
A__ = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
A : Optional[int] = (0, 0)
A : int = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
A : Dict = time.time()
A : Optional[Any] = AStar(init, goal)
A : Optional[int] = a_star.search()
A : Optional[int] = time.time() - start_time
print(F'''AStar execution time = {end_time:f} seconds''')
A : Dict = time.time()
A : Tuple = BidirectionalAStar(init, goal)
A : List[Any] = time.time() - bd_start_time
print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')
| 274 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> set:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE__ : List[str] = get_edges(__lowerCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
SCREAMING_SNAKE_CASE__ : List[str] = edges.pop()
chosen_vertices.add(__lowerCAmelCase )
chosen_vertices.add(__lowerCAmelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(__lowerCAmelCase )
return chosen_vertices
def _lowercase ( __lowerCAmelCase ) -> set:
SCREAMING_SNAKE_CASE__ : Tuple = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 369 |
"""simple docstring"""
from math import loga
def _lowercase ( __lowerCAmelCase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56 | 0 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase_ = logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests.""" )
@require_torch
@require_tf
@slow
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Path , _UpperCAmelCase : Union[str, None] = None , _UpperCAmelCase : Union[List[str], None] = None , _UpperCAmelCase : Union[str, List[str], None] = None , _UpperCAmelCase : bool = True , ):
"""simple docstring"""
UpperCAmelCase__ = [file for file in os.listdir(_UpperCAmelCase ) if os.path.isfile(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )]
if identifier is not None:
UpperCAmelCase__ = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
for n_ in n_identifier:
UpperCAmelCase__ = [file for file in files if n_ not in file]
else:
UpperCAmelCase__ = [file for file in files if n_identifier not in file]
UpperCAmelCase__ = ignore_files or []
ignore_files.append("""__init__.py""" )
UpperCAmelCase__ = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print("""Testing""" , _UpperCAmelCase )
if only_modules:
UpperCAmelCase__ = file.split(""".""" )[0]
try:
UpperCAmelCase__ = getattr(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = doctest.DocTestSuite(_UpperCAmelCase )
UpperCAmelCase__ = unittest.TextTestRunner().run(_UpperCAmelCase )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(f'''{module_identifier} is not a module.''' )
else:
UpperCAmelCase__ = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = Path("""src/transformers""" )
UpperCAmelCase__ = """modeling"""
UpperCAmelCase__ = [
"""modeling_ctrl.py""",
"""modeling_tf_ctrl.py""",
]
self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase , ignore_files=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = Path("""src/transformers""" )
UpperCAmelCase__ = """tokenization"""
self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = Path("""src/transformers""" )
UpperCAmelCase__ = """configuration"""
self.analyze_directory(_UpperCAmelCase , identifier=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = Path("""src/transformers""" )
UpperCAmelCase__ = ["""configuration""", """modeling""", """tokenization"""]
self.analyze_directory(_UpperCAmelCase , n_identifier=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = Path("""docs/source""" )
UpperCAmelCase__ = ["""favicon.ico"""]
self.analyze_directory(_UpperCAmelCase , ignore_files=_UpperCAmelCase , only_modules=_UpperCAmelCase )
| 346 |
'''simple docstring'''
from math import factorial
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ):
'''simple docstring'''
UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase__ = n // 2
return int(factorial(SCREAMING_SNAKE_CASE__ ) / (factorial(SCREAMING_SNAKE_CASE__ ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(2_0))
else:
try:
UpperCAmelCase_ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number.')
| 346 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 157 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_SCREAMING_SNAKE_CASE : str = False
class __a ( unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class __a ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase ( self : Optional[int] ):
UpperCamelCase__ : Any =VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCamelCase__ : int =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
UpperCamelCase__ : Dict =torch.manual_seed(0 )
UpperCamelCase__ : Optional[int] =pipe.dual_guided(
prompt='''first prompt''' , image=lowercase_ , text_to_image_strength=0.7_5 , generator=lowercase_ , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowercase_ )
UpperCamelCase__ : str =VersatileDiffusionPipeline.from_pretrained(lowercase_ , torch_dtype=torch.floataa )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCamelCase__ : int =generator.manual_seed(0 )
UpperCamelCase__ : str =pipe.dual_guided(
prompt='''first prompt''' , image=lowercase_ , text_to_image_strength=0.7_5 , generator=lowercase_ , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def _lowerCAmelCase ( self : Optional[Any] ):
UpperCamelCase__ : Dict =VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
UpperCamelCase__ : str ='''cyberpunk 2077'''
UpperCamelCase__ : str =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
UpperCamelCase__ : int =torch.manual_seed(0 )
UpperCamelCase__ : int =pipe.dual_guided(
prompt=lowercase_ , image=lowercase_ , text_to_image_strength=0.7_5 , generator=lowercase_ , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' , ).images
UpperCamelCase__ : List[str] =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
UpperCamelCase__ : Dict =np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
UpperCamelCase__ : Dict ='''A painting of a squirrel eating a burger '''
UpperCamelCase__ : Optional[int] =torch.manual_seed(0 )
UpperCamelCase__ : str =pipe.text_to_image(
prompt=lowercase_ , generator=lowercase_ , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''' ).images
UpperCamelCase__ : str =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
UpperCamelCase__ : List[Any] =np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
UpperCamelCase__ : Optional[Any] =pipe.image_variation(lowercase_ , generator=lowercase_ , output_type='''numpy''' ).images
UpperCamelCase__ : str =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
UpperCamelCase__ : Tuple =np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 157 | 1 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowerCAmelCase = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def __lowerCamelCase ( lowerCAmelCase_ ) -> Tuple:
_a : int = test_results.split(' ' )
_a : Optional[Any] = 0
_a : Optional[Any] = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
_a : Union[str, Any] = expressions[-2] if '=' in expressions[-1] else expressions[-1]
for i, expression in enumerate(lowerCAmelCase_ ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def __lowerCamelCase ( lowerCAmelCase_ ) -> str:
_a : List[str] = {}
_a : str = None
_a : List[str] = False
for line in failures_short_lines.split('\n' ):
if re.search(r'_ \[doctest\]' , lowerCAmelCase_ ):
_a : int = True
_a : Any = line.split(' ' )[2]
elif in_error and not line.split(' ' )[0].isdigit():
_a : Any = line
_a : Union[str, Any] = False
return failures
class __magic_name__ :
def __init__( self : Optional[Any] ,_UpperCAmelCase : str ,_UpperCAmelCase : Dict ):
_a : str = title
_a : Tuple = doc_test_results['time_spent'].split(',' )[0]
_a : List[Any] = doc_test_results['success']
_a : Dict = doc_test_results['failures']
_a : Optional[Any] = self.n_success + self.n_failures
# Failures and success of the modeling tests
_a : Union[str, Any] = doc_test_results
@property
def __lowercase ( self : Tuple ):
_a : Union[str, Any] = [self._time_spent]
_a : List[str] = 0
for time in time_spent:
_a : Optional[Any] = time.split(':' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(_UpperCAmelCase ) == 1:
_a : Union[str, Any] = [0, 0, time_parts[0]]
_a , _a , _a : Any = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3600 + minutes * 60 + seconds
_a , _a , _a : Any = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
return F"""{int(_UpperCAmelCase )}h{int(_UpperCAmelCase )}m{int(_UpperCAmelCase )}s"""
@property
def __lowercase ( self : int ):
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def __lowercase ( self : Optional[int] ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": F"""🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.""",
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
},
}
@property
def __lowercase ( self : Tuple ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
F"""There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"""
F""" {self.time}."""
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
},
}
@property
def __lowercase ( self : Optional[int] ):
_a : List[str] = 40
_a : Optional[int] = {k: v['failed'] for k, v in doc_test_results.items() if isinstance(_UpperCAmelCase ,_UpperCAmelCase )}
_a : Optional[int] = ''
for category, failures in category_failures.items():
if len(_UpperCAmelCase ) == 0:
continue
if report != "":
report += "\n\n"
report += F"""*{category} failures*:""".ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(_UpperCAmelCase )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": F"""The following examples had failures:\n\n\n{report}\n""",
},
}
@property
def __lowercase ( self : List[Any] ):
_a : List[Any] = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(_UpperCAmelCase )
@staticmethod
def __lowercase ( ):
_a : Union[str, Any] = [
{
'type': 'section',
'text': {
'type': 'plain_text',
'text': 'There was an issue running the tests.',
},
'accessory': {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
'url': F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
},
}
]
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(_UpperCAmelCase )} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] ,text='There was an issue running the tests.' ,blocks=_UpperCAmelCase ,)
def __lowercase ( self : str ):
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(self.payload )} ) )
_a : List[Any] = F"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else 'All tests passed.'
_a : Dict = client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] ,blocks=self.payload ,text=_UpperCAmelCase ,)
def __lowercase ( self : List[str] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Tuple ):
_a : Union[str, Any] = ''
for key, value in failures.items():
_a : Optional[Any] = value[:200] + ' [Truncated]' if len(_UpperCAmelCase ) > 250 else value
failures_text += F"""*{key}*\n_{value}_\n\n"""
_a : Tuple = job_name
_a : Union[str, Any] = {'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}
if job_link is not None:
_a : Optional[int] = {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
'url': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def __lowercase ( self : List[Any] ):
if self.thread_ts is None:
raise ValueError('Can only post reply if a post has been made.' )
_a : str = self.doc_test_results.pop('job_link' )
self.doc_test_results.pop('failures' )
self.doc_test_results.pop('success' )
self.doc_test_results.pop('time_spent' )
_a : Tuple = sorted(self.doc_test_results.items() ,key=lambda _UpperCAmelCase : t[0] )
for job, job_result in sorted_dict:
if len(job_result['failures'] ):
_a : Optional[int] = F"""*Num failures* :{len(job_result['failed'] )} \n"""
_a : List[str] = job_result['failures']
_a : str = self.get_reply_blocks(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,text=_UpperCAmelCase )
print('Sending the following reply' )
print(json.dumps({'blocks': blocks} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] ,text=F"""Results for {job}""" ,blocks=_UpperCAmelCase ,thread_ts=self.thread_ts['ts'] ,)
time.sleep(1 )
def __lowerCamelCase ( ) -> Optional[Any]:
_a : Optional[int] = os.environ['GITHUB_RUN_ID']
_a : Any = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
_a : Union[str, Any] = requests.get(lowerCAmelCase_ ).json()
_a : List[Any] = {}
try:
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
_a : int = math.ceil((result['total_count'] - 100) / 100 )
for i in range(lowerCAmelCase_ ):
_a : List[Any] = requests.get(url + f"""&page={i + 2}""" ).json()
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
return jobs
except Exception as e:
print('Unknown error, could not fetch links.' , lowerCAmelCase_ )
return {}
def __lowerCamelCase ( lowerCAmelCase_ ) -> Optional[int]:
_a : Optional[Any] = {}
if os.path.exists(lowerCAmelCase_ ):
_a : Dict = os.listdir(lowerCAmelCase_ )
for file in files:
try:
with open(os.path.join(lowerCAmelCase_ , lowerCAmelCase_ ) , encoding='utf-8' ) as f:
_a : List[str] = f.read()
except UnicodeDecodeError as e:
raise ValueError(f"""Could not open {os.path.join(lowerCAmelCase_ , lowerCAmelCase_ )}.""" ) from e
return _artifact
def __lowerCamelCase ( ) -> Optional[int]:
class __magic_name__ :
def __init__( self : Tuple ,_UpperCAmelCase : str ):
_a : str = name
_a : Union[str, Any] = []
def __str__( self : Optional[Any] ):
return self.name
def __lowercase ( self : Optional[int] ,_UpperCAmelCase : str ):
self.paths.append({'name': self.name, 'path': path} )
_a : Dict[str, Artifact] = {}
_a : Any = filter(os.path.isdir , os.listdir() )
for directory in directories:
_a : Optional[int] = directory
if artifact_name not in _available_artifacts:
_a : Optional[Any] = Artifact(lowerCAmelCase_ )
_available_artifacts[artifact_name].add_path(lowerCAmelCase_ )
return _available_artifacts
if __name__ == "__main__":
__lowerCAmelCase = get_job_links()
__lowerCAmelCase = retrieve_available_artifacts()
__lowerCAmelCase = collections.OrderedDict(
[
('''*.py''', '''API Examples'''),
('''*.md''', '''MD Examples'''),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
__lowerCAmelCase = {
v: {
'''failed''': [],
'''failures''': {},
}
for v in docs.values()
}
# Link to the GitHub Action job
__lowerCAmelCase = github_actions_job_links.get('''run_doctests''')
__lowerCAmelCase = available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
__lowerCAmelCase = retrieve_artifact(artifact_path['''name'''])
if "stats" in artifact:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = handle_test_results(artifact['''stats'''])
__lowerCAmelCase = failed
__lowerCAmelCase = success
__lowerCAmelCase = time_spent[1:-1] + ''', '''
__lowerCAmelCase = extract_first_line_failure(artifact['''failures_short'''])
for line in artifact["summary_short"].split('''\n'''):
if re.search('''FAILED''', line):
__lowerCAmelCase = line.replace('''FAILED ''', '''''')
__lowerCAmelCase = line.split()[0].replace('''\n''', '''''')
if "::" in line:
__lowerCAmelCase , __lowerCAmelCase = line.split('''::''')
else:
__lowerCAmelCase , __lowerCAmelCase = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
__lowerCAmelCase = docs[file_regex]
doc_test_results[category]["failed"].append(test)
__lowerCAmelCase = all_failures[test] if test in all_failures else '''N/A'''
__lowerCAmelCase = failure
break
__lowerCAmelCase = Message('''🤗 Results of the doc tests.''', doc_test_results)
message.post()
message.post_reply()
| 89 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=1 ) -> Dict:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_prefix_segments:] )
else:
return ".".join(path.split('.' )[:n_shave_prefix_segments] )
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=0 ) -> Tuple:
_a : Any = []
for old_item in old_list:
_a : Union[str, Any] = old_item.replace('in_layers.0' , 'norm1' )
_a : Optional[int] = new_item.replace('in_layers.2' , 'conv1' )
_a : str = new_item.replace('out_layers.0' , 'norm2' )
_a : List[str] = new_item.replace('out_layers.3' , 'conv2' )
_a : str = new_item.replace('emb_layers.1' , 'time_emb_proj' )
_a : Tuple = new_item.replace('skip_connection' , 'conv_shortcut' )
_a : Any = shave_segments(lowerCAmelCase_ , n_shave_prefix_segments=lowerCAmelCase_ )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=0 ) -> Any:
_a : List[str] = []
for old_item in old_list:
_a : List[Any] = old_item
_a : Optional[int] = new_item.replace('norm.weight' , 'group_norm.weight' )
_a : Optional[Any] = new_item.replace('norm.bias' , 'group_norm.bias' )
_a : Any = new_item.replace('proj_out.weight' , 'proj_attn.weight' )
_a : Optional[Any] = new_item.replace('proj_out.bias' , 'proj_attn.bias' )
_a : Optional[int] = shave_segments(lowerCAmelCase_ , n_shave_prefix_segments=lowerCAmelCase_ )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None ) -> Any:
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
_a : Optional[Any] = old_checkpoint[path]
_a : Optional[Any] = old_tensor.shape[0] // 3
_a : Any = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
_a : int = old_tensor.shape[0] // config['num_head_channels'] // 3
_a : str = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
_a , _a , _a : Tuple = old_tensor.split(channels // num_heads , dim=1 )
_a : Dict = query.reshape(lowerCAmelCase_ )
_a : str = key.reshape(lowerCAmelCase_ )
_a : Optional[int] = value.reshape(lowerCAmelCase_ )
for path in paths:
_a : Dict = path['new']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
_a : Any = new_path.replace('middle_block.0' , 'mid_block.resnets.0' )
_a : str = new_path.replace('middle_block.1' , 'mid_block.attentions.0' )
_a : Union[str, Any] = new_path.replace('middle_block.2' , 'mid_block.resnets.1' )
if additional_replacements is not None:
for replacement in additional_replacements:
_a : int = new_path.replace(replacement['old'] , replacement['new'] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
_a : List[str] = old_checkpoint[path['old']][:, :, 0]
else:
_a : Dict = old_checkpoint[path['old']]
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> List[Any]:
_a : Optional[int] = {}
_a : Dict = checkpoint['time_embed.0.weight']
_a : Tuple = checkpoint['time_embed.0.bias']
_a : Union[str, Any] = checkpoint['time_embed.2.weight']
_a : List[str] = checkpoint['time_embed.2.bias']
_a : List[str] = checkpoint['input_blocks.0.0.weight']
_a : Union[str, Any] = checkpoint['input_blocks.0.0.bias']
_a : Optional[int] = checkpoint['out.0.weight']
_a : int = checkpoint['out.0.bias']
_a : List[str] = checkpoint['out.2.weight']
_a : Optional[int] = checkpoint['out.2.bias']
# Retrieves the keys for the input blocks only
_a : Optional[int] = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'input_blocks' in layer} )
_a : Dict = {
layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
for layer_id in range(lowerCAmelCase_ )
}
# Retrieves the keys for the middle blocks only
_a : List[Any] = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'middle_block' in layer} )
_a : Union[str, Any] = {
layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
for layer_id in range(lowerCAmelCase_ )
}
# Retrieves the keys for the output blocks only
_a : Optional[int] = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'output_blocks' in layer} )
_a : str = {
layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
for layer_id in range(lowerCAmelCase_ )
}
for i in range(1 , lowerCAmelCase_ ):
_a : List[Any] = (i - 1) // (config['num_res_blocks'] + 1)
_a : Optional[int] = (i - 1) % (config['num_res_blocks'] + 1)
_a : Optional[int] = [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
_a : Optional[Any] = [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
_a : List[Any] = checkpoint[
f"""input_blocks.{i}.0.op.weight"""
]
_a : Union[str, Any] = checkpoint[
f"""input_blocks.{i}.0.op.bias"""
]
continue
_a : Any = renew_resnet_paths(lowerCAmelCase_ )
_a : List[str] = {'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
_a : Optional[Any] = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
assign_to_checkpoint(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path, resnet_op] , config=lowerCAmelCase_ )
if len(lowerCAmelCase_ ):
_a : List[str] = renew_attention_paths(lowerCAmelCase_ )
_a : List[Any] = {
'old': f"""input_blocks.{i}.1""",
'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
}
_a : Optional[Any] = {
f"""input_blocks.{i}.1.qkv.bias""": {
'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
},
f"""input_blocks.{i}.1.qkv.weight""": {
'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
},
}
assign_to_checkpoint(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , attention_paths_to_split=lowerCAmelCase_ , config=lowerCAmelCase_ , )
_a : str = middle_blocks[0]
_a : Tuple = middle_blocks[1]
_a : Any = middle_blocks[2]
_a : List[Any] = renew_resnet_paths(lowerCAmelCase_ )
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , config=lowerCAmelCase_ )
_a : Any = renew_resnet_paths(lowerCAmelCase_ )
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , config=lowerCAmelCase_ )
_a : int = renew_attention_paths(lowerCAmelCase_ )
_a : int = {
'middle_block.1.qkv.bias': {
'key': 'mid_block.attentions.0.key.bias',
'query': 'mid_block.attentions.0.query.bias',
'value': 'mid_block.attentions.0.value.bias',
},
'middle_block.1.qkv.weight': {
'key': 'mid_block.attentions.0.key.weight',
'query': 'mid_block.attentions.0.query.weight',
'value': 'mid_block.attentions.0.value.weight',
},
}
assign_to_checkpoint(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , attention_paths_to_split=lowerCAmelCase_ , config=lowerCAmelCase_ )
for i in range(lowerCAmelCase_ ):
_a : List[str] = i // (config['num_res_blocks'] + 1)
_a : Any = i % (config['num_res_blocks'] + 1)
_a : Union[str, Any] = [shave_segments(lowerCAmelCase_ , 2 ) for name in output_blocks[i]]
_a : Optional[Any] = {}
for layer in output_block_layers:
_a , _a : str = layer.split('.' )[0], shave_segments(lowerCAmelCase_ , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(lowerCAmelCase_ )
else:
_a : str = [layer_name]
if len(lowerCAmelCase_ ) > 1:
_a : str = [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
_a : Optional[Any] = [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
_a : Dict = renew_resnet_paths(lowerCAmelCase_ )
_a : str = renew_resnet_paths(lowerCAmelCase_ )
_a : Optional[int] = {'old': f"""output_blocks.{i}.0""", 'new': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
assign_to_checkpoint(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , config=lowerCAmelCase_ )
if ["conv.weight", "conv.bias"] in output_block_list.values():
_a : List[Any] = list(output_block_list.values() ).index(['conv.weight', 'conv.bias'] )
_a : Tuple = checkpoint[
f"""output_blocks.{i}.{index}.conv.weight"""
]
_a : List[str] = checkpoint[
f"""output_blocks.{i}.{index}.conv.bias"""
]
# Clear attentions as they have been attributed above.
if len(lowerCAmelCase_ ) == 2:
_a : Union[str, Any] = []
if len(lowerCAmelCase_ ):
_a : Tuple = renew_attention_paths(lowerCAmelCase_ )
_a : str = {
'old': f"""output_blocks.{i}.1""",
'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
}
_a : List[Any] = {
f"""output_blocks.{i}.1.qkv.bias""": {
'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
},
f"""output_blocks.{i}.1.qkv.weight""": {
'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
},
}
assign_to_checkpoint(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any('qkv' in key for key in attentions ) else None , config=lowerCAmelCase_ , )
else:
_a : List[Any] = renew_resnet_paths(lowerCAmelCase_ , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
_a : int = '.'.join(['output_blocks', str(lowerCAmelCase_ ), path['old']] )
_a : Union[str, Any] = '.'.join(['up_blocks', str(lowerCAmelCase_ ), 'resnets', str(lowerCAmelCase_ ), path['new']] )
_a : Union[str, Any] = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the architecture.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
__lowerCAmelCase = json.loads(f.read())
__lowerCAmelCase = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
__lowerCAmelCase = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
__lowerCAmelCase = DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
__lowerCAmelCase = VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
__lowerCAmelCase = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 89 | 1 |
"""simple docstring"""
from __future__ import annotations
a = 10
def _snake_case ( _snake_case : list[int] ) -> list[int]:
'''simple docstring'''
_A = 1
_A = max(_snake_case )
while placement <= max_digit:
# declare and initialize empty buckets
_A = [[] for _ in range(_snake_case )]
# split list_of_ints between the buckets
for i in list_of_ints:
_A = int((i / placement) % RADIX )
buckets[tmp].append(_snake_case )
# put each buckets' contents into list_of_ints
_A = 0
for b in range(_snake_case ):
for i in buckets[b]:
_A = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 271 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_A = defaultdict(_snake_case )
_A = []
_A = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'local': doc['local'], 'title': doc['title']} )
else:
new_doc_list.append(_snake_case )
_A = new_doc_list
_A = [key for key, value in counts.items() if value > 1]
_A = []
for duplicate_key in duplicates:
_A = list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key} )
if len(_snake_case ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1] )
_A = sorted(_snake_case , key=lambda _snake_case : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(_snake_case ) > 1:
raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.' )
overview_doc.extend(_snake_case )
# Sort
return overview_doc
def _snake_case ( _snake_case : Tuple=False ) -> List[Any]:
'''simple docstring'''
with open(_snake_case , encoding='utf-8' ) as f:
_A = yaml.safe_load(f.read() )
# Get to the API doc
_A = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_A = content[api_idx]['sections']
# Then to the model doc
_A = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_A = api_doc[scheduler_idx]['sections']
_A = clean_doc_toc(_snake_case )
_A = False
if new_scheduler_doc != scheduler_doc:
_A = True
if overwrite:
_A = new_scheduler_doc
if diff:
if overwrite:
_A = api_doc
with open(_snake_case , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(_snake_case , allow_unicode=_snake_case ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
def _snake_case ( _snake_case : str=False ) -> Union[str, Any]:
'''simple docstring'''
with open(_snake_case , encoding='utf-8' ) as f:
_A = yaml.safe_load(f.read() )
# Get to the API doc
_A = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_A = content[api_idx]['sections']
# Then to the model doc
_A = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_A = False
_A = api_doc[pipeline_idx]['sections']
_A = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_A = pipeline_doc['section']
_A = clean_doc_toc(_snake_case )
if overwrite:
_A = new_sub_pipeline_doc
new_pipeline_docs.append(_snake_case )
# sort overall pipeline doc
_A = clean_doc_toc(_snake_case )
if new_pipeline_docs != pipeline_docs:
_A = True
if overwrite:
_A = new_pipeline_docs
if diff:
if overwrite:
_A = api_doc
with open(_snake_case , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(_snake_case , allow_unicode=_snake_case ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
a = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 271 | 1 |
from numpy import exp, pi, sqrt
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase = 0.0 , _UpperCamelCase = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase_ = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt''',
'''tiny''': '''https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt''',
'''base.en''': '''https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt''',
'''base''': '''https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt''',
'''small.en''': '''https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt''',
'''small''': '''https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt''',
'''medium.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt''',
'''medium''': '''https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt''',
'''large''': '''https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt''',
'''large-v2''': '''https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt''',
}
def lowerCamelCase_ ( _UpperCamelCase ) -> List[Any]:
"""simple docstring"""
snake_case_ : List[str] = ['''layers''', '''blocks''']
for k in ignore_keys:
state_dict.pop(_UpperCamelCase , _UpperCamelCase )
lowerCAmelCase_ = {
'''blocks''': '''layers''',
'''mlp.0''': '''fc1''',
'''mlp.2''': '''fc2''',
'''mlp_ln''': '''final_layer_norm''',
'''.attn.query''': '''.self_attn.q_proj''',
'''.attn.key''': '''.self_attn.k_proj''',
'''.attn.value''': '''.self_attn.v_proj''',
'''.attn_ln''': '''.self_attn_layer_norm''',
'''.attn.out''': '''.self_attn.out_proj''',
'''.cross_attn.query''': '''.encoder_attn.q_proj''',
'''.cross_attn.key''': '''.encoder_attn.k_proj''',
'''.cross_attn.value''': '''.encoder_attn.v_proj''',
'''.cross_attn_ln''': '''.encoder_attn_layer_norm''',
'''.cross_attn.out''': '''.encoder_attn.out_proj''',
'''decoder.ln.''': '''decoder.layer_norm.''',
'''encoder.ln.''': '''encoder.layer_norm.''',
'''token_embedding''': '''embed_tokens''',
'''encoder.positional_embedding''': '''encoder.embed_positions.weight''',
'''decoder.positional_embedding''': '''decoder.embed_positions.weight''',
'''ln_post''': '''layer_norm''',
}
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ : str = list(s_dict.keys() )
for key in keys:
snake_case_ : Optional[int] = key
for k, v in WHISPER_MAPPING.items():
if k in key:
snake_case_ : List[str] = new_key.replace(_UpperCamelCase , _UpperCamelCase )
print(f'''{key} -> {new_key}''' )
snake_case_ : Tuple = s_dict.pop(_UpperCamelCase )
return s_dict
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
snake_case_ , snake_case_ : Dict = emb.weight.shape
snake_case_ : Tuple = nn.Linear(_UpperCamelCase , _UpperCamelCase , bias=_UpperCamelCase )
snake_case_ : Any = emb.weight.data
return lin_layer
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> bytes:
"""simple docstring"""
os.makedirs(_UpperCamelCase , exist_ok=_UpperCamelCase )
snake_case_ : List[Any] = os.path.basename(_UpperCamelCase )
snake_case_ : Any = url.split('''/''' )[-2]
snake_case_ : str = os.path.join(_UpperCamelCase , _UpperCamelCase )
if os.path.exists(_UpperCamelCase ) and not os.path.isfile(_UpperCamelCase ):
raise RuntimeError(f'''{download_target} exists and is not a regular file''' )
if os.path.isfile(_UpperCamelCase ):
snake_case_ : Union[str, Any] = open(_UpperCamelCase , '''rb''' ).read()
if hashlib.shaaaa(_UpperCamelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(f'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file''' )
with urllib.request.urlopen(_UpperCamelCase ) as source, open(_UpperCamelCase , '''wb''' ) as output:
with tqdm(
total=int(source.info().get('''Content-Length''' ) ) , ncols=80 , unit='''iB''' , unit_scale=_UpperCamelCase , unit_divisor=1_024 ) as loop:
while True:
snake_case_ : Dict = source.read(8_192 )
if not buffer:
break
output.write(_UpperCamelCase )
loop.update(len(_UpperCamelCase ) )
snake_case_ : Any = open(_UpperCamelCase , '''rb''' ).read()
if hashlib.shaaaa(_UpperCamelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.''' )
return model_bytes
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if ".pt" not in checkpoint_path:
snake_case_ : str = _download(_MODELS[checkpoint_path] )
else:
snake_case_ : Union[str, Any] = torch.load(_UpperCamelCase , map_location='''cpu''' )
snake_case_ : int = original_checkpoint['''dims''']
snake_case_ : List[str] = original_checkpoint['''model_state_dict''']
snake_case_ : str = state_dict['''decoder.token_embedding.weight''']
remove_ignore_keys_(_UpperCamelCase )
rename_keys(_UpperCamelCase )
snake_case_ : Optional[int] = True
snake_case_ : int = state_dict['''decoder.layers.0.fc1.weight'''].shape[0]
snake_case_ : List[str] = WhisperConfig(
vocab_size=dimensions['''n_vocab'''] , encoder_ffn_dim=_UpperCamelCase , decoder_ffn_dim=_UpperCamelCase , num_mel_bins=dimensions['''n_mels'''] , d_model=dimensions['''n_audio_state'''] , max_target_positions=dimensions['''n_text_ctx'''] , encoder_layers=dimensions['''n_audio_layer'''] , encoder_attention_heads=dimensions['''n_audio_head'''] , decoder_layers=dimensions['''n_text_layer'''] , decoder_attention_heads=dimensions['''n_text_state'''] , max_source_positions=dimensions['''n_audio_ctx'''] , )
snake_case_ : Union[str, Any] = WhisperForConditionalGeneration(_UpperCamelCase )
snake_case_ , snake_case_ : List[Any] = model.model.load_state_dict(_UpperCamelCase , strict=_UpperCamelCase )
if len(_UpperCamelCase ) > 0 and not set(_UpperCamelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f''' but all the following weights are missing {missing}''' )
if tie_embeds:
snake_case_ : List[str] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case_ : Any = proj_out_weights
model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Patht to the downloaded checkpoints''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
lowerCAmelCase_ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 279 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was passed to the function""" )
SCREAMING_SNAKE_CASE = ""
while len(_lowerCAmelCase ) % 3 != 0:
SCREAMING_SNAKE_CASE = "0" + bin_string
SCREAMING_SNAKE_CASE = [
bin_string[index : index + 3]
for index in range(len(_lowerCAmelCase ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
SCREAMING_SNAKE_CASE = 0
for index, val in enumerate(_lowerCAmelCase ):
oct_val += int(2 ** (2 - index) * int(_lowerCAmelCase ) )
oct_string += str(_lowerCAmelCase )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 354 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""MvpTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MvpForCausalLM""",
"""MvpForConditionalGeneration""",
"""MvpForQuestionAnswering""",
"""MvpForSequenceClassification""",
"""MvpModel""",
"""MvpPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 193 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class lowercase__ ( lowercase ):
lowercase__ = 42
@flax_register_to_config
class lowercase__ ( nn.Module , lowercase , lowercase ):
lowercase__ = 32
lowercase__ = 4
lowercase__ = 4
lowercase__ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
lowercase__ = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
lowercase__ = False
lowercase__ = (3_20, 6_40, 12_80, 12_80)
lowercase__ = 2
lowercase__ = 8
lowercase__ = None
lowercase__ = 12_80
lowercase__ = 0.0
lowercase__ = False
lowercase__ = jnp.floataa
lowercase__ = True
lowercase__ = 0
lowercase__ = False
def UpperCamelCase_ ( self : List[str] ,lowerCamelCase__ : jax.random.KeyArray ):
'''simple docstring'''
# init input tensors
_UpperCamelCase : Any = (1, self.in_channels, self.sample_size, self.sample_size)
_UpperCamelCase : Dict = jnp.zeros(lowerCamelCase__ ,dtype=jnp.floataa )
_UpperCamelCase : str = jnp.ones((1,) ,dtype=jnp.intaa )
_UpperCamelCase : str = jnp.zeros((1, 1, self.cross_attention_dim) ,dtype=jnp.floataa )
_UpperCamelCase , _UpperCamelCase : Dict = jax.random.split(lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = {'params': params_rng, 'dropout': dropout_rng}
return self.init(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )["params"]
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.block_out_channels
_UpperCamelCase : Union[str, Any] = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_UpperCamelCase : Tuple = self.num_attention_heads or self.attention_head_dim
# input
_UpperCamelCase : List[Any] = nn.Conv(
block_out_channels[0] ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
# time
_UpperCamelCase : Optional[int] = FlaxTimesteps(
block_out_channels[0] ,flip_sin_to_cos=self.flip_sin_to_cos ,freq_shift=self.config.freq_shift )
_UpperCamelCase : int = FlaxTimestepEmbedding(lowerCamelCase__ ,dtype=self.dtype )
_UpperCamelCase : Tuple = self.only_cross_attention
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : Any = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : Optional[int] = (num_attention_heads,) * len(self.down_block_types )
# down
_UpperCamelCase : str = []
_UpperCamelCase : Tuple = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
_UpperCamelCase : Dict = output_channel
_UpperCamelCase : Dict = block_out_channels[i]
_UpperCamelCase : Optional[Any] = i == len(lowerCamelCase__ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_UpperCamelCase : Tuple = FlaxCrossAttnDownBlockaD(
in_channels=lowerCamelCase__ ,out_channels=lowerCamelCase__ ,dropout=self.dropout ,num_layers=self.layers_per_block ,num_attention_heads=num_attention_heads[i] ,add_downsample=not is_final_block ,use_linear_projection=self.use_linear_projection ,only_cross_attention=only_cross_attention[i] ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
else:
_UpperCamelCase : Union[str, Any] = FlaxDownBlockaD(
in_channels=lowerCamelCase__ ,out_channels=lowerCamelCase__ ,dropout=self.dropout ,num_layers=self.layers_per_block ,add_downsample=not is_final_block ,dtype=self.dtype ,)
down_blocks.append(lowerCamelCase__ )
_UpperCamelCase : Any = down_blocks
# mid
_UpperCamelCase : str = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] ,dropout=self.dropout ,num_attention_heads=num_attention_heads[-1] ,use_linear_projection=self.use_linear_projection ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
# up
_UpperCamelCase : Tuple = []
_UpperCamelCase : Union[str, Any] = list(reversed(lowerCamelCase__ ) )
_UpperCamelCase : Optional[Any] = list(reversed(lowerCamelCase__ ) )
_UpperCamelCase : Dict = list(reversed(lowerCamelCase__ ) )
_UpperCamelCase : Union[str, Any] = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
_UpperCamelCase : Dict = output_channel
_UpperCamelCase : Any = reversed_block_out_channels[i]
_UpperCamelCase : int = reversed_block_out_channels[min(i + 1 ,len(lowerCamelCase__ ) - 1 )]
_UpperCamelCase : Optional[int] = i == len(lowerCamelCase__ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
_UpperCamelCase : Tuple = FlaxCrossAttnUpBlockaD(
in_channels=lowerCamelCase__ ,out_channels=lowerCamelCase__ ,prev_output_channel=lowerCamelCase__ ,num_layers=self.layers_per_block + 1 ,num_attention_heads=reversed_num_attention_heads[i] ,add_upsample=not is_final_block ,dropout=self.dropout ,use_linear_projection=self.use_linear_projection ,only_cross_attention=only_cross_attention[i] ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
else:
_UpperCamelCase : List[str] = FlaxUpBlockaD(
in_channels=lowerCamelCase__ ,out_channels=lowerCamelCase__ ,prev_output_channel=lowerCamelCase__ ,num_layers=self.layers_per_block + 1 ,add_upsample=not is_final_block ,dropout=self.dropout ,dtype=self.dtype ,)
up_blocks.append(lowerCamelCase__ )
_UpperCamelCase : str = output_channel
_UpperCamelCase : Dict = up_blocks
# out
_UpperCamelCase : Tuple = nn.GroupNorm(num_groups=32 ,epsilon=1E-5 )
_UpperCamelCase : str = nn.Conv(
self.out_channels ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
def __call__( self : Dict ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Optional[int]=None ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = False ,):
'''simple docstring'''
# 1. time
if not isinstance(lowerCamelCase__ ,jnp.ndarray ):
_UpperCamelCase : Any = jnp.array([timesteps] ,dtype=jnp.intaa )
elif isinstance(lowerCamelCase__ ,jnp.ndarray ) and len(timesteps.shape ) == 0:
_UpperCamelCase : Dict = timesteps.astype(dtype=jnp.floataa )
_UpperCamelCase : Optional[int] = jnp.expand_dims(lowerCamelCase__ ,0 )
_UpperCamelCase : List[Any] = self.time_proj(lowerCamelCase__ )
_UpperCamelCase : Tuple = self.time_embedding(lowerCamelCase__ )
# 2. pre-process
_UpperCamelCase : List[str] = jnp.transpose(lowerCamelCase__ ,(0, 2, 3, 1) )
_UpperCamelCase : str = self.conv_in(lowerCamelCase__ )
# 3. down
_UpperCamelCase : Tuple = (sample,)
for down_block in self.down_blocks:
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase , _UpperCamelCase : List[str] = down_block(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,deterministic=not train )
else:
_UpperCamelCase , _UpperCamelCase : List[Any] = down_block(lowerCamelCase__ ,lowerCamelCase__ ,deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
_UpperCamelCase : Optional[int] = ()
for down_block_res_sample, down_block_additional_residual in zip(
lowerCamelCase__ ,lowerCamelCase__ ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
_UpperCamelCase : int = new_down_block_res_samples
# 4. mid
_UpperCamelCase : int = self.mid_block(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
_UpperCamelCase : List[str] = down_block_res_samples[-(self.layers_per_block + 1) :]
_UpperCamelCase : int = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : Tuple = up_block(
lowerCamelCase__ ,temb=lowerCamelCase__ ,encoder_hidden_states=lowerCamelCase__ ,res_hidden_states_tuple=lowerCamelCase__ ,deterministic=not train ,)
else:
_UpperCamelCase : str = up_block(lowerCamelCase__ ,temb=lowerCamelCase__ ,res_hidden_states_tuple=lowerCamelCase__ ,deterministic=not train )
# 6. post-process
_UpperCamelCase : Optional[int] = self.conv_norm_out(lowerCamelCase__ )
_UpperCamelCase : Tuple = nn.silu(lowerCamelCase__ )
_UpperCamelCase : int = self.conv_out(lowerCamelCase__ )
_UpperCamelCase : Any = jnp.transpose(lowerCamelCase__ ,(0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=lowerCamelCase__ )
| 83 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = 42
__A = None
def a( A : Optional[Any] , A : Any=0.999 , A : Dict="cosine" , ) -> Optional[int]:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(A : Optional[int] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(A : Any ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
a = []
for i in range(A ):
a = i / num_diffusion_timesteps
a = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(A ) / alpha_bar_fn(A ) , A ) )
return torch.tensor(A , dtype=torch.floataa )
class _lowercase ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
__A = 1
@register_to_config
def __init__(self , lowerCamelCase_ = 1000 , lowerCamelCase_ = 0.0001 , lowerCamelCase_ = 0.02 , lowerCamelCase_ = "linear" , lowerCamelCase_ = None , lowerCamelCase_ = True , lowerCamelCase_ = True , lowerCamelCase_ = 0 , lowerCamelCase_ = "epsilon" , lowerCamelCase_ = 1.0 , **lowerCamelCase_ , ):
"""simple docstring"""
if kwargs.get("set_alpha_to_one" , lowerCamelCase_ ) is not None:
a = (
"The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
)
deprecate("set_alpha_to_one" , "1.0.0" , lowerCamelCase_ , standard_warn=lowerCamelCase_ )
a = kwargs["set_alpha_to_one"]
if trained_betas is not None:
a = torch.tensor(lowerCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
a = torch.linspace(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
a = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowerCamelCase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
a = betas_for_alpha_bar(lowerCamelCase_ )
else:
raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )
a = 1.0 - self.betas
a = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
a = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
a = 1.0
# setable values
a = None
a = torch.from_numpy(np.arange(0 , lowerCamelCase_ ).copy().astype(np.intaa ) )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = None ):
"""simple docstring"""
return sample
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = None ):
"""simple docstring"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
F''' maximal {self.config.num_train_timesteps} timesteps.''' )
a = num_inference_steps
a = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
a = (np.arange(0 , lowerCamelCase_ ) * step_ratio).round().copy().astype(np.intaa )
a = torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )
self.timesteps += self.config.steps_offset
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 0.0 , lowerCamelCase_ = False , lowerCamelCase_ = None , lowerCamelCase_ = True , ):
"""simple docstring"""
a = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
a = self.alphas_cumprod[timestep]
a = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
a = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
a = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
a = model_output
elif self.config.prediction_type == "sample":
a = model_output
a = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
a = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
a = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
" `v_prediction`" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
a = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
a = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
a = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=lowerCamelCase_ , pred_original_sample=lowerCamelCase_ )
def __len__(self ):
"""simple docstring"""
return self.config.num_train_timesteps
| 227 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
'''tokenization_deberta''': ['''DebertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = ['''DebertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DebertaForMaskedLM''',
'''DebertaForQuestionAnswering''',
'''DebertaForSequenceClassification''',
'''DebertaForTokenClassification''',
'''DebertaModel''',
'''DebertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDebertaForMaskedLM''',
'''TFDebertaForQuestionAnswering''',
'''TFDebertaForSequenceClassification''',
'''TFDebertaForTokenClassification''',
'''TFDebertaModel''',
'''TFDebertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 219 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__snake_case = 16
__snake_case = 32
def a ( __a , __a = 16 , __a = "bert-base-cased" ) -> Any:
'''simple docstring'''
UpperCamelCase__ :List[str] = AutoTokenizer.from_pretrained(__a )
UpperCamelCase__ :List[Any] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__a ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase__ :Tuple = 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
UpperCamelCase__ :Optional[int] = datasets.map(
__a , batched=__a , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=__a )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase__ :Optional[int] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__a ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__a , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(__a , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
UpperCamelCase__ :Dict = DataLoader(
tokenized_datasets['''train'''] , shuffle=__a , collate_fn=__a , batch_size=__a )
UpperCamelCase__ :str = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__a , collate_fn=__a , batch_size=__a )
return train_dataloader, eval_dataloader
def a ( __a , __a , __a , __a ) -> str:
'''simple docstring'''
model.eval()
UpperCamelCase__ :List[str] = 0
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase__ :int = model(**__a )
UpperCamelCase__ :Tuple = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCamelCase__ , UpperCamelCase__ :int = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__a ) - 1:
UpperCamelCase__ :Union[str, Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCamelCase__ :List[str] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__a , references=__a , )
UpperCamelCase__ :Union[str, Any] = metric.compute()
return eval_metric["accuracy"]
def a ( __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase__ :Any = config['''lr''']
UpperCamelCase__ :Optional[int] = int(config['''num_epochs'''] )
UpperCamelCase__ :List[Any] = int(config['''seed'''] )
UpperCamelCase__ :List[Any] = int(config['''batch_size'''] )
UpperCamelCase__ :List[Any] = args.model_name_or_path
set_seed(__a )
UpperCamelCase__ , UpperCamelCase__ :Any = get_dataloaders(__a , __a , __a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase__ :Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(__a , return_dict=__a )
# Instantiate optimizer
UpperCamelCase__ :Any = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCamelCase__ :Optional[Any] = optimizer_cls(params=model.parameters() , lr=__a )
if accelerator.state.deepspeed_plugin is not None:
UpperCamelCase__ :Tuple = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
UpperCamelCase__ :Dict = 1
UpperCamelCase__ :Tuple = (len(__a ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCamelCase__ :Any = get_linear_schedule_with_warmup(
optimizer=__a , num_warmup_steps=0 , num_training_steps=__a , )
else:
UpperCamelCase__ :Any = DummyScheduler(__a , total_num_steps=__a , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ :Dict = accelerator.prepare(
__a , __a , __a , __a , __a )
# We need to keep track of how many total steps we have iterated over
UpperCamelCase__ :Tuple = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCamelCase__ :Optional[Any] = 0
UpperCamelCase__ :Optional[int] = evaluate.load('''glue''' , '''mrpc''' )
UpperCamelCase__ :List[Any] = num_epochs
if args.partial_train_epoch is not None:
UpperCamelCase__ :Optional[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
UpperCamelCase__ :Dict = args.resume_from_checkpoint.split('''epoch_''' )[1]
UpperCamelCase__ :Tuple = ''''''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
UpperCamelCase__ :Any = int(__a ) + 1
UpperCamelCase__ :Dict = evaluation_loop(__a , __a , __a , __a )
accelerator.print('''resumed checkpoint performance:''' , __a )
accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' , lr_scheduler.get_lr()[0] )
accelerator.print('''resumed optimizers\'s lr:''' , optimizer.param_groups[0]['''lr'''] )
with open(os.path.join(args.output_dir , f'''state_{starting_epoch-1}.json''' ) , '''r''' ) as f:
UpperCamelCase__ :Optional[int] = json.load(__a )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
UpperCamelCase__ :Optional[Any] = {}
for epoch in range(__a , __a ):
model.train()
for step, batch in enumerate(__a ):
UpperCamelCase__ :Optional[int] = model(**__a )
UpperCamelCase__ :Optional[int] = outputs.loss
UpperCamelCase__ :str = loss / gradient_accumulation_steps
accelerator.backward(__a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
UpperCamelCase__ :Union[str, Any] = f'''epoch_{epoch}'''
UpperCamelCase__ :List[Any] = os.path.join(args.output_dir , __a )
accelerator.save_state(__a )
UpperCamelCase__ :List[Any] = evaluation_loop(__a , __a , __a , __a )
UpperCamelCase__ :int = accuracy
UpperCamelCase__ :List[Any] = lr_scheduler.get_lr()[0]
UpperCamelCase__ :Any = optimizer.param_groups[0]['''lr''']
UpperCamelCase__ :int = epoch
UpperCamelCase__ :Tuple = overall_step
accelerator.print(f'''epoch {epoch}:''' , __a )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f'''state_{epoch}.json''' ) , '''w''' ) as f:
json.dump(__a , __a )
def a ( ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :List[Any] = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' , type=__a , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=__a , )
parser.add_argument(
'''--output_dir''' , type=__a , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--resume_from_checkpoint''' , type=__a , default=__a , help='''If the training should continue from a checkpoint folder.''' , )
parser.add_argument(
'''--partial_train_epoch''' , type=__a , default=__a , help='''If passed, the training will stop after this number of epochs.''' , )
parser.add_argument(
'''--num_epochs''' , type=__a , default=2 , help='''Number of train epochs.''' , )
UpperCamelCase__ :Optional[int] = parser.parse_args()
UpperCamelCase__ :List[str] = {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
training_function(__a , __a )
if __name__ == "__main__":
main() | 219 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase : str = {
'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:
__lowercase : str = [
'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecAudioForAudioFrameClassification',
'Data2VecAudioForCTC',
'Data2VecAudioForSequenceClassification',
'Data2VecAudioForXVector',
'Data2VecAudioModel',
'Data2VecAudioPreTrainedModel',
]
__lowercase : Tuple = [
'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecTextForCausalLM',
'Data2VecTextForMaskedLM',
'Data2VecTextForMultipleChoice',
'Data2VecTextForQuestionAnswering',
'Data2VecTextForSequenceClassification',
'Data2VecTextForTokenClassification',
'Data2VecTextModel',
'Data2VecTextPreTrainedModel',
]
__lowercase : Dict = [
'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
'Data2VecVisionForImageClassification',
'Data2VecVisionForMaskedImageModeling',
'Data2VecVisionForSemanticSegmentation',
'Data2VecVisionModel',
'Data2VecVisionPreTrainedModel',
]
if is_tf_available():
__lowercase : Optional[Any] = [
'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
__lowercase : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 27 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
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
enable_full_determinism()
class __lowercase ( unittest.TestCase ):
def UpperCAmelCase__ (self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Tuple = 1
lowerCamelCase_ : str = 3
lowerCamelCase_ : Dict = (3_2, 3_2)
lowerCamelCase_ : Optional[Any] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(A )
return image
@property
def UpperCAmelCase__ (self ):
torch.manual_seed(0 )
lowerCamelCase_ : Optional[Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , )
return model
@property
def UpperCAmelCase__ (self ):
torch.manual_seed(0 )
lowerCamelCase_ : Union[str, Any] = AutoencoderKL(
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 , )
return model
@property
def UpperCAmelCase__ (self ):
torch.manual_seed(0 )
lowerCamelCase_ : Any = RobertaSeriesConfig(
hidden_size=3_2 , project_dim=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5_0_0_6 , )
return RobertaSeriesModelWithTransformation(A )
@property
def UpperCAmelCase__ (self ):
def extract(*A , **A ):
class __lowercase :
def __init__(self ):
lowerCamelCase_ : Any = torch.ones([0] )
def UpperCAmelCase__ (self , A ):
self.pixel_values.to(A )
return self
return Out()
return extract
def UpperCAmelCase__ (self ):
lowerCamelCase_ : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ : List[Any] = self.dummy_cond_unet
lowerCamelCase_ : Any = PNDMScheduler(skip_prk_steps=A )
lowerCamelCase_ : Union[str, Any] = self.dummy_vae
lowerCamelCase_ : List[Any] = self.dummy_text_encoder
lowerCamelCase_ : Optional[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
lowerCamelCase_ : Dict = 7_7
lowerCamelCase_ : Union[str, Any] = self.dummy_image.to(A )
lowerCamelCase_ : Union[str, Any] = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
lowerCamelCase_ : Dict = AltDiffusionImgaImgPipeline(
unet=A , scheduler=A , vae=A , text_encoder=A , tokenizer=A , safety_checker=A , feature_extractor=self.dummy_extractor , )
lowerCamelCase_ : Tuple = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=A )
lowerCamelCase_ : int = alt_pipe.to(A )
alt_pipe.set_progress_bar_config(disable=A )
lowerCamelCase_ : Optional[Any] = '''A painting of a squirrel eating a burger'''
lowerCamelCase_ : Optional[Any] = torch.Generator(device=A ).manual_seed(0 )
lowerCamelCase_ : Optional[Any] = alt_pipe(
[prompt] , generator=A , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , image=A , )
lowerCamelCase_ : int = output.images
lowerCamelCase_ : Union[str, Any] = torch.Generator(device=A ).manual_seed(0 )
lowerCamelCase_ : Union[str, Any] = alt_pipe(
[prompt] , generator=A , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , image=A , return_dict=A , )[0]
lowerCamelCase_ : List[str] = image[0, -3:, -3:, -1]
lowerCamelCase_ : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowerCamelCase_ : str = np.array([0.44_27, 0.37_31, 0.42_49, 0.49_41, 0.45_46, 0.41_48, 0.41_93, 0.46_66, 0.44_99] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Dict = self.dummy_cond_unet
lowerCamelCase_ : Optional[Any] = PNDMScheduler(skip_prk_steps=A )
lowerCamelCase_ : List[Any] = self.dummy_vae
lowerCamelCase_ : Dict = self.dummy_text_encoder
lowerCamelCase_ : Any = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
lowerCamelCase_ : Optional[Any] = 7_7
lowerCamelCase_ : str = self.dummy_image.to(A )
# put models in fp16
lowerCamelCase_ : Optional[int] = unet.half()
lowerCamelCase_ : Dict = vae.half()
lowerCamelCase_ : Union[str, Any] = bert.half()
# make sure here that pndm scheduler skips prk
lowerCamelCase_ : Dict = AltDiffusionImgaImgPipeline(
unet=A , scheduler=A , vae=A , text_encoder=A , tokenizer=A , safety_checker=A , feature_extractor=self.dummy_extractor , )
lowerCamelCase_ : List[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=A )
lowerCamelCase_ : Any = alt_pipe.to(A )
alt_pipe.set_progress_bar_config(disable=A )
lowerCamelCase_ : Tuple = '''A painting of a squirrel eating a burger'''
lowerCamelCase_ : str = torch.manual_seed(0 )
lowerCamelCase_ : Optional[int] = alt_pipe(
[prompt] , generator=A , num_inference_steps=2 , output_type='''np''' , image=A , ).images
assert image.shape == (1, 3_2, 3_2, 3)
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCamelCase_ : List[str] = init_image.resize((7_6_0, 5_0_4) )
lowerCamelCase_ : List[Any] = '''BAAI/AltDiffusion'''
lowerCamelCase_ : List[Any] = AltDiffusionImgaImgPipeline.from_pretrained(
A , safety_checker=A , )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase_ : Dict = '''A fantasy landscape, trending on artstation'''
lowerCamelCase_ : Any = torch.manual_seed(0 )
lowerCamelCase_ : Optional[Any] = pipe(
prompt=A , image=A , strength=0.75 , guidance_scale=7.5 , generator=A , output_type='''np''' , )
lowerCamelCase_ : Dict = output.images[0]
lowerCamelCase_ : str = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 7_6_0, 3)
lowerCamelCase_ : Union[str, Any] = np.array([0.93_58, 0.93_97, 0.95_99, 0.99_01, 1.00_00, 1.00_00, 0.98_82, 1.00_00, 1.00_00] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __lowercase ( unittest.TestCase ):
def UpperCAmelCase__ (self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
lowerCamelCase_ : List[str] = init_image.resize((7_6_8, 5_1_2) )
lowerCamelCase_ : str = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''' )
lowerCamelCase_ : int = '''BAAI/AltDiffusion'''
lowerCamelCase_ : List[Any] = AltDiffusionImgaImgPipeline.from_pretrained(
A , safety_checker=A , )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase_ : Tuple = '''A fantasy landscape, trending on artstation'''
lowerCamelCase_ : List[Any] = torch.manual_seed(0 )
lowerCamelCase_ : Dict = pipe(
prompt=A , image=A , strength=0.75 , guidance_scale=7.5 , generator=A , output_type='''np''' , )
lowerCamelCase_ : List[str] = output.images[0]
assert image.shape == (5_1_2, 7_6_8, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1E-2
| 318 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[tuple[int, int]]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ :str = position
lowerCAmelCase__ :Optional[Any] = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
lowerCAmelCase__ :Union[str, Any] = []
for position in positions:
lowerCAmelCase__ , lowerCAmelCase__ :Optional[int] = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(_SCREAMING_SNAKE_CASE )
return permissible_positions
def __A (_SCREAMING_SNAKE_CASE ) ->bool:
"""simple docstring"""
return not any(elem == 0 for row in board for elem in row )
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->bool:
"""simple docstring"""
if is_complete(_SCREAMING_SNAKE_CASE ):
return True
for position in get_valid_pos(_SCREAMING_SNAKE_CASE , len(_SCREAMING_SNAKE_CASE ) ):
lowerCAmelCase__ , lowerCAmelCase__ :List[str] = position
if board[y][x] == 0:
lowerCAmelCase__ :str = curr + 1
if open_knight_tour_helper(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , curr + 1 ):
return True
lowerCAmelCase__ :Optional[Any] = 0
return False
def __A (_SCREAMING_SNAKE_CASE ) ->list[list[int]]:
"""simple docstring"""
lowerCAmelCase__ :Union[str, Any] = [[0 for i in range(_SCREAMING_SNAKE_CASE )] for j in range(_SCREAMING_SNAKE_CASE )]
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ :Tuple = 1
if open_knight_tour_helper(_SCREAMING_SNAKE_CASE , (i, j) , 1 ):
return board
lowerCAmelCase__ :Dict = 0
lowerCAmelCase__ :Any = F"Open Kight Tour cannot be performed on a board of size {n}"
raise ValueError(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 254 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1e-12 ) ->str:
"""simple docstring"""
lowerCAmelCase__ :Tuple = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(_SCREAMING_SNAKE_CASE , axis=1 ) , a_min=_SCREAMING_SNAKE_CASE ) ).T
lowerCAmelCase__ :int = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(_SCREAMING_SNAKE_CASE , axis=1 ) , a_min=_SCREAMING_SNAKE_CASE ) ).T
return jnp.matmul(_SCREAMING_SNAKE_CASE , norm_emb_a.T )
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
__magic_name__ :CLIPConfig
__magic_name__ :jnp.dtype = jnp.floataa
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = FlaxCLIPVisionModule(self.config.vision_config )
lowerCAmelCase__ :str = nn.Dense(self.config.projection_dim , use_bias=__UpperCAmelCase , dtype=self.dtype )
lowerCAmelCase__ :Optional[Any] = self.param('concept_embeds' , jax.nn.initializers.ones , (1_7, self.config.projection_dim) )
lowerCAmelCase__ :Optional[int] = self.param(
'special_care_embeds' , jax.nn.initializers.ones , (3, self.config.projection_dim) )
lowerCAmelCase__ :Any = self.param('concept_embeds_weights' , jax.nn.initializers.ones , (1_7,) )
lowerCAmelCase__ :List[Any] = self.param('special_care_embeds_weights' , jax.nn.initializers.ones , (3,) )
def __call__( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = self.vision_model(__UpperCAmelCase )[1]
lowerCAmelCase__ :Optional[int] = self.visual_projection(__UpperCAmelCase )
lowerCAmelCase__ :Union[str, Any] = jax_cosine_distance(__UpperCAmelCase , self.special_care_embeds )
lowerCAmelCase__ :Tuple = jax_cosine_distance(__UpperCAmelCase , self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
lowerCAmelCase__ :Dict = 0.0
lowerCAmelCase__ :List[str] = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
lowerCAmelCase__ :Optional[Any] = jnp.round(__UpperCAmelCase , 3 )
lowerCAmelCase__ :Tuple = jnp.any(special_scores > 0 , axis=1 , keepdims=__UpperCAmelCase )
# Use a lower threshold if an image has any special care concept
lowerCAmelCase__ :List[Any] = is_special_care * 0.01
lowerCAmelCase__ :Union[str, Any] = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
lowerCAmelCase__ :Any = jnp.round(__UpperCAmelCase , 3 )
lowerCAmelCase__ :Tuple = jnp.any(concept_scores > 0 , axis=1 )
return has_nsfw_concepts
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :Tuple = CLIPConfig
__magic_name__ :Tuple = """clip_input"""
__magic_name__ :str = FlaxStableDiffusionSafetyCheckerModule
def __init__( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = 0 , __UpperCAmelCase = jnp.floataa , __UpperCAmelCase = True , **__UpperCAmelCase , ):
'''simple docstring'''
if input_shape is None:
lowerCAmelCase__ :Dict = (1, 2_2_4, 2_2_4, 3)
lowerCAmelCase__ :Any = self.module_class(config=__UpperCAmelCase , dtype=__UpperCAmelCase , **__UpperCAmelCase )
super().__init__(__UpperCAmelCase , __UpperCAmelCase , input_shape=__UpperCAmelCase , seed=__UpperCAmelCase , dtype=__UpperCAmelCase , _do_init=_do_init )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
lowerCAmelCase__ :str = jax.random.normal(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ :List[Any] = jax.random.split(__UpperCAmelCase )
lowerCAmelCase__ :Optional[int] = {'params': params_rng, 'dropout': dropout_rng}
lowerCAmelCase__ :Optional[int] = self.module.init(__UpperCAmelCase , __UpperCAmelCase )['params']
return random_params
def __call__( self , __UpperCAmelCase , __UpperCAmelCase = None , ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = jnp.transpose(__UpperCAmelCase , (0, 2, 3, 1) )
return self.module.apply(
{'params': params or self.params} , jnp.array(__UpperCAmelCase , dtype=jnp.floataa ) , rngs={} , )
| 254 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
'NllbMoeForConditionalGeneration',
'NllbMoeModel',
'NllbMoePreTrainedModel',
'NllbMoeTop2Router',
'NllbMoeSparseMLP',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 180 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class a ( __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Any = '''resnet'''
lowerCamelCase :Any = ['''basic''', '''bottleneck''']
def __init__( self , lowerCAmelCase_=3 , lowerCAmelCase_=64 , lowerCAmelCase_=[2_56, 5_12, 10_24, 20_48] , lowerCAmelCase_=[3, 4, 6, 3] , lowerCAmelCase_="bottleneck" , lowerCAmelCase_="relu" , lowerCAmelCase_=False , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ , ) -> Union[str, Any]:
super().__init__(**lowerCAmelCase_ )
if layer_type not in self.layer_types:
raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' )
_A = num_channels
_A = embedding_size
_A = hidden_sizes
_A = depths
_A = layer_type
_A = hidden_act
_A = downsample_in_first_stage
_A = ["""stem"""] + [F'''stage{idx}''' for idx in range(1 , len(lowerCAmelCase_ ) + 1 )]
_A , _A = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase_ , out_indices=lowerCAmelCase_ , stage_names=self.stage_names )
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Optional[Any] = version.parse('''1.11''' )
@property
def UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCAmelCase ( self ) -> float:
return 1E-3
| 180 | 1 |
"""simple docstring"""
from typing import Any
class __A :
'''simple docstring'''
def __init__( self : Any , UpperCAmelCase_ : Any ) ->int:
"""simple docstring"""
snake_case_ = data
snake_case_ = None
class __A :
'''simple docstring'''
def __init__( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
snake_case_ = None
def lowerCAmelCase ( self : Any ) ->int:
"""simple docstring"""
snake_case_ = self.head
while temp is not None:
print(temp.data , end=""" """ )
snake_case_ = temp.next
print()
def lowerCAmelCase ( self : Optional[int] , UpperCAmelCase_ : Any ) ->Dict:
"""simple docstring"""
snake_case_ = Node(UpperCAmelCase_ )
snake_case_ = self.head
snake_case_ = new_node
def lowerCAmelCase ( self : List[str] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] ) ->Dict:
"""simple docstring"""
if node_data_a == node_data_a:
return
else:
snake_case_ = self.head
while node_a is not None and node_a.data != node_data_a:
snake_case_ = node_a.next
snake_case_ = self.head
while node_a is not None and node_a.data != node_data_a:
snake_case_ = node_a.next
if node_a is None or node_a is None:
return
snake_case_ = node_a.data, node_a.data
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : List[str] = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print('After swapping')
ll.print_list()
| 365 |
"""simple docstring"""
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 __A (snake_case__ , snake_case__ , unittest.TestCase):
'''simple docstring'''
__lowercase: List[Any] = CycleDiffusionPipeline
__lowercase: int = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"""negative_prompt""",
"""height""",
"""width""",
"""negative_prompt_embeds""",
}
__lowercase: str = PipelineTesterMixin.required_optional_params - {"""latents"""}
__lowercase: Any = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""})
__lowercase: Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
__lowercase: Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCAmelCase ( self : Dict ) ->Any:
"""simple docstring"""
torch.manual_seed(0 )
snake_case_ = 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_ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , num_train_timesteps=1_000 , clip_sample=UpperCAmelCase_ , set_alpha_to_one=UpperCAmelCase_ , )
torch.manual_seed(0 )
snake_case_ = 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_ = 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 , )
snake_case_ = CLIPTextModel(UpperCAmelCase_ )
snake_case_ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
snake_case_ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCAmelCase ( self : int , UpperCAmelCase_ : str , UpperCAmelCase_ : List[str]=0 ) ->str:
"""simple docstring"""
snake_case_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
snake_case_ = image / 2 + 0.5
if str(UpperCAmelCase_ ).startswith("""mps""" ):
snake_case_ = torch.manual_seed(UpperCAmelCase_ )
else:
snake_case_ = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
snake_case_ = {
"""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 : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
snake_case_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = CycleDiffusionPipeline(**UpperCAmelCase_ )
snake_case_ = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
snake_case_ = self.get_dummy_inputs(UpperCAmelCase_ )
snake_case_ = pipe(**UpperCAmelCase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = 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 : Union[str, Any] ) ->str:
"""simple docstring"""
snake_case_ = self.get_dummy_components()
for name, module in components.items():
if hasattr(UpperCAmelCase_ , """half""" ):
snake_case_ = module.half()
snake_case_ = CycleDiffusionPipeline(**UpperCAmelCase_ )
snake_case_ = pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
snake_case_ = self.get_dummy_inputs(UpperCAmelCase_ )
snake_case_ = pipe(**UpperCAmelCase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = 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 : Tuple ) ->Optional[int]:
"""simple docstring"""
return super().test_save_load_local()
@unittest.skip("""non-deterministic pipeline""" )
def lowerCAmelCase ( self : int ) ->Optional[int]:
"""simple docstring"""
return super().test_inference_batch_single_identical()
@skip_mps
def lowerCAmelCase ( self : Tuple ) ->List[Any]:
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowerCAmelCase ( self : Any ) ->Tuple:
"""simple docstring"""
return super().test_save_load_optional_components()
@skip_mps
def lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class __A (unittest.TestCase):
'''simple docstring'''
def lowerCAmelCase ( self : List[Any] ) ->Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
snake_case_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
snake_case_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy""" )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = """CompVis/stable-diffusion-v1-4"""
snake_case_ = DDIMScheduler.from_pretrained(UpperCAmelCase_ , subfolder="""scheduler""" )
snake_case_ = CycleDiffusionPipeline.from_pretrained(
UpperCAmelCase_ , scheduler=UpperCAmelCase_ , safety_checker=UpperCAmelCase_ , torch_dtype=torch.floataa , revision="""fp16""" )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
pipe.enable_attention_slicing()
snake_case_ = """A black colored car"""
snake_case_ = """A blue colored car"""
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=UpperCAmelCase_ , source_prompt=UpperCAmelCase_ , image=UpperCAmelCase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=UpperCAmelCase_ , output_type="""np""" , )
snake_case_ = 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 : Tuple ) ->List[str]:
"""simple docstring"""
snake_case_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
snake_case_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy""" )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = """CompVis/stable-diffusion-v1-4"""
snake_case_ = DDIMScheduler.from_pretrained(UpperCAmelCase_ , subfolder="""scheduler""" )
snake_case_ = CycleDiffusionPipeline.from_pretrained(UpperCAmelCase_ , scheduler=UpperCAmelCase_ , safety_checker=UpperCAmelCase_ )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
pipe.enable_attention_slicing()
snake_case_ = """A black colored car"""
snake_case_ = """A blue colored car"""
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=UpperCAmelCase_ , source_prompt=UpperCAmelCase_ , image=UpperCAmelCase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=UpperCAmelCase_ , output_type="""np""" , )
snake_case_ = output.images
assert np.abs(image - expected_image ).max() < 2E-2
| 233 | 0 |
import re
def A_ ( snake_case : Optional[int] ) -> bool:
'''simple docstring'''
__UpperCamelCase = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase , __UpperCAmelCase ) )
if __name__ == "__main__":
lowercase__ : Any = '0094702343221'
print(is_sri_lankan_phone_number(phone))
| 328 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(__UpperCAmelCase ) == 0:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
return min(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ = [90, 23, 6, 33, 21, 65, 123, 3_4423]
snake_case_ = math.log(len(__UpperCAmelCase ), 2 )
print('''Optimal value : ''', end='''''' )
print(minimax(0, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 56 | 0 |
from math import factorial
def lowerCamelCase_ ( UpperCamelCase__ : int = 100 ):
'''simple docstring'''
return sum(int(lowerCAmelCase__ ) for x in str(factorial(lowerCAmelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 365 | import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase = {
"""facebook/maskformer-swin-base-ade""": (
"""https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowercase = logging.get_logger(__name__)
class __lowercase ( A ):
'''simple docstring'''
_A : Any = '''maskformer'''
_A : Any = {'''hidden_size''': '''mask_feature_size'''}
_A : List[str] = ['''resnet''', '''swin''']
_A : Tuple = ['''detr''']
def __init__( self : Optional[Any] , _a : int = 256 , _a : int = 256 , _a : float = 0.1 , _a : bool = False , _a : Optional[Dict] = None , _a : Optional[Dict] = None , _a : float = 0.02 , _a : float = 1.0 , _a : float = 1.0 , _a : float = 1.0 , _a : float = 20.0 , _a : Optional[bool] = None , **_a : List[str] , ):
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
UpperCamelCase__ = SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(_a , _a ):
UpperCamelCase__ = backbone_config.pop('''model_type''' )
UpperCamelCase__ = CONFIG_MAPPING[backbone_model_type]
UpperCamelCase__ = config_class.from_dict(_a )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
F"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
UpperCamelCase__ = DetrConfig()
else:
# verify that the decoder is supported
UpperCamelCase__ = (
decoder_config.pop('''model_type''' ) if isinstance(_a , _a ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
F"""Transformer Decoder {decoder_type} not supported, please use one of"""
F""" {",".join(self.decoders_supported )}""" )
if isinstance(_a , _a ):
UpperCamelCase__ = CONFIG_MAPPING[decoder_type]
UpperCamelCase__ = config_class.from_dict(_a )
UpperCamelCase__ = backbone_config
UpperCamelCase__ = decoder_config
# main feature dimension for the model
UpperCamelCase__ = fpn_feature_size
UpperCamelCase__ = mask_feature_size
# initializer
UpperCamelCase__ = init_std
UpperCamelCase__ = init_xavier_std
# Hungarian matcher && loss
UpperCamelCase__ = cross_entropy_weight
UpperCamelCase__ = dice_weight
UpperCamelCase__ = mask_weight
UpperCamelCase__ = use_auxiliary_loss
UpperCamelCase__ = no_object_weight
UpperCamelCase__ = output_auxiliary_logits
UpperCamelCase__ = self.decoder_config.encoder_attention_heads
UpperCamelCase__ = self.decoder_config.num_hidden_layers
super().__init__(**_a )
@classmethod
def A_ ( cls : Tuple , _a : PretrainedConfig , _a : PretrainedConfig , **_a : str ):
return cls(
backbone_config=_a , decoder_config=_a , **_a , )
def A_ ( self : str ):
UpperCamelCase__ = copy.deepcopy(self.__dict__ )
UpperCamelCase__ = self.backbone_config.to_dict()
UpperCamelCase__ = self.decoder_config.to_dict()
UpperCamelCase__ = self.__class__.model_type
return output
| 35 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _snake_case ( _lowercase ):
lowerCamelCase__: List[Any] = ["vqvae"]
def __init__( self: Tuple , __lowerCamelCase: AutoencoderKL , __lowerCamelCase: UNetaDConditionModel , __lowerCamelCase: Mel , __lowerCamelCase: Union[DDIMScheduler, DDPMScheduler] , ) -> str:
super().__init__()
self.register_modules(unet=__lowerCamelCase , scheduler=__lowerCamelCase , mel=__lowerCamelCase , vqvae=__lowerCamelCase )
def _lowerCamelCase ( self: Union[str, Any] ) -> int:
return 50 if isinstance(self.scheduler , __lowerCamelCase ) else 10_00
@torch.no_grad()
def __call__( self: Optional[int] , __lowerCamelCase: int = 1 , __lowerCamelCase: str = None , __lowerCamelCase: np.ndarray = None , __lowerCamelCase: int = 0 , __lowerCamelCase: int = 0 , __lowerCamelCase: int = None , __lowerCamelCase: torch.Generator = None , __lowerCamelCase: float = 0 , __lowerCamelCase: float = 0 , __lowerCamelCase: torch.Generator = None , __lowerCamelCase: float = 0 , __lowerCamelCase: torch.Tensor = None , __lowerCamelCase: torch.Tensor = None , __lowerCamelCase: int=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
__UpperCAmelCase : Tuple = steps or self.get_default_steps()
self.scheduler.set_timesteps(__lowerCamelCase )
__UpperCAmelCase : Any = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
__UpperCAmelCase : Optional[int] = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
__UpperCAmelCase : Union[str, Any] = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__lowerCamelCase , device=self.device , )
__UpperCAmelCase : List[Any] = noise
__UpperCAmelCase : Dict = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__lowerCamelCase , __lowerCamelCase )
__UpperCAmelCase : List[Any] = self.mel.audio_slice_to_image(__lowerCamelCase )
__UpperCAmelCase : Optional[Any] = np.frombuffer(input_image.tobytes() , dtype="uint8" ).reshape(
(input_image.height, input_image.width) )
__UpperCAmelCase : Optional[Any] = (input_image / 2_55) * 2 - 1
__UpperCAmelCase : int = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
__UpperCAmelCase : Dict = self.vqvae.encode(torch.unsqueeze(__lowerCamelCase , 0 ) ).latent_dist.sample(
generator=__lowerCamelCase )[0]
__UpperCAmelCase : Any = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
__UpperCAmelCase : Any = self.scheduler.add_noise(__lowerCamelCase , __lowerCamelCase , self.scheduler.timesteps[start_step - 1] )
__UpperCAmelCase : Tuple = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
__UpperCAmelCase : Tuple = int(mask_start_secs * pixels_per_second )
__UpperCAmelCase : Dict = int(mask_end_secs * pixels_per_second )
__UpperCAmelCase : Optional[int] = self.scheduler.add_noise(__lowerCamelCase , __lowerCamelCase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , __lowerCamelCase ):
__UpperCAmelCase : Any = self.unet(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )["sample"]
else:
__UpperCAmelCase : str = self.unet(__lowerCamelCase , __lowerCamelCase )["sample"]
if isinstance(self.scheduler , __lowerCamelCase ):
__UpperCAmelCase : str = self.scheduler.step(
model_output=__lowerCamelCase , timestep=__lowerCamelCase , sample=__lowerCamelCase , eta=__lowerCamelCase , generator=__lowerCamelCase , )["prev_sample"]
else:
__UpperCAmelCase : List[str] = self.scheduler.step(
model_output=__lowerCamelCase , timestep=__lowerCamelCase , sample=__lowerCamelCase , generator=__lowerCamelCase , )["prev_sample"]
if mask is not None:
if mask_start > 0:
__UpperCAmelCase : Union[str, Any] = mask[:, step, :, :mask_start]
if mask_end > 0:
__UpperCAmelCase : List[Any] = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
__UpperCAmelCase : Optional[int] = 1 / self.vqvae.config.scaling_factor * images
__UpperCAmelCase : Dict = self.vqvae.decode(__lowerCamelCase )["sample"]
__UpperCAmelCase : Dict = (images / 2 + 0.5).clamp(0 , 1 )
__UpperCAmelCase : Optional[int] = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
__UpperCAmelCase : str = (images * 2_55).round().astype("uint8" )
__UpperCAmelCase : List[Any] = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__lowerCamelCase , mode="RGB" ).convert("L" ) for _ in images) )
__UpperCAmelCase : Optional[int] = [self.mel.image_to_audio(__lowerCamelCase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__lowerCamelCase )[:, np.newaxis, :] ) , **ImagePipelineOutput(__lowerCamelCase ) )
@torch.no_grad()
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: List[Image.Image] , __lowerCamelCase: int = 50 ) -> np.ndarray:
assert isinstance(self.scheduler , __lowerCamelCase )
self.scheduler.set_timesteps(__lowerCamelCase )
__UpperCAmelCase : Any = np.array(
[np.frombuffer(image.tobytes() , dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
__UpperCAmelCase : Dict = (sample / 2_55) * 2 - 1
__UpperCAmelCase : int = torch.Tensor(__lowerCamelCase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
__UpperCAmelCase : Union[str, Any] = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
__UpperCAmelCase : Union[str, Any] = self.scheduler.alphas_cumprod[t]
__UpperCAmelCase : Dict = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
__UpperCAmelCase : Tuple = 1 - alpha_prod_t
__UpperCAmelCase : int = self.unet(__lowerCamelCase , __lowerCamelCase )["sample"]
__UpperCAmelCase : Any = (1 - alpha_prod_t_prev) ** 0.5 * model_output
__UpperCAmelCase : Any = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
__UpperCAmelCase : Tuple = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def _lowerCamelCase ( __lowerCamelCase: torch.Tensor , __lowerCamelCase: torch.Tensor , __lowerCamelCase: float ) -> torch.Tensor:
__UpperCAmelCase : Dict = acos(torch.dot(torch.flatten(__lowerCamelCase ) , torch.flatten(__lowerCamelCase ) ) / torch.norm(__lowerCamelCase ) / torch.norm(__lowerCamelCase ) )
return sin((1 - alpha) * theta ) * xa / sin(__lowerCamelCase ) + sin(alpha * theta ) * xa / sin(__lowerCamelCase )
| 157 | from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> tuple[int | None, int | None, float]:
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
__UpperCAmelCase : List[str] = (low + high) // 2
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Tuple = max_subarray(snake_case__, snake_case__, snake_case__ )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = max_subarray(snake_case__, mid + 1, snake_case__ )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = max_cross_sum(snake_case__, snake_case__, snake_case__, snake_case__ )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__, snake_case__ ) -> tuple[int, int, float]:
__UpperCAmelCase , __UpperCAmelCase : Any = float("-inf" ), -1
__UpperCAmelCase , __UpperCAmelCase : Dict = float("-inf" ), -1
__UpperCAmelCase : int | float = 0
for i in range(snake_case__, low - 1, -1 ):
summ += arr[i]
if summ > left_sum:
__UpperCAmelCase : Optional[int] = summ
__UpperCAmelCase : Optional[Any] = i
__UpperCAmelCase : List[Any] = 0
for i in range(mid + 1, high + 1 ):
summ += arr[i]
if summ > right_sum:
__UpperCAmelCase : List[str] = summ
__UpperCAmelCase : Dict = i
return max_left, max_right, (left_sum + right_sum)
def _UpperCamelCase ( snake_case__ ) -> float:
__UpperCAmelCase : Optional[int] = [randint(1, snake_case__ ) for _ in range(snake_case__ )]
__UpperCAmelCase : Optional[int] = time.time()
max_subarray(snake_case__, 0, input_size - 1 )
__UpperCAmelCase : List[str] = time.time()
return end - start
def _UpperCamelCase ( ) -> None:
__UpperCAmelCase : str = [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
__UpperCAmelCase : Optional[Any] = [time_max_subarray(snake_case__ ) for input_size in input_sizes]
print("No of Inputs\t\tTime Taken" )
for input_size, runtime in zip(snake_case__, snake_case__ ):
print(snake_case__, "\t\t", snake_case__ )
plt.plot(snake_case__, snake_case__ )
plt.xlabel("Number of Inputs" )
plt.ylabel("Time taken in seconds" )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 157 | 1 |
from math import isclose, sqrt
def lowerCamelCase__ ( a , a , a ) -> tuple[float, float, float]:
_A: List[str] = point_y / 4 / point_x
_A: str = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
_A: Any = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
_A: Dict = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
_A: Tuple = outgoing_gradient**2 + 4
_A: Any = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
_A: Union[str, Any] = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
_A: List[str] = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
_A: Optional[int] = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
_A: Union[str, Any] = x_minus if isclose(a , a ) else x_plus
_A: Union[str, Any] = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def lowerCamelCase__ ( a = 1.4 , a = -9.6 ) -> int:
_A: int = 0
_A: float = first_x_coord
_A: float = first_y_coord
_A: float = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
_A , _A , _A: List[str] = next_point(a , a , a )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(F"""{solution() = }""")
| 301 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCAmelCase__ : Tuple = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'PatchingSpec',
],
'convert': ['export', 'validate_model_outputs'],
'features': ['FeaturesManager'],
'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
UpperCAmelCase__ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 301 | 1 |
from __future__ import annotations
def A ( _SCREAMING_SNAKE_CASE ) -> int:
# preprocessing the first row
for i in range(1 ,len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 ,len(_SCREAMING_SNAKE_CASE ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 ,len(_SCREAMING_SNAKE_CASE ) ):
for j in range(1 ,len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] ,matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE ):
_a = ["""input_features"""]
def __init__( self , lowerCAmelCase=80 , lowerCAmelCase=16_000 , lowerCAmelCase=160 , lowerCAmelCase=30 , lowerCAmelCase=400 , lowerCAmelCase=0.0 , lowerCAmelCase=False , **lowerCAmelCase , ) -> Any:
'''simple docstring'''
super().__init__(
feature_size=lowerCAmelCase , sampling_rate=lowerCAmelCase , padding_value=lowerCAmelCase , return_attention_mask=lowerCAmelCase , **lowerCAmelCase , )
_lowercase =n_fft
_lowercase =hop_length
_lowercase =chunk_length
_lowercase =chunk_length * sampling_rate
_lowercase =self.n_samples // hop_length
_lowercase =sampling_rate
_lowercase =mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCAmelCase , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=lowerCAmelCase , norm='slaney' , mel_scale='slaney' , )
def A__ ( self , lowerCAmelCase ) -> np.ndarray:
'''simple docstring'''
_lowercase =spectrogram(
lowerCAmelCase , window_function(self.n_fft , 'hann' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel='log10' , )
_lowercase =log_spec[:, :-1]
_lowercase =np.maximum(lowerCAmelCase , log_spec.max() - 8.0 )
_lowercase =(log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def A__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 0.0 ) -> List[np.ndarray]:
'''simple docstring'''
if attention_mask is not None:
_lowercase =np.array(lowerCAmelCase , np.intaa )
_lowercase =[]
for vector, length in zip(lowerCAmelCase , attention_mask.sum(-1 ) ):
_lowercase =(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
_lowercase =padding_value
normed_input_values.append(lowerCAmelCase )
else:
_lowercase =[(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self , lowerCAmelCase , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = "max_length" , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , **lowerCAmelCase , ) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
F''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
_lowercase =isinstance(lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
_lowercase =is_batched_numpy or (
isinstance(lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_lowercase =[np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase , np.ndarray ):
_lowercase =np.asarray(lowerCAmelCase , dtype=np.floataa )
elif isinstance(lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowercase =raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowercase =[np.asarray([raw_speech] ).T]
_lowercase =BatchFeature({'input_features': raw_speech} )
# convert into correct format for padding
_lowercase =self.pad(
lowerCAmelCase , padding=lowerCAmelCase , max_length=max_length if max_length else self.n_samples , truncation=lowerCAmelCase , pad_to_multiple_of=lowerCAmelCase , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
_lowercase =self.zero_mean_unit_var_norm(
padded_inputs['input_features'] , attention_mask=padded_inputs['attention_mask'] , padding_value=self.padding_value , )
_lowercase =np.stack(padded_inputs['input_features'] , axis=0 )
# make sure list is in array format
_lowercase =padded_inputs.get('input_features' ).transpose(2 , 0 , 1 )
_lowercase =[self._np_extract_fbank_features(lowerCAmelCase ) for waveform in input_features[0]]
if isinstance(input_features[0] , lowerCAmelCase ):
_lowercase =[np.asarray(lowerCAmelCase , dtype=np.floataa ) for feature in input_features]
else:
_lowercase =input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
_lowercase =padded_inputs['attention_mask'][:, :: self.hop_length]
if return_tensors is not None:
_lowercase =padded_inputs.convert_to_tensors(lowerCAmelCase )
return padded_inputs
def A__ ( self ) -> Dict[str, Any]:
'''simple docstring'''
_lowercase =copy.deepcopy(self.__dict__ )
_lowercase =self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 205 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Dict = {
"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:
_lowercase : List[Any] = [
"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
_lowercase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 272 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_lowercase : List[str] = ""
if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):
class __SCREAMING_SNAKE_CASE ( tr.AbstractTransform ):
'''simple docstring'''
def __init__( self : List[Any], lowerCamelCase : str = " " )-> List[str]:
lowerCamelCase__ : List[str] =sentence_delimiter
def snake_case ( self : Any, lowerCamelCase : str )-> Optional[Any]:
return list(lowerCamelCase )
def snake_case ( self : Optional[Any], lowerCamelCase : List[str] )-> Tuple:
lowerCamelCase__ : Optional[int] =[]
for sent_idx, sentence in enumerate(lowerCamelCase ):
chars.extend(self.process_string(lowerCamelCase ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCamelCase ) - 1:
chars.append(self.sentence_delimiter )
return chars
_lowercase : Optional[int] = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
_lowercase : List[str] = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
_lowercase : Dict = "\\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"
_lowercase : List[Any] = "\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (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 characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"
_lowercase : Dict = "\nComputes CER score of transcribed segments against references.\nArgs:\n references: list of references for each speech input.\n predictions: list of transcribtions to score.\n concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n (float): the character 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 >>> cer = datasets.load_metric(\"cer\")\n >>> cer_score = cer.compute(predictions=predictions, references=references)\n >>> print(cer_score)\n 0.34146341463414637\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def snake_case ( self : Dict )-> Optional[Any]:
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''',
'''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
], )
def snake_case ( self : Optional[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : List[str], lowerCamelCase : Dict=False )-> List[Any]:
if concatenate_texts:
return jiwer.compute_measures(
lowerCamelCase, lowerCamelCase, truth_transform=lowerCamelCase, hypothesis_transform=lowerCamelCase, )["wer"]
lowerCamelCase__ : Optional[Any] =0
lowerCamelCase__ : Union[str, Any] =0
for prediction, reference in zip(lowerCamelCase, lowerCamelCase ):
lowerCamelCase__ : int =jiwer.compute_measures(
lowerCamelCase, lowerCamelCase, truth_transform=lowerCamelCase, hypothesis_transform=lowerCamelCase, )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 272 | 1 |
from __future__ import annotations
from math import gcd
def a__ ( __UpperCamelCase , __UpperCamelCase = 2 , __UpperCamelCase = 1 , __UpperCamelCase = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError("The input value cannot be less than 2" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
return (pow(__UpperCamelCase , 2 ) + step) % modulus
for _ in range(__UpperCamelCase ):
# These track the position within the cycle detection logic.
SCREAMING_SNAKE_CASE_ = seed
SCREAMING_SNAKE_CASE_ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
SCREAMING_SNAKE_CASE_ = rand_fn(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = rand_fn(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = rand_fn(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
SCREAMING_SNAKE_CASE_ = gcd(hare - tortoise , __UpperCamelCase )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
SCREAMING_SNAKE_CASE_ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
A : Dict = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
A : Dict = parser.parse_args()
A : Union[str, Any] = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(f"{args.num} is probably prime")
else:
A : Optional[Any] = args.num // divisor
print(f"{args.num} = {divisor} * {quotient}")
| 118 | import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''char'''
lowerCamelCase__ = '''bpe'''
lowerCamelCase__ = '''wp'''
A : Tuple = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''image_processor''', '''char_tokenizer''']
lowerCamelCase__ = '''ViTImageProcessor'''
lowerCamelCase__ = '''MgpstrTokenizer'''
def __init__( self : Optional[Any] , __magic_name__ : Tuple=None , __magic_name__ : int=None , **__magic_name__ : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __magic_name__ , )
SCREAMING_SNAKE_CASE_ = kwargs.pop("feature_extractor" )
SCREAMING_SNAKE_CASE_ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
SCREAMING_SNAKE_CASE_ = tokenizer
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained("gpt2" )
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(__magic_name__ , __magic_name__ )
def __call__( self : Dict , __magic_name__ : Union[str, Any]=None , __magic_name__ : Union[str, Any]=None , __magic_name__ : Dict=None , **__magic_name__ : Tuple ) -> int:
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
SCREAMING_SNAKE_CASE_ = self.image_processor(__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
if text is not None:
SCREAMING_SNAKE_CASE_ = self.char_tokenizer(__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
SCREAMING_SNAKE_CASE_ = encodings["input_ids"]
return inputs
def __A ( self : Tuple , __magic_name__ : int ) -> Any:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = sequences
SCREAMING_SNAKE_CASE_ = char_preds.size(0 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self._decode_helper(__magic_name__ , "char" )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self._decode_helper(__magic_name__ , "bpe" )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self._decode_helper(__magic_name__ , "wp" )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = [char_scores[i], bpe_scores[i], wp_scores[i]]
SCREAMING_SNAKE_CASE_ = [char_strs[i], bpe_strs[i], wp_strs[i]]
SCREAMING_SNAKE_CASE_ = scores.index(max(__magic_name__ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = final_strs
SCREAMING_SNAKE_CASE_ = final_scores
SCREAMING_SNAKE_CASE_ = char_strs
SCREAMING_SNAKE_CASE_ = bpe_strs
SCREAMING_SNAKE_CASE_ = wp_strs
return out
def __A ( self : int , __magic_name__ : List[Any] , __magic_name__ : str ) -> Any:
if format == DecodeType.CHARACTER:
SCREAMING_SNAKE_CASE_ = self.char_decode
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = "[s]"
elif format == DecodeType.BPE:
SCREAMING_SNAKE_CASE_ = self.bpe_decode
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = "#"
elif format == DecodeType.WORDPIECE:
SCREAMING_SNAKE_CASE_ = self.wp_decode
SCREAMING_SNAKE_CASE_ = 102
SCREAMING_SNAKE_CASE_ = "[SEP]"
else:
raise ValueError(F'''Format {format} is not supported.''' )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = [], []
SCREAMING_SNAKE_CASE_ = pred_logits.size(0 )
SCREAMING_SNAKE_CASE_ = pred_logits.size(1 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = pred_logits.topk(1 , dim=-1 , largest=__magic_name__ , sorted=__magic_name__ )
SCREAMING_SNAKE_CASE_ = preds_index.view(-1 , __magic_name__ )[:, 1:]
SCREAMING_SNAKE_CASE_ = decoder(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = torch.nn.functional.softmax(__magic_name__ , dim=2 ).max(dim=2 )
SCREAMING_SNAKE_CASE_ = preds_max_prob[:, 1:]
for index in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = preds_str[index].find(__magic_name__ )
SCREAMING_SNAKE_CASE_ = preds_str[index][:pred_eos]
SCREAMING_SNAKE_CASE_ = preds_index[index].cpu().tolist()
SCREAMING_SNAKE_CASE_ = pred_index.index(__magic_name__ ) if eos_token in pred_index else -1
SCREAMING_SNAKE_CASE_ = preds_max_prob[index][: pred_eos_index + 1]
SCREAMING_SNAKE_CASE_ = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__magic_name__ )
conf_scores.append(__magic_name__ )
return dec_strs, conf_scores
def __A ( self : Any , __magic_name__ : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE_ = [seq.replace(" " , "" ) for seq in self.char_tokenizer.batch_decode(__magic_name__ )]
return decode_strs
def __A ( self : Any , __magic_name__ : Union[str, Any] ) -> Tuple:
return self.bpe_tokenizer.batch_decode(__magic_name__ )
def __A ( self : str , __magic_name__ : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = [seq.replace(" " , "" ) for seq in self.wp_tokenizer.batch_decode(__magic_name__ )]
return decode_strs
| 118 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_:Optional[int] = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tokenization_canine""": ["""CanineTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:str = [
"""CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CanineForMultipleChoice""",
"""CanineForQuestionAnswering""",
"""CanineForSequenceClassification""",
"""CanineForTokenClassification""",
"""CanineLayer""",
"""CanineModel""",
"""CaninePreTrainedModel""",
"""load_tf_weights_in_canine""",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
SCREAMING_SNAKE_CASE_:Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 354 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_:Any = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
"""MobileNetV2OnnxConfig""",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:List[Any] = ["""MobileNetV2FeatureExtractor"""]
SCREAMING_SNAKE_CASE_:Tuple = ["""MobileNetV2ImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Any = [
"""MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MobileNetV2ForImageClassification""",
"""MobileNetV2ForSemanticSegmentation""",
"""MobileNetV2Model""",
"""MobileNetV2PreTrainedModel""",
"""load_tf_weights_in_mobilenet_v2""",
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
SCREAMING_SNAKE_CASE_:Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 115 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = ['pixel_values']
def __init__( self , __snake_case = True , __snake_case = None , __snake_case = PILImageResampling.BICUBIC , __snake_case = True , __snake_case = 1 / 2_5_5 , __snake_case = True , __snake_case = None , __snake_case = None , __snake_case = True , **__snake_case , ):
super().__init__(**__snake_case )
snake_case = size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
snake_case = get_size_dict(__snake_case , default_to_square=__snake_case )
snake_case = do_resize
snake_case = size
snake_case = resample
snake_case = do_rescale
snake_case = rescale_factor
snake_case = do_normalize
snake_case = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
snake_case = image_std if image_std is not None else OPENAI_CLIP_STD
snake_case = do_convert_rgb
def a_ ( self , __snake_case , __snake_case , __snake_case = PILImageResampling.BICUBIC , __snake_case = None , **__snake_case , ):
snake_case = get_size_dict(__snake_case , default_to_square=__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
snake_case = (size['''height'''], size['''width'''])
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def a_ ( self , __snake_case , __snake_case , __snake_case = None , **__snake_case , ):
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def a_ ( self , __snake_case , __snake_case , __snake_case , __snake_case = None , **__snake_case , ):
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def a_ ( self , __snake_case , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = None , __snake_case = ChannelDimension.FIRST , **__snake_case , ):
snake_case = do_resize if do_resize is not None else self.do_resize
snake_case = resample if resample is not None else self.resample
snake_case = do_rescale if do_rescale is not None else self.do_rescale
snake_case = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case = do_normalize if do_normalize is not None else self.do_normalize
snake_case = image_mean if image_mean is not None else self.image_mean
snake_case = image_std if image_std is not None else self.image_std
snake_case = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
snake_case = size if size is not None else self.size
snake_case = get_size_dict(__snake_case , default_to_square=__snake_case )
snake_case = make_list_of_images(__snake_case )
if not valid_images(__snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
snake_case = [convert_to_rgb(__snake_case ) for image in images]
# All transformations expect numpy arrays.
snake_case = [to_numpy_array(__snake_case ) for image in images]
if do_resize:
snake_case = [self.resize(image=__snake_case , size=__snake_case , resample=__snake_case ) for image in images]
if do_rescale:
snake_case = [self.rescale(image=__snake_case , scale=__snake_case ) for image in images]
if do_normalize:
snake_case = [self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case ) for image in images]
snake_case = [to_channel_dimension_format(__snake_case , __snake_case ) for image in images]
snake_case = BatchFeature(data={'''pixel_values''': images} , tensor_type=__snake_case )
return encoded_outputs
| 127 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[Any] = {
"google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
"google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
"google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
"google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = 'mobilenet_v2'
def __init__( self , __snake_case=3 , __snake_case=2_2_4 , __snake_case=1.0 , __snake_case=8 , __snake_case=8 , __snake_case=6 , __snake_case=3_2 , __snake_case=True , __snake_case=True , __snake_case="relu6" , __snake_case=True , __snake_case=0.8 , __snake_case=0.02 , __snake_case=0.001 , __snake_case=2_5_5 , **__snake_case , ):
super().__init__(**__snake_case )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
snake_case = num_channels
snake_case = image_size
snake_case = depth_multiplier
snake_case = depth_divisible_by
snake_case = min_depth
snake_case = expand_ratio
snake_case = output_stride
snake_case = first_layer_is_expansion
snake_case = finegrained_output
snake_case = hidden_act
snake_case = tf_padding
snake_case = classifier_dropout_prob
snake_case = initializer_range
snake_case = layer_norm_eps
snake_case = semantic_loss_ignore_index
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = version.parse('1.11' )
@property
def a_ ( self ):
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def a_ ( self ):
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def a_ ( self ):
return 1E-4
| 127 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : Optional[int] = ['''pixel_values''']
def __init__( self , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = PILImageResampling.BICUBIC , _UpperCamelCase = True , _UpperCamelCase = True , _UpperCamelCase = 1 / 2_5_5 , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = None , **_UpperCamelCase , ) -> None:
super().__init__(**_UpperCamelCase )
UpperCAmelCase_ : str = size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
UpperCAmelCase_ : List[Any] = get_size_dict(_UpperCamelCase )
UpperCAmelCase_ : str = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
UpperCAmelCase_ : Optional[int] = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase , param_name='crop_size' )
UpperCAmelCase_ : Dict = do_resize
UpperCAmelCase_ : List[str] = do_rescale
UpperCAmelCase_ : Any = do_normalize
UpperCAmelCase_ : Tuple = do_center_crop
UpperCAmelCase_ : Any = crop_size
UpperCAmelCase_ : Optional[int] = size
UpperCAmelCase_ : List[Any] = resample
UpperCAmelCase_ : Dict = rescale_factor
UpperCAmelCase_ : Optional[Any] = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCAmelCase_ : Dict = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = PILImageResampling.BILINEAR , _UpperCamelCase = None , **_UpperCamelCase , ) -> np.ndarray:
UpperCAmelCase_ : int = get_size_dict(_UpperCamelCase )
if "shortest_edge" in size:
UpperCAmelCase_ : int = get_resize_output_image_size(_UpperCamelCase , size=size['shortest_edge'] , default_to_square=_UpperCamelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
UpperCAmelCase_ : Tuple = (size['height'], size['width'])
else:
raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}" )
return resize(_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ) -> np.ndarray:
UpperCAmelCase_ : Any = get_size_dict(_UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
return center_crop(_UpperCamelCase , size=(size['height'], size['width']) , data_format=_UpperCamelCase , **_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase ) -> np.ndarray:
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ) -> np.ndarray:
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 , ) -> BatchFeature:
UpperCAmelCase_ : Tuple = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ : Any = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ : int = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ : Tuple = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ : Optional[int] = get_size_dict(_UpperCamelCase , param_name='crop_size' , default_to_square=_UpperCamelCase )
UpperCAmelCase_ : Any = resample if resample is not None else self.resample
UpperCAmelCase_ : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ : List[Any] = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ : Any = image_std if image_std is not None else self.image_std
UpperCAmelCase_ : List[str] = size if size is not None else self.size
UpperCAmelCase_ : Union[str, Any] = get_size_dict(_UpperCamelCase )
if not is_batched(_UpperCamelCase ):
UpperCAmelCase_ : Dict = [images]
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:
raise ValueError('Size 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.' )
# All transformations expect numpy arrays.
UpperCAmelCase_ : Any = [to_numpy_array(_UpperCamelCase ) for image in images]
if do_resize:
UpperCAmelCase_ : List[Any] = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
if do_center_crop:
UpperCAmelCase_ : Tuple = [self.center_crop(image=_UpperCamelCase , size=_UpperCamelCase ) for image in images]
if do_rescale:
UpperCAmelCase_ : Union[str, Any] = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase ) for image in images]
if do_normalize:
UpperCAmelCase_ : int = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase ) for image in images]
UpperCAmelCase_ : Tuple = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
UpperCAmelCase_ : Tuple = {'pixel_values': images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase )
| 365 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = Dict[str, Any]
__UpperCAmelCase = List[Prediction]
@add_end_docstrings(_snake_case )
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> Optional[int]:
super().__init__(*_UpperCamelCase , **_UpperCamelCase )
if self.framework == "tf":
raise ValueError(f"The {self.__class__} is only available in PyTorch." )
requires_backends(self , 'vision' )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def __UpperCAmelCase ( self , **_UpperCamelCase ) -> Tuple:
UpperCAmelCase_ : str = {}
if "threshold" in kwargs:
UpperCAmelCase_ : Tuple = kwargs['threshold']
return {}, {}, postprocess_kwargs
def __call__( self , *_UpperCamelCase , **_UpperCamelCase ) -> Union[Predictions, List[Prediction]]:
return super().__call__(*_UpperCamelCase , **_UpperCamelCase )
def __UpperCAmelCase ( self , _UpperCamelCase ) -> Dict:
UpperCAmelCase_ : Any = load_image(_UpperCamelCase )
UpperCAmelCase_ : Any = torch.IntTensor([[image.height, image.width]] )
UpperCAmelCase_ : str = self.image_processor(images=[image] , return_tensors='pt' )
if self.tokenizer is not None:
UpperCAmelCase_ : List[str] = self.tokenizer(text=inputs['words'] , boxes=inputs['boxes'] , return_tensors='pt' )
UpperCAmelCase_ : Any = target_size
return inputs
def __UpperCAmelCase ( self , _UpperCamelCase ) -> List[Any]:
UpperCAmelCase_ : Any = model_inputs.pop('target_size' )
UpperCAmelCase_ : Optional[Any] = self.model(**_UpperCamelCase )
UpperCAmelCase_ : Dict = outputs.__class__({'target_size': target_size, **outputs} )
if self.tokenizer is not None:
UpperCAmelCase_ : List[str] = model_inputs['bbox']
return model_outputs
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase=0.9 ) -> List[str]:
UpperCAmelCase_ : List[Any] = model_outputs['target_size']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
UpperCAmelCase_ , UpperCAmelCase_ : str = target_size[0].tolist()
def unnormalize(_UpperCamelCase ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1_0_0_0),
(height * bbox[1] / 1_0_0_0),
(width * bbox[2] / 1_0_0_0),
(height * bbox[3] / 1_0_0_0),
] ) )
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = model_outputs['logits'].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
UpperCAmelCase_ : Union[str, Any] = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
UpperCAmelCase_ : Tuple = [unnormalize(_UpperCamelCase ) for bbox in model_outputs['bbox'].squeeze(0 )]
UpperCAmelCase_ : List[str] = ['score', 'label', 'box']
UpperCAmelCase_ : Any = [dict(zip(_UpperCamelCase , _UpperCamelCase ) ) for vals in zip(scores.tolist() , _UpperCamelCase , _UpperCamelCase ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
UpperCAmelCase_ : Union[str, Any] = self.image_processor.post_process_object_detection(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ : str = raw_annotations[0]
UpperCAmelCase_ : str = raw_annotation['scores']
UpperCAmelCase_ : Tuple = raw_annotation['labels']
UpperCAmelCase_ : List[Any] = raw_annotation['boxes']
UpperCAmelCase_ : Union[str, Any] = scores.tolist()
UpperCAmelCase_ : int = [self.model.config.idalabel[label.item()] for label in labels]
UpperCAmelCase_ : Any = [self._get_bounding_box(_UpperCamelCase ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
UpperCAmelCase_ : int = ['score', 'label', 'box']
UpperCAmelCase_ : Dict = [
dict(zip(_UpperCamelCase , _UpperCamelCase ) )
for vals in zip(raw_annotation['scores'] , raw_annotation['labels'] , raw_annotation['boxes'] )
]
return annotation
def __UpperCAmelCase ( self , _UpperCamelCase ) -> Dict[str, int]:
if self.framework != "pt":
raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.' )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Dict = box.int().tolist()
UpperCAmelCase_ : str = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 145 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : List[Any] = {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = ["XGLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = ["XGLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = [
"XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[int] = [
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = [
"TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 228 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase : Tuple = [float(x) for x in input("Enter the elements of first array: ").split()]
__UpperCamelCase : List[Any] = [float(x) for x in input("Enter the elements of second array: ").split()]
print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 228 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> int:
if not isinstance(_lowercase , _lowercase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
UpperCAmelCase : Optional[Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338 | 0 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
lowerCAmelCase__ : Any = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def UpperCamelCase__ ( A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__=False , ) -> List[str]:
output_path.parent.mkdir(parents=A__ , exist_ok=A__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
A__ , A__ , f=output_path.as_posix() , input_names=A__ , output_names=A__ , dynamic_axes=A__ , do_constant_folding=A__ , use_external_data_format=A__ , enable_onnx_checker=A__ , opset_version=A__ , )
else:
export(
A__ , A__ , f=output_path.as_posix() , input_names=A__ , output_names=A__ , dynamic_axes=A__ , do_constant_folding=A__ , opset_version=A__ , )
@torch.no_grad()
def UpperCamelCase__ ( A__ , A__ , A__ , A__ = False ) -> Optional[int]:
snake_case__ : Optional[Any] = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
snake_case__ : Union[str, Any] = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
snake_case__ : Tuple = 'cpu'
snake_case__ : Optional[int] = StableDiffusionPipeline.from_pretrained(A__ , torch_dtype=A__ ).to(A__ )
snake_case__ : Any = Path(A__ )
# TEXT ENCODER
snake_case__ : Union[str, Any] = pipeline.text_encoder.config.max_position_embeddings
snake_case__ : List[str] = pipeline.text_encoder.config.hidden_size
snake_case__ : Union[str, Any] = pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=A__ , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=A__ , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=A__ , )
del pipeline.text_encoder
# UNET
snake_case__ : str = pipeline.unet.config.in_channels
snake_case__ : List[str] = pipeline.unet.config.sample_size
snake_case__ : Any = output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , A__ , A__ , A__ ).to(device=A__ , dtype=A__ ),
torch.randn(2 ).to(device=A__ , dtype=A__ ),
torch.randn(2 , A__ , A__ ).to(device=A__ , dtype=A__ ),
False,
) , output_path=A__ , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=A__ , use_external_data_format=A__ , )
snake_case__ : Tuple = str(unet_path.absolute().as_posix() )
snake_case__ : Dict = os.path.dirname(A__ )
snake_case__ : Tuple = onnx.load(A__ )
# clean up existing tensor files
shutil.rmtree(A__ )
os.mkdir(A__ )
# collate external tensor files into one
onnx.save_model(
A__ , A__ , save_as_external_data=A__ , all_tensors_to_one_file=A__ , location='weights.pb' , convert_attribute=A__ , )
del pipeline.unet
# VAE ENCODER
snake_case__ : Optional[int] = pipeline.vae
snake_case__ : Any = vae_encoder.config.in_channels
snake_case__ : Dict = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
snake_case__ : Tuple = lambda A__ , A__ : vae_encoder.encode(A__ , A__ )[0].sample()
onnx_export(
A__ , model_args=(
torch.randn(1 , A__ , A__ , A__ ).to(device=A__ , dtype=A__ ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=A__ , )
# VAE DECODER
snake_case__ : Optional[Any] = pipeline.vae
snake_case__ : Any = vae_decoder.config.latent_channels
snake_case__ : List[str] = vae_decoder.config.out_channels
# forward only through the decoder part
snake_case__ : Optional[int] = vae_encoder.decode
onnx_export(
A__ , model_args=(
torch.randn(1 , A__ , A__ , A__ ).to(device=A__ , dtype=A__ ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=A__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
snake_case__ : Optional[int] = pipeline.safety_checker
snake_case__ : Tuple = safety_checker.config.vision_config.num_channels
snake_case__ : Dict = safety_checker.config.vision_config.image_size
snake_case__ : Optional[int] = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , A__ , A__ , A__ , ).to(device=A__ , dtype=A__ ),
torch.randn(1 , A__ , A__ , A__ ).to(device=A__ , dtype=A__ ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=A__ , )
del pipeline.safety_checker
snake_case__ : List[Any] = OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
snake_case__ : Tuple = pipeline.feature_extractor
else:
snake_case__ : Dict = None
snake_case__ : Optional[int] = None
snake_case__ : Optional[Any] = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=A__ , feature_extractor=A__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(A__ )
print('ONNX pipeline saved to' , A__ )
del pipeline
del onnx_pipeline
snake_case__ : Dict = OnnxStableDiffusionPipeline.from_pretrained(A__ , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
lowerCAmelCase__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=14,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
lowerCAmelCase__ : Union[str, Any] = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 143 | import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ : int = logging.get_logger(__name__)
def UpperCamelCase__ ( A__ , A__=False ) -> List[Any]:
snake_case__ : str = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('cls_token', 'deit.embeddings.cls_token'),
('dist_token', 'deit.embeddings.distillation_token'),
('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
('pos_embed', 'deit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
('pre_logits.fc.weight', 'pooler.dense.weight'),
('pre_logits.fc.bias', 'pooler.dense.bias'),
] )
# if just the base model, we should remove "deit" from all keys that start with "deit"
snake_case__ : Dict = [(pair[0], pair[1][4:]) if pair[1].startswith('deit' ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
('norm.weight', 'deit.layernorm.weight'),
('norm.bias', 'deit.layernorm.bias'),
('head.weight', 'cls_classifier.weight'),
('head.bias', 'cls_classifier.bias'),
('head_dist.weight', 'distillation_classifier.weight'),
('head_dist.bias', 'distillation_classifier.bias'),
] )
return rename_keys
def UpperCamelCase__ ( A__ , A__ , A__=False ) -> Dict:
for i in range(config.num_hidden_layers ):
if base_model:
snake_case__ : Tuple = ''
else:
snake_case__ : List[Any] = 'deit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case__ : Tuple = state_dict.pop(F"""blocks.{i}.attn.qkv.weight""" )
snake_case__ : List[Any] = state_dict.pop(F"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : int = in_proj_weight[
: config.hidden_size, :
]
snake_case__ : Optional[Any] = in_proj_bias[: config.hidden_size]
snake_case__ : List[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case__ : str = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case__ : Tuple = in_proj_weight[
-config.hidden_size :, :
]
snake_case__ : int = in_proj_bias[-config.hidden_size :]
def UpperCamelCase__ ( A__ , A__ , A__ ) -> str:
snake_case__ : Optional[int] = dct.pop(A__ )
snake_case__ : int = val
def UpperCamelCase__ ( ) -> Dict:
snake_case__ : str = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case__ : Dict = Image.open(requests.get(A__ , stream=A__ ).raw )
return im
@torch.no_grad()
def UpperCamelCase__ ( A__ , A__ ) -> List[str]:
snake_case__ : List[Any] = DeiTConfig()
# all deit models have fine-tuned heads
snake_case__ : Optional[int] = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
snake_case__ : Any = 1000
snake_case__ : Union[str, Any] = 'huggingface/label-files'
snake_case__ : int = 'imagenet-1k-id2label.json'
snake_case__ : str = json.load(open(hf_hub_download(A__ , A__ , repo_type='dataset' ) , 'r' ) )
snake_case__ : int = {int(A__ ): v for k, v in idalabel.items()}
snake_case__ : List[Any] = idalabel
snake_case__ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case__ : Tuple = int(deit_name[-6:-4] )
snake_case__ : str = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith('tiny' ):
snake_case__ : Optional[int] = 192
snake_case__ : str = 768
snake_case__ : Optional[Any] = 12
snake_case__ : Tuple = 3
elif deit_name[9:].startswith('small' ):
snake_case__ : str = 384
snake_case__ : str = 1536
snake_case__ : Dict = 12
snake_case__ : str = 6
if deit_name[9:].startswith('base' ):
pass
elif deit_name[4:].startswith('large' ):
snake_case__ : List[Any] = 1024
snake_case__ : str = 4096
snake_case__ : Tuple = 24
snake_case__ : Tuple = 16
# load original model from timm
snake_case__ : Optional[int] = timm.create_model(A__ , pretrained=A__ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case__ : Optional[Any] = timm_model.state_dict()
snake_case__ : Tuple = create_rename_keys(A__ , A__ )
for src, dest in rename_keys:
rename_key(A__ , A__ , A__ )
read_in_q_k_v(A__ , A__ , A__ )
# load HuggingFace model
snake_case__ : int = DeiTForImageClassificationWithTeacher(A__ ).eval()
model.load_state_dict(A__ )
# Check outputs on an image, prepared by DeiTImageProcessor
snake_case__ : Union[str, Any] = int(
(256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
snake_case__ : List[Any] = DeiTImageProcessor(size=A__ , crop_size=config.image_size )
snake_case__ : Tuple = image_processor(images=prepare_img() , return_tensors='pt' )
snake_case__ : Tuple = encoding['pixel_values']
snake_case__ : Dict = model(A__ )
snake_case__ : Union[str, Any] = timm_model(A__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(A__ , outputs.logits , atol=1e-3 )
Path(A__ ).mkdir(exist_ok=A__ )
print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(A__ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(A__ )
if __name__ == "__main__":
lowerCAmelCase__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--deit_name''',
default='''vit_deit_base_distilled_patch16_224''',
type=str,
help='''Name of the DeiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
lowerCAmelCase__ : int = parser.parse_args()
convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 143 | 1 |
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/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class _a ( UpperCamelCase__ , UpperCamelCase__ ):
_lowercase : Tuple = '''dinat'''
_lowercase : List[str] = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self: List[Any] , UpperCamelCase_: List[Any]=4 , UpperCamelCase_: Dict=3 , UpperCamelCase_: str=64 , UpperCamelCase_: Dict=[3, 4, 6, 5] , UpperCamelCase_: Optional[int]=[2, 4, 8, 16] , UpperCamelCase_: Tuple=7 , UpperCamelCase_: Dict=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] , UpperCamelCase_: List[Any]=3.0 , UpperCamelCase_: Dict=True , UpperCamelCase_: str=0.0 , UpperCamelCase_: Any=0.0 , UpperCamelCase_: int=0.1 , UpperCamelCase_: Optional[Any]="gelu" , UpperCamelCase_: Optional[Any]=0.02 , UpperCamelCase_: Optional[Any]=1E-5 , UpperCamelCase_: Tuple=0.0 , UpperCamelCase_: Optional[int]=None , UpperCamelCase_: List[str]=None , **UpperCamelCase_: int , ) -> List[str]:
"""simple docstring"""
super().__init__(**UpperCamelCase_ )
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = embed_dim
lowercase__ = depths
lowercase__ = len(UpperCamelCase_ )
lowercase__ = num_heads
lowercase__ = kernel_size
lowercase__ = dilations
lowercase__ = mlp_ratio
lowercase__ = qkv_bias
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = drop_path_rate
lowercase__ = hidden_act
lowercase__ = layer_norm_eps
lowercase__ = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowercase__ = int(embed_dim * 2 ** (len(UpperCamelCase_ ) - 1) )
lowercase__ = layer_scale_init_value
lowercase__ = ['''stem'''] + [f'stage{idx}' for idx in range(1 , len(UpperCamelCase_ ) + 1 )]
lowercase__ , lowercase__ = get_aligned_output_features_output_indices(
out_features=UpperCamelCase_ , out_indices=UpperCamelCase_ , stage_names=self.stage_names )
| 93 |
import logging
from transformers import PretrainedConfig
lowerCAmelCase = logging.getLogger(__name__)
lowerCAmelCase = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class _a ( UpperCamelCase__ ):
_lowercase : List[Any] = '''bertabs'''
def __init__( self: List[str] , UpperCamelCase_: Dict=30_522 , UpperCamelCase_: Union[str, Any]=512 , UpperCamelCase_: Optional[int]=6 , UpperCamelCase_: int=512 , UpperCamelCase_: Optional[int]=8 , UpperCamelCase_: List[Any]=512 , UpperCamelCase_: Tuple=0.2 , UpperCamelCase_: List[Any]=6 , UpperCamelCase_: Tuple=768 , UpperCamelCase_: List[Any]=8 , UpperCamelCase_: Union[str, Any]=2_048 , UpperCamelCase_: str=0.2 , **UpperCamelCase_: Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**UpperCamelCase_ )
lowercase__ = vocab_size
lowercase__ = max_pos
lowercase__ = enc_layers
lowercase__ = enc_hidden_size
lowercase__ = enc_heads
lowercase__ = enc_ff_size
lowercase__ = enc_dropout
lowercase__ = dec_layers
lowercase__ = dec_hidden_size
lowercase__ = dec_heads
lowercase__ = dec_ff_size
lowercase__ = dec_dropout
| 93 | 1 |
# 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
A__ : Union[str, Any] = float('''nan''')
class __snake_case :
def __init__( self : Tuple , A_ : Optional[Any]):
lowerCAmelCase_ : str = sys.stdout
lowerCAmelCase_ : Tuple = open(A_ , '''a''')
def __getattr__( self : Dict , A_ : Optional[int]):
return getattr(self.stdout , A_)
def UpperCAmelCase__ ( self : Any , A_ : Any):
self.stdout.write(A_)
# strip tqdm codes
self.file.write(re.sub(r'''^.*\r''' , '''''' , A_ , 0 , re.M))
def UpperCamelCase( __UpperCamelCase : str=80 ,__UpperCamelCase : int=False ):
lowerCAmelCase_ : int = []
# deal with critical env vars
lowerCAmelCase_ : Union[str, Any] = ['''CUDA_VISIBLE_DEVICES''']
for key in env_keys:
lowerCAmelCase_ : Dict = 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)
lowerCAmelCase_ : Tuple = 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
lowerCAmelCase_ : List[Any] = []
lowerCAmelCase_ : Dict = ''''''
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 )
lowerCAmelCase_ : int = ''''''
return "\\\n".join(__UpperCamelCase )
def UpperCamelCase( __UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Any ):
# unwrap multi-line input
lowerCAmelCase_ : Tuple = re.sub(R'''[\\\n]+''' ,''' ''' ,args.base_cmd )
# remove --output_dir if any and set our own
lowerCAmelCase_ : Optional[Any] = re.sub('''--output_dir\s+[^\s]+''' ,'''''' ,args.base_cmd )
args.base_cmd += f""" --output_dir {output_dir}"""
# ensure we have --overwrite_output_dir
lowerCAmelCase_ : Any = re.sub('''--overwrite_output_dir\s+''' ,'''''' ,args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def UpperCamelCase( __UpperCamelCase : str ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : int ,__UpperCamelCase : List[str] ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Optional[int] ):
# Enable to debug everything but the run itself, to do it fast and see the progress.
# This is useful for debugging the output formatting quickly - we can remove it later once
# everybody is happy with the output
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, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2] )} ,)
lowerCAmelCase_ : Any = subprocess.run(__UpperCamelCase ,capture_output=__UpperCamelCase ,text=__UpperCamelCase )
if verbose:
print('''STDOUT''' ,result.stdout )
print('''STDERR''' ,result.stderr )
# save the streams
lowerCAmelCase_ : Tuple = 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:
lowerCAmelCase_ : List[str] = 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 UpperCamelCase( __UpperCamelCase : Dict ,__UpperCamelCase : List[Any] ,__UpperCamelCase : Tuple ,__UpperCamelCase : Any ,__UpperCamelCase : str ,__UpperCamelCase : int ,__UpperCamelCase : Optional[Any] ,__UpperCamelCase : Tuple ,__UpperCamelCase : Optional[int] ,__UpperCamelCase : Tuple ,):
lowerCAmelCase_ : Any = []
lowerCAmelCase_ : int = []
lowerCAmelCase_ : List[Any] = f"""{id}: {variation:<{longest_variation_len}}"""
lowerCAmelCase_ : Tuple = f"""{preamble}: """
lowerCAmelCase_ : Optional[Any] = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(__UpperCamelCase ) ,desc=__UpperCamelCase ,leave=__UpperCamelCase ):
lowerCAmelCase_ : str = process_run_single(
__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
lowerCAmelCase_ : Tuple = single_run_metrics[target_metric_key]
if not math.isnan(__UpperCamelCase ):
metrics.append(__UpperCamelCase )
results.append(__UpperCamelCase )
outcome += "✓"
else:
outcome += "✘"
lowerCAmelCase_ : List[str] = f"""\33[2K\r{outcome}"""
if len(__UpperCamelCase ) > 0:
lowerCAmelCase_ : Dict = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
lowerCAmelCase_ : Tuple = round(mean_metrics[target_metric_key] ,2 )
lowerCAmelCase_ : Optional[int] = f"""{outcome} {mean_target}"""
if len(__UpperCamelCase ) > 1:
results_str += f""" {tuple(round(__UpperCamelCase ,2 ) for x in results )}"""
print(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = variation
return mean_metrics
else:
print(__UpperCamelCase )
return {variation_key: variation, target_metric_key: nan}
def UpperCamelCase( ):
lowerCAmelCase_ : Any = torch.cuda.get_device_properties(torch.device('''cuda''' ) )
return f"""
Datetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}
Software:
transformers: {transformers.__version__}
torch : {torch.__version__}
cuda : {torch.version.cuda}
python : {platform.python_version()}
Hardware:
{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB
"""
def UpperCamelCase( __UpperCamelCase : List[str] ,__UpperCamelCase : List[str] ,__UpperCamelCase : Any ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : List[Any] ):
lowerCAmelCase_ : Optional[Any] = pd.DataFrame(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = '''variation'''
lowerCAmelCase_ : List[str] = '''diff_%'''
lowerCAmelCase_ : str = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
lowerCAmelCase_ : List[str] = 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
lowerCAmelCase_ : Dict = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(__UpperCamelCase ):
lowerCAmelCase_ : Any = 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
lowerCAmelCase_ : List[Any] = [variation_key, target_metric_key, diff_key, *report_metric_keys]
lowerCAmelCase_ : Optional[Any] = df.reindex(__UpperCamelCase ,axis='''columns''' ) # reorder cols
# capitalize
lowerCAmelCase_ : Any = df.rename(str.capitalize ,axis='''columns''' )
# make the cols as narrow as possible
lowerCAmelCase_ : Optional[Any] = df.rename(lambda __UpperCamelCase : c.replace('''_''' ,'''<br>''' ) ,axis='''columns''' )
lowerCAmelCase_ : Union[str, Any] = df.rename(lambda __UpperCamelCase : c.replace('''_''' ,'''\n''' ) ,axis='''columns''' )
lowerCAmelCase_ : List[str] = ['''''', '''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 UpperCamelCase( ):
lowerCAmelCase_ : Any = 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''' ,)
lowerCAmelCase_ : List[Any] = parser.parse_args()
lowerCAmelCase_ : Union[str, Any] = args.output_dir
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
lowerCAmelCase_ : List[Any] = get_base_command(__UpperCamelCase ,__UpperCamelCase )
# split each dimension into its --foo variations
lowerCAmelCase_ : Dict = [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
lowerCAmelCase_ : Union[str, Any] = list(map(str.strip ,map(''' '''.join ,itertools.product(*__UpperCamelCase ) ) ) )
lowerCAmelCase_ : int = max(len(__UpperCamelCase ) for x in variations )
# split wanted keys
lowerCAmelCase_ : List[str] = args.report_metric_keys.split()
# capture prints into a log file for convenience
lowerCAmelCase_ : Optional[int] = 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}""" )
lowerCAmelCase_ : Optional[Any] = Tee(__UpperCamelCase )
print(f"""\n*** Running {len(__UpperCamelCase )} benchmarks:""" )
print(f"""Base command: {" ".join(__UpperCamelCase )}""" )
lowerCAmelCase_ : int = '''variation'''
lowerCAmelCase_ : List[Any] = []
for id, variation in enumerate(tqdm(__UpperCamelCase ,desc='''Total completion: ''' ,leave=__UpperCamelCase ) ):
lowerCAmelCase_ : int = 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()
| 103 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 1_000 ) -> int:
return sum(e for e in range(3 , _lowerCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 35 | 0 |
"""simple docstring"""
_A = 8.314_462 # Unit - J mol-1 K-1
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 360 |
"""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 lowercase_ ( __UpperCAmelCase ) -> str:
if isinstance(__UpperCAmelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class _lowerCamelCase :
def _lowerCAmelCase ( self : Dict , UpperCamelCase : List[Any] , UpperCamelCase : int ) -> int:
"""simple docstring"""
pass
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
def _lowerCAmelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
pass
def _lowerCAmelCase ( self : str , UpperCamelCase : Tuple , UpperCamelCase : Optional[Any] , UpperCamelCase : List[Any] , UpperCamelCase : Dict , UpperCamelCase : Any=None , **UpperCamelCase : Optional[Any] ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Dict = TFVisionTextDualEncoderModel(UpperCamelCase )
lowerCAmelCase__ : List[Any] = model(input_ids=UpperCamelCase , pixel_values=UpperCamelCase , attention_mask=UpperCamelCase )
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 _lowerCAmelCase ( self : int , UpperCamelCase : List[str] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Tuple , UpperCamelCase : Dict , UpperCamelCase : Any=None , **UpperCamelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self.get_vision_text_model(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = TFVisionTextDualEncoderModel(vision_model=UpperCamelCase , text_model=UpperCamelCase )
lowerCAmelCase__ : Optional[int] = model(input_ids=UpperCamelCase , pixel_values=UpperCamelCase , attention_mask=UpperCamelCase )
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 _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : Any , UpperCamelCase : str , UpperCamelCase : Tuple , UpperCamelCase : Union[str, Any] , UpperCamelCase : List[str]=None , **UpperCamelCase : Optional[Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : int = self.get_vision_text_model(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Dict = {"""vision_model""": vision_model, """text_model""": text_model}
lowerCAmelCase__ : Optional[int] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = model(input_ids=UpperCamelCase , pixel_values=UpperCamelCase , attention_mask=UpperCamelCase )
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 _lowerCAmelCase ( self : int , UpperCamelCase : List[str] , UpperCamelCase : Optional[Any] , UpperCamelCase : Any , UpperCamelCase : List[str] , UpperCamelCase : Tuple=None , **UpperCamelCase : Optional[Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self.get_vision_text_model(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = TFVisionTextDualEncoderModel(vision_model=UpperCamelCase , text_model=UpperCamelCase )
lowerCAmelCase__ : List[Any] = model(input_ids=UpperCamelCase , pixel_values=UpperCamelCase , attention_mask=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase )
lowerCAmelCase__ : str = TFVisionTextDualEncoderModel.from_pretrained(UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = model(input_ids=UpperCamelCase , pixel_values=UpperCamelCase , attention_mask=UpperCamelCase )
lowerCAmelCase__ : int = after_output[0].numpy()
lowerCAmelCase__ : Tuple = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(UpperCamelCase , 1E-5 )
def _lowerCAmelCase ( self : int , UpperCamelCase : Dict , UpperCamelCase : str , UpperCamelCase : Optional[int] , UpperCamelCase : List[str] , UpperCamelCase : List[Any]=None , **UpperCamelCase : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : str = self.get_vision_text_model(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : int = TFVisionTextDualEncoderModel(vision_model=UpperCamelCase , text_model=UpperCamelCase )
lowerCAmelCase__ : Dict = model(
input_ids=UpperCamelCase , pixel_values=UpperCamelCase , attention_mask=UpperCamelCase , output_attentions=UpperCamelCase )
lowerCAmelCase__ : Optional[int] = output.vision_model_output.attentions
self.assertEqual(len(UpperCamelCase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCAmelCase__ : Optional[int] = to_atuple(vision_model.config.image_size )
lowerCAmelCase__ : Any = to_atuple(vision_model.config.patch_size )
lowerCAmelCase__ : List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowerCAmelCase__ : Tuple = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowerCAmelCase__ : List[str] = output.text_model_output.attentions
self.assertEqual(len(UpperCamelCase ) , 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 _lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase : np.ndarray , UpperCamelCase : np.ndarray , UpperCamelCase : float ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = np.abs((a - b) ).max()
self.assertLessEqual(UpperCamelCase , UpperCamelCase , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def _lowerCAmelCase ( self : str ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**UpperCamelCase )
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
lowerCAmelCase__ : Any = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**UpperCamelCase )
def _lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase )
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
self.check_save_load(**UpperCamelCase )
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**UpperCamelCase )
@slow
def _lowerCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = self.get_pretrained_model_and_inputs()
lowerCAmelCase__ : Union[str, Any] = model_a(**UpperCamelCase )
lowerCAmelCase__ : Any = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(UpperCamelCase )
lowerCAmelCase__ : int = TFVisionTextDualEncoderModel.from_pretrained(UpperCamelCase )
lowerCAmelCase__ : List[str] = model_a(**UpperCamelCase )
lowerCAmelCase__ : Dict = after_outputs[0].numpy()
lowerCAmelCase__ : List[str] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(UpperCamelCase , 1E-5 )
@require_tf
class _lowerCamelCase ( a_ , unittest.TestCase ):
def _lowerCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Dict = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
lowerCAmelCase__ : Optional[Any] = 13
lowerCAmelCase__ : List[str] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowerCAmelCase__ : Dict = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowerCAmelCase__ : Optional[Any] = random_attention_mask([batch_size, 4] )
lowerCAmelCase__ : Dict = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _lowerCAmelCase ( self : Any , UpperCamelCase : Union[str, Any] , UpperCamelCase : int ) -> str:
"""simple docstring"""
lowerCAmelCase__ : str = TFViTModel(UpperCamelCase , name="""vision_model""" )
lowerCAmelCase__ : Any = TFBertModel(UpperCamelCase , name="""text_model""" )
return vision_model, text_model
def _lowerCAmelCase ( self : int ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : str = TFViTModelTester(self )
lowerCAmelCase__ : str = TFBertModelTester(self )
lowerCAmelCase__ : Optional[int] = vit_model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : List[Any] = bert_model_tester.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Dict = vision_config_and_inputs
(
(
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) ,
) : List[Any] = 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 _lowerCamelCase ( a_ , unittest.TestCase ):
def _lowerCAmelCase ( self : int ) -> str:
"""simple docstring"""
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
lowerCAmelCase__ : Union[str, Any] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
lowerCAmelCase__ : str = 13
lowerCAmelCase__ : Optional[int] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowerCAmelCase__ : List[Any] = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowerCAmelCase__ : Optional[Any] = random_attention_mask([batch_size, 4] )
lowerCAmelCase__ : List[Any] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _lowerCAmelCase ( self : Optional[Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : str , UpperCamelCase : str=None , **UpperCamelCase : Optional[Any] ) -> int:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = self.get_vision_text_model(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = TFVisionTextDualEncoderModel(vision_model=UpperCamelCase , text_model=UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = model(
input_ids=UpperCamelCase , pixel_values=UpperCamelCase , attention_mask=UpperCamelCase , output_attentions=UpperCamelCase )
lowerCAmelCase__ : Optional[int] = output.vision_model_output.attentions
self.assertEqual(len(UpperCamelCase ) , 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)
lowerCAmelCase__ : Dict = to_atuple(vision_model.config.image_size )
lowerCAmelCase__ : Any = to_atuple(vision_model.config.patch_size )
lowerCAmelCase__ : Dict = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
lowerCAmelCase__ : Optional[Any] = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
lowerCAmelCase__ : Union[str, Any] = output.text_model_output.attentions
self.assertEqual(len(UpperCamelCase ) , 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 _lowerCAmelCase ( self : int , UpperCamelCase : Any , UpperCamelCase : str ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : Any = TFDeiTModel(UpperCamelCase , name="""vision_model""" )
lowerCAmelCase__ : str = TFRobertaModel(UpperCamelCase , name="""text_model""" )
return vision_model, text_model
def _lowerCAmelCase ( self : List[str] ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = TFDeiTModelTester(self )
lowerCAmelCase__ : Union[str, Any] = TFRobertaModelTester(self )
lowerCAmelCase__ : Optional[int] = vit_model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : Any = bert_model_tester.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = vision_config_and_inputs
(
(
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) ,
) : str = 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 _lowerCamelCase ( a_ , unittest.TestCase ):
def _lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
lowerCAmelCase__ : Any = 13
lowerCAmelCase__ : List[str] = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
lowerCAmelCase__ : Optional[Any] = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
lowerCAmelCase__ : str = random_attention_mask([batch_size, 4] )
lowerCAmelCase__ : List[str] = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _lowerCAmelCase ( self : str , UpperCamelCase : str , UpperCamelCase : Optional[Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : int = TFCLIPVisionModel(UpperCamelCase , name="""vision_model""" )
lowerCAmelCase__ : List[str] = TFBertModel(UpperCamelCase , name="""text_model""" )
return vision_model, text_model
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : str = TFCLIPVisionModelTester(self )
lowerCAmelCase__ : int = TFBertModelTester(self )
lowerCAmelCase__ : str = clip_model_tester.prepare_config_and_inputs()
lowerCAmelCase__ : Optional[int] = bert_model_tester.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ : Dict = vision_config_and_inputs
(
(
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) ,
) : str = 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 _lowerCamelCase ( unittest.TestCase ):
@slow
def _lowerCAmelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=UpperCamelCase )
lowerCAmelCase__ : Any = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
lowerCAmelCase__ : Any = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCAmelCase__ : Tuple = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=UpperCamelCase , padding=UpperCamelCase , return_tensors="""np""" )
lowerCAmelCase__ : Tuple = model(**UpperCamelCase )
# 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]) , )
lowerCAmelCase__ : List[Any] = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , UpperCamelCase , atol=1E-3 ) )
| 212 | 0 |
def a ( lowerCamelCase_ ):
'''simple docstring'''
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError('''only integers accepted as input''' )
else:
lowercase__ = str(abs(lowerCamelCase_ ) )
lowercase__ = [list(lowerCamelCase_ ) for char in range(len(lowerCamelCase_ ) )]
for index in range(len(lowerCamelCase_ ) ):
num_transpositions[index].pop(lowerCamelCase_ )
return max(
int(''''''.join(list(lowerCamelCase_ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__('doctest').testmod()
| 207 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
A__ : Tuple = logging.get_logger(__name__)
A__ : int = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
'constant': get_constant_schedule,
'constant_w_warmup': get_constant_schedule_with_warmup,
}
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : int=None, lowerCamelCase : int=None, *lowerCamelCase : List[Any], **lowerCamelCase : Any ):
'''simple docstring'''
super().__init__(*lowerCamelCase, **lowerCamelCase )
if config is None:
assert isinstance(self.model, lowerCamelCase ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
lowercase__ = self.model.config
else:
lowercase__ = config
lowercase__ = data_args
lowercase__ = self.config.tgt_vocab_size if isinstance(self.config, lowerCamelCase ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
''' padding..''' )
if self.args.label_smoothing == 0:
lowercase__ = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowercase__ = label_smoothed_nll_loss
def lowercase__ ( self : List[Any], lowerCamelCase : int ):
'''simple docstring'''
if self.optimizer is None:
lowercase__ = ['''bias''', '''LayerNorm.weight''']
lowercase__ = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
lowercase__ = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowercase__ = Adafactor
lowercase__ = {'''scale_parameter''': False, '''relative_step''': False}
else:
lowercase__ = AdamW
lowercase__ = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
lowercase__ = self.args.learning_rate
if self.sharded_ddp:
lowercase__ = OSS(
params=lowerCamelCase, optim=lowerCamelCase, **lowerCamelCase, )
else:
lowercase__ = optimizer_cls(lowerCamelCase, **lowerCamelCase )
if self.lr_scheduler is None:
lowercase__ = self._get_lr_scheduler(lowerCamelCase )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def lowercase__ ( self : List[str], lowerCamelCase : Optional[int] ):
'''simple docstring'''
lowercase__ = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowercase__ = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowercase__ = schedule_func(self.optimizer, num_warmup_steps=self.args.warmup_steps )
else:
lowercase__ = schedule_func(
self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=lowerCamelCase )
return scheduler
def lowercase__ ( self : List[Any] ):
'''simple docstring'''
if isinstance(self.train_dataset, torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size, distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED), )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def lowercase__ ( self : Any, lowerCamelCase : List[Any], lowerCamelCase : str, lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowercase__ = model(**lowerCamelCase, use_cache=lowerCamelCase )[0]
lowercase__ = self.loss_fn(logits.view(-1, logits.shape[-1] ), labels.view(-1 ) )
else:
# compute usual loss via models
lowercase__ , lowercase__ = model(**lowerCamelCase, labels=lowerCamelCase, use_cache=lowerCamelCase )[:2]
else:
# compute label smoothed loss
lowercase__ = model(**lowerCamelCase, use_cache=lowerCamelCase )[0]
lowercase__ = torch.nn.functional.log_softmax(lowerCamelCase, dim=-1 )
lowercase__ , lowercase__ = self.loss_fn(lowerCamelCase, lowerCamelCase, self.args.label_smoothing, ignore_index=self.config.pad_token_id )
return loss, logits
def lowercase__ ( self : List[str], lowerCamelCase : Optional[Any], lowerCamelCase : Tuple ):
'''simple docstring'''
lowercase__ = inputs.pop('''labels''' )
lowercase__ , lowercase__ = self._compute_loss(lowerCamelCase, lowerCamelCase, lowerCamelCase )
return loss
def lowercase__ ( self : str, lowerCamelCase : nn.Module, lowerCamelCase : Dict[str, Union[torch.Tensor, Any]], lowerCamelCase : bool, lowerCamelCase : Optional[List[str]] = None, ):
'''simple docstring'''
lowercase__ = self._prepare_inputs(lowerCamelCase )
lowercase__ = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowercase__ = self.model.generate(
inputs['''input_ids'''], attention_mask=inputs['''attention_mask'''], **lowerCamelCase, )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowercase__ = self._pad_tensors_to_max_len(lowerCamelCase, gen_kwargs['''max_length'''] )
lowercase__ = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
lowercase__ , lowercase__ = self._compute_loss(lowerCamelCase, lowerCamelCase, lowerCamelCase )
lowercase__ = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowercase__ = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowercase__ = self._pad_tensors_to_max_len(lowerCamelCase, gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def lowercase__ ( self : List[Any], lowerCamelCase : str, lowerCamelCase : Any ):
'''simple docstring'''
# If PAD token is not defined at least EOS token has to be defined
lowercase__ = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
F""" padded to `max_length`={max_length}""" )
lowercase__ = pad_token_id * torch.ones(
(tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device )
lowercase__ = tensor
return padded_tensor
| 207 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Optional[int] = s.rsplit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return new.join(SCREAMING_SNAKE_CASE )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : Optional[Any] = {}
A_ : Any = ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
A_ : Union[str, Any] = key.replace(f'''{group_key}.''' , f'''{group_key}.group.''' )
if "res_path" in key:
A_ : Union[str, Any] = key.replace('''res_path.''' , '''res_path.path.''' )
if key.endswith('''.w''' ):
A_ : int = rreplace(SCREAMING_SNAKE_CASE , '''.w''' , '''.weight''' , 1 )
if key.endswith('''.b''' ):
A_ : Optional[int] = rreplace(SCREAMING_SNAKE_CASE , '''.b''' , '''.bias''' , 1 )
A_ : Optional[int] = value.float()
return upgrade
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True ):
from dall_e import Encoder
A_ : Optional[int] = Encoder()
if os.path.exists(SCREAMING_SNAKE_CASE ):
A_ : Optional[Any] = torch.load(SCREAMING_SNAKE_CASE )
else:
A_ : List[str] = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE )
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Optional[int] = ckpt.state_dict()
encoder.load_state_dict(SCREAMING_SNAKE_CASE )
if config_path is not None:
A_ : List[Any] = FlavaImageCodebookConfig.from_pretrained(SCREAMING_SNAKE_CASE )
else:
A_ : str = FlavaImageCodebookConfig()
A_ : Optional[int] = FlavaImageCodebook(SCREAMING_SNAKE_CASE ).eval()
A_ : Dict = encoder.state_dict()
A_ : Union[str, Any] = upgrade_state_dict(SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(SCREAMING_SNAKE_CASE )
A_ : Optional[int] = hf_model.state_dict()
A_ : Tuple = count_parameters(SCREAMING_SNAKE_CASE )
A_ : List[str] = count_parameters(SCREAMING_SNAKE_CASE )
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1e-3 )
if save_checkpoint:
hf_model.save_pretrained(SCREAMING_SNAKE_CASE )
else:
return hf_state_dict
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
UpperCamelCase = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 359 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if len(SCREAMING_SNAKE_CASE ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE )]
A_ : Any = []
def generate(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Dict = [0] * n
res.append(tuple(SCREAMING_SNAKE_CASE ) )
A_ : int = 0
while i < n:
if c[i] < i:
if i % 2 == 0:
A_ , A_ : Optional[int] = arr[i], arr[0]
else:
A_ , A_ : Tuple = arr[i], arr[c[i]]
res.append(tuple(SCREAMING_SNAKE_CASE ) )
c[i] += 1
A_ : Optional[int] = 0
else:
A_ : str = 0
i += 1
generate(len(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
return res
if __name__ == "__main__":
UpperCamelCase = input("""Enter numbers separated by a comma:\n""").strip()
UpperCamelCase = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 65 | 0 |
'''simple docstring'''
import os
import string
import sys
__lowercase = 1 << 8
__lowercase = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 2_7,
'''up''': 6_5 + ARROW_KEY_FLAG,
'''down''': 6_6 + ARROW_KEY_FLAG,
'''right''': 6_7 + ARROW_KEY_FLAG,
'''left''': 6_8 + ARROW_KEY_FLAG,
'''mod_int''': 9_1,
'''undefined''': sys.maxsize,
'''interrupt''': 3,
'''insert''': 5_0,
'''delete''': 5_1,
'''pg_up''': 5_3,
'''pg_down''': 5_4,
}
__lowercase = KEYMAP['''up''']
__lowercase = KEYMAP['''left''']
if sys.platform == "win32":
__lowercase = []
__lowercase = {
B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
}
for i in range(1_0):
__lowercase = ord(str(i))
def snake_case__ ( ) -> List[Any]:
'''simple docstring'''
if os.name == "nt":
import msvcrt
lowerCAmelCase = """mbcs"""
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_A ) == 0:
# Read the keystroke
lowerCAmelCase = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
lowerCAmelCase = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
lowerCAmelCase = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
WIN_CH_BUFFER.append(_A )
if ord(_A ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
lowerCAmelCase = chr(KEYMAP["""esc"""] )
except KeyError:
lowerCAmelCase = cha[1]
else:
lowerCAmelCase = ch.decode(_A )
else:
lowerCAmelCase = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
lowerCAmelCase = sys.stdin.fileno()
lowerCAmelCase = termios.tcgetattr(_A )
try:
tty.setraw(_A )
lowerCAmelCase = sys.stdin.read(1 )
finally:
termios.tcsetattr(_A , termios.TCSADRAIN , _A )
return ch
def snake_case__ ( ) -> Tuple:
'''simple docstring'''
lowerCAmelCase = get_raw_chars()
if ord(_A ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_A ) == KEYMAP["esc"]:
lowerCAmelCase = get_raw_chars()
if ord(_A ) == KEYMAP["mod_int"]:
lowerCAmelCase = get_raw_chars()
if ord(_A ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_A ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_A ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 272 | '''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = '''ernie_m'''
UpperCAmelCase_ : Dict[str, str] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self , __lowerCAmelCase = 250002 , __lowerCAmelCase = 768 , __lowerCAmelCase = 12 , __lowerCAmelCase = 12 , __lowerCAmelCase = 3072 , __lowerCAmelCase = "gelu" , __lowerCAmelCase = 0.1 , __lowerCAmelCase = 0.1 , __lowerCAmelCase = 514 , __lowerCAmelCase = 0.02 , __lowerCAmelCase = 1 , __lowerCAmelCase = 1E-0_5 , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=0.0 , **__lowerCAmelCase , ):
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase)
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = classifier_dropout
lowerCAmelCase = is_decoder
lowerCAmelCase = act_dropout
| 272 | 1 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a__ : Union[str, Any] = datasets.load_iris()
a__ : Any = np.array(data['''data'''])
a__ : Dict = np.array(data['''target'''])
a__ : Optional[int] = data['''target_names''']
a__ , a__ , a__ , a__ : Dict = train_test_split(X, y)
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return np.linalg.norm(np.array(lowerCAmelCase_ ) - np.array(lowerCAmelCase_ ) )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=5 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = zip(lowerCAmelCase_ , lowerCAmelCase_ )
# List of distances of all points from the point to be classified
__SCREAMING_SNAKE_CASE = []
for data_point in data:
__SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , lowerCAmelCase_ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
__SCREAMING_SNAKE_CASE = [i[1] for i in sorted(lowerCAmelCase_ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
__SCREAMING_SNAKE_CASE = Counter(lowerCAmelCase_ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 195 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def __init__( self : Optional[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Tuple=1_3 , UpperCAmelCase__ : Optional[int]=3_0 , UpperCAmelCase__ : Any=2 , UpperCAmelCase__ : List[str]=3 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : int=3_2 , UpperCAmelCase__ : Any=5 , UpperCAmelCase__ : Union[str, Any]=4 , UpperCAmelCase__ : List[str]=3_7 , UpperCAmelCase__ : Tuple="gelu" , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : str=0.1 , UpperCAmelCase__ : Any=1_0 , UpperCAmelCase__ : str=0.02 , ) -> Tuple:
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = type_sequence_label_size
__SCREAMING_SNAKE_CASE = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2
__SCREAMING_SNAKE_CASE = num_patches + 1
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , )
return config, pixel_values
def UpperCAmelCase_ ( self : Tuple , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
__SCREAMING_SNAKE_CASE = FlaxViTModel(config=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
__SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
__SCREAMING_SNAKE_CASE = (self.patch_size, self.patch_size)
__SCREAMING_SNAKE_CASE = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def UpperCAmelCase_ ( self : str , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Tuple ) -> Tuple:
__SCREAMING_SNAKE_CASE = self.type_sequence_label_size
__SCREAMING_SNAKE_CASE = FlaxViTForImageClassification(config=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = FlaxViTForImageClassification(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]:
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
__SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class UpperCamelCase_ ( UpperCamelCase , unittest.TestCase):
"""simple docstring"""
snake_case__ : List[str] = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def UpperCAmelCase_ ( self : int ) -> None:
__SCREAMING_SNAKE_CASE = FlaxViTModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=3_7 )
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Dict ) -> Dict:
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> Any:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Any ) -> List[Any]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__SCREAMING_SNAKE_CASE = self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = model_class(UpperCAmelCase__ )
@jax.jit
def model_jitted(UpperCAmelCase__ : Optional[int] , **UpperCAmelCase__ : Any ):
return model(pixel_values=UpperCAmelCase__ , **UpperCAmelCase__ )
with self.subTest("JIT Enabled" ):
__SCREAMING_SNAKE_CASE = model_jitted(**UpperCAmelCase__ ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
__SCREAMING_SNAKE_CASE = model_jitted(**UpperCAmelCase__ ).to_tuple()
self.assertEqual(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
for jitted_output, output in zip(UpperCAmelCase__ , UpperCAmelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
for model_class_name in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class_name.from_pretrained("google/vit-base-patch16-224" )
__SCREAMING_SNAKE_CASE = model(np.ones((1, 3, 2_2_4, 2_2_4) ) )
self.assertIsNotNone(UpperCAmelCase__ )
| 195 | 1 |
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