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
|
|---|---|---|---|---|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def a__ ( __UpperCamelCase , __UpperCamelCase = "cpu" , __UpperCamelCase = None ):
SCREAMING_SNAKE_CASE_ = torch.load(__a , map_location=__a )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__a , torch.Tensor ):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" )
SCREAMING_SNAKE_CASE_ = v.half()
if save_path is None: # overwrite src_path
SCREAMING_SNAKE_CASE_ = src_path
torch.save(__a , __a )
if __name__ == "__main__":
fire.Fire(convert)
| 350
|
from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to be of square shaped array but got a "
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
SCREAMING_SNAKE_CASE_ = (table[i][j] - total) / upper[j][j]
SCREAMING_SNAKE_CASE_ = 1
for j in range(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : Optional[int] = {
"facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}
class lowerCamelCase (_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase__ = '''data2vec-text'''
def __init__( self : Optional[int] , __magic_name__ : Optional[Any]=30_522 , __magic_name__ : Union[str, Any]=768 , __magic_name__ : str=12 , __magic_name__ : Any=12 , __magic_name__ : str=3_072 , __magic_name__ : Any="gelu" , __magic_name__ : Dict=0.1 , __magic_name__ : Tuple=0.1 , __magic_name__ : List[Any]=512 , __magic_name__ : Any=2 , __magic_name__ : Optional[Any]=0.02 , __magic_name__ : Optional[int]=1e-12 , __magic_name__ : int=1 , __magic_name__ : int=0 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Dict="absolute" , __magic_name__ : int=True , __magic_name__ : int=None , **__magic_name__ : str , ) -> Union[str, Any]:
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = classifier_dropout
class lowerCamelCase (_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@property
def __A ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE_ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 351
|
from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values" )
return 4 * pi * radius**2
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values" )
return 3 * pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values" )
SCREAMING_SNAKE_CASE_ = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values" )
return 2 * pi * radius * (height + radius)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values" )
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori" )
return 4 * pow(__UpperCamelCase , 2 ) * torus_radius * tube_radius
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values" )
return length * width
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values" )
return side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values" )
return (base * height) / 2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle" )
SCREAMING_SNAKE_CASE_ = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE_ = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values" )
return base * height
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values" )
return 1 / 2 * (basea + basea) * height
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values" )
return pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values" )
return pi * radius_x * radius_y
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values" )
return 1 / 2 * diagonal_a * diagonal_a
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides" )
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 305
| 0
|
def a__ ( __UpperCamelCase ):
if len(lowerCAmelCase__ ) <= 1:
return [tuple(lowerCAmelCase__ )]
SCREAMING_SNAKE_CASE_ = []
def generate(__UpperCamelCase , __UpperCamelCase ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCAmelCase__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
SCREAMING_SNAKE_CASE_ = arr[k - 1], arr[i]
else: # k is odd
SCREAMING_SNAKE_CASE_ = arr[k - 1], arr[0]
generate(k - 1 , lowerCAmelCase__ )
generate(len(lowerCAmelCase__ ) , lowerCAmelCase__ )
return res
if __name__ == "__main__":
A : Dict = input("Enter numbers separated by a comma:\n").strip()
A : Optional[Any] = [int(item) for item in user_input.split(",")]
print(heaps(arr))
| 352
|
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
A : List[str] = logging.get_logger(__name__)
A : int = {
"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 lowerCamelCase (SCREAMING_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 : Dict , __magic_name__ : Dict=50_265 , __magic_name__ : str=512 , __magic_name__ : List[Any]=8 , __magic_name__ : Any=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Any=8 , __magic_name__ : Optional[int]=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Tuple=0.0 , __magic_name__ : Dict=0.0 , __magic_name__ : Optional[int]=True , __magic_name__ : Any=True , __magic_name__ : Dict="gelu" , __magic_name__ : Tuple=512 , __magic_name__ : List[str]=0.1 , __magic_name__ : List[Any]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : Union[str, Any]=1 , __magic_name__ : List[Any]=False , __magic_name__ : str=0 , __magic_name__ : Dict=1 , __magic_name__ : str=2 , __magic_name__ : Union[str, Any]=2 , **__magic_name__ : Optional[Any] , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , is_encoder_decoder=__magic_name__ , decoder_start_token_id=__magic_name__ , forced_eos_token_id=__magic_name__ , **__magic_name__ , )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def __A ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ = {0: "batch"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = 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 __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super().outputs
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __A ( self : int , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# Generate decoder inputs
SCREAMING_SNAKE_CASE_ = seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE_ = dict(**__magic_name__ , **__magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
SCREAMING_SNAKE_CASE_ = common_inputs["decoder_input_ids"].shape[1]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = decoder_seq_length + 3
SCREAMING_SNAKE_CASE_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(__magic_name__ , __magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ = min(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = max(__magic_name__ , __magic_name__ ) - min_num_layers
SCREAMING_SNAKE_CASE_ = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(__magic_name__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE_ = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(__magic_name__ , __magic_name__ ):
common_inputs["past_key_values"].append((torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) )
return common_inputs
def __A ( self : Union[str, Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE_ = seqlen + 2
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = common_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["attention_mask"], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(__magic_name__ )
]
return common_inputs
def __A ( self : Dict , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , 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
SCREAMING_SNAKE_CASE_ = tokenizer.num_special_tokens_to_add(__magic_name__ )
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__magic_name__ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE_ = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE_ = dict(tokenizer(__magic_name__ , return_tensors=__magic_name__ ) )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
elif self.task == "causal-lm":
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_causal_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : List[Any] , __magic_name__ : str , __magic_name__ : List[str] ) -> List[str]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super()._flatten_past_key_values_(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self )._flatten_past_key_values_(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
| 305
| 0
|
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
A : str = {
"""/attention/""": """/0/SelfAttention/""",
"""/self_attention/""": """/0/SelfAttention/""",
"""/encoder_decoder_attention/""": """/1/EncDecAttention/""",
"""value""": """v""",
"""query""": """q""",
"""key""": """k""",
"""out""": """o""",
"""pre_self_attention_layer_norm""": """0/layer_norm""",
"""pre_cross_attention_layer_norm""": """1/layer_norm""",
"""pre_attention_layer_norm""": """0/layer_norm""", # previously 1, but seems wrong
"""token_embedder""": """shared""",
"""encoder_norm""": """final_layer_norm""",
"""decoder_norm""": """final_layer_norm""",
"""relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
"""router/router_weights/w/""": """router/classifier/""",
"""roer/roer_weights/w/""": """router/classifier/""",
"""logits_dense""": """lm_head""",
}
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = list(s_dict.keys() )
for key in keys:
SCREAMING_SNAKE_CASE_ = r".*/layers_(\d+)"
SCREAMING_SNAKE_CASE_ = key
if re.match(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = re.sub(r"layers_(\d+)" , r"block/\1/layer" , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = r"(encoder|decoder)\/"
if re.match(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = re.match(__UpperCamelCase , __UpperCamelCase ).groups()
if groups[0] == "encoder":
SCREAMING_SNAKE_CASE_ = re.sub(r"/mlp/" , r"/1/mlp/" , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = re.sub(r"/pre_mlp_layer_norm/" , r"/1/layer_norm/" , __UpperCamelCase )
elif groups[0] == "decoder":
SCREAMING_SNAKE_CASE_ = re.sub(r"/mlp/" , r"/2/mlp/" , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = re.sub(r"/pre_mlp_layer_norm/" , r"/2/layer_norm/" , __UpperCamelCase )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
SCREAMING_SNAKE_CASE_ = new_key.replace(__UpperCamelCase , __UpperCamelCase )
print(F'''{key} -> {new_key}''' )
SCREAMING_SNAKE_CASE_ = s_dict.pop(__UpperCamelCase )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
SCREAMING_SNAKE_CASE_ = s_dict[
"encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
SCREAMING_SNAKE_CASE_ = s_dict[
"decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
SCREAMING_SNAKE_CASE_ = s_dict[key].shape[0]
SCREAMING_SNAKE_CASE_ = s_dict[key]
for idx in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = expert_weihts[idx]
print(F'''{key} -> {key.replace("expert/" , "nested fstring" )}''' )
s_dict.pop(__UpperCamelCase )
return s_dict
A : Optional[Any] = {
"""NUM_ENCODER_LAYERS""": """num_layers""",
"""NUM_DECODER_LAYERS""": """num_decoder_layers""",
"""NUM_HEADS""": """num_heads""",
"""HEAD_DIM""": """d_kv""",
"""EMBED_DIM""": """d_model""",
"""MLP_DIM""": """d_ff""",
"""NUM_SELECTED_EXPERTS""": """num_selected_experts""",
"""NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
"""NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
"""dense.MlpBlock.activations""": """feed_forward_proj""",
}
def a__ ( __UpperCamelCase , __UpperCamelCase ):
import regex as re
with open(__UpperCamelCase , "r" ) as f:
SCREAMING_SNAKE_CASE_ = f.read()
SCREAMING_SNAKE_CASE_ = re.findall(r"(.*) = ([0-9.]*)" , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
SCREAMING_SNAKE_CASE_ = float(__UpperCamelCase ) if "." in value else int(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = re.findall(r"(.*activations) = \(\'(.*)\',\)" , __UpperCamelCase )[0]
SCREAMING_SNAKE_CASE_ = str(activation[1] )
SCREAMING_SNAKE_CASE_ = num_experts
SCREAMING_SNAKE_CASE_ = SwitchTransformersConfig(**__UpperCamelCase )
return config
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase="./" , __UpperCamelCase=8 ):
print(F'''Loading flax weights from : {flax_checkpoint_path}''' )
SCREAMING_SNAKE_CASE_ = checkpoints.load_tax_checkpoint(__UpperCamelCase )
if gin_file is not None:
SCREAMING_SNAKE_CASE_ = convert_gin_to_config(__UpperCamelCase , __UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = SwitchTransformersConfig.from_pretrained(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = SwitchTransformersForConditionalGeneration(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = flax_params["target"]
SCREAMING_SNAKE_CASE_ = flatten_dict(__UpperCamelCase , sep="/" )
SCREAMING_SNAKE_CASE_ = rename_keys(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = unflatten_dict(__UpperCamelCase , sep="/" )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__UpperCamelCase , __UpperCamelCase )
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
pt_model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
A : str = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 353
|
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , __magic_name__ : List[str] , __magic_name__ : int=100 , __magic_name__ : Optional[Any]=13 , __magic_name__ : Dict=30 , __magic_name__ : Tuple=2 , __magic_name__ : str=3 , __magic_name__ : str=True , __magic_name__ : Optional[int]=True , __magic_name__ : Union[str, Any]=32 , __magic_name__ : Optional[int]=4 , __magic_name__ : Dict=4 , __magic_name__ : Tuple=37 , __magic_name__ : Any="gelu" , __magic_name__ : int=0.1 , __magic_name__ : List[str]=0.1 , __magic_name__ : Optional[int]=10 , __magic_name__ : Tuple=0.02 , __magic_name__ : Optional[int]=3 , __magic_name__ : List[str]=None , __magic_name__ : Tuple=[0, 1, 2, 3] , ) -> List[str]:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = 100
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
SCREAMING_SNAKE_CASE_ = scope
SCREAMING_SNAKE_CASE_ = out_indices
SCREAMING_SNAKE_CASE_ = num_labels
# in BeiT, 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 __A ( self : Any ) -> int:
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __A ( self : Dict ) -> Optional[int]:
return BeitConfig(
vocab_size=self.vocab_size , 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=__magic_name__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def __A ( self : Optional[int] , __magic_name__ : List[str] , __magic_name__ : Optional[int] , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = BeitModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : str ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __A ( self : Dict , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : Tuple , __magic_name__ : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = self.type_sequence_label_size
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self : Tuple , __magic_name__ : Any , __magic_name__ : Optional[Any] , __magic_name__ : Tuple , __magic_name__ : int ) -> int:
SCREAMING_SNAKE_CASE_ = self.num_labels
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def __A ( self : str ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = config_and_inputs
SCREAMING_SNAKE_CASE_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = BeitModelTester(self )
SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def __A ( self : Dict ) -> List[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="BEiT does not use inputs_embeds" )
def __A ( self : List[str] ) -> Optional[Any]:
pass
@require_torch_multi_gpu
@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def __A ( self : str ) -> List[str]:
pass
def __A ( self : List[Any] ) -> List[str]:
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(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def __A ( self : Union[str, Any] ) -> int:
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(__magic_name__ )
SCREAMING_SNAKE_CASE_ = inspect.signature(model.forward )
# 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] , __magic_name__ )
def __A ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__magic_name__ )
def __A ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def __A ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__magic_name__ )
def __A ( self : int ) -> Optional[int]:
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : Any ) -> Tuple:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.gradient_checkpointing_enable()
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = _config_zero_init(__magic_name__ )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ = model_class(config=__magic_name__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def __A ( self : int ) -> Optional[int]:
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ = BeitModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self : List[Any] ) -> str:
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def __A ( self : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).pixel_values.to(__magic_name__ )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE_ = torch.ones((1, 196) , dtype=torch.bool ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(pixel_values=__magic_name__ , bool_masked_pos=__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , __magic_name__ , atol=1e-2 ) )
@slow
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 1_000) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([-1.2385, -1.0987, -1.0108] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 281
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to(
__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 21_841) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([1.6881, -0.2787, 0.5901] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 2_396
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Tuple ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = version.parse(PIL.__version__ ) < version.parse("9.0.0" )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=__magic_name__ , )
else:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=__magic_name__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __magic_name__ , atol=1e-4 ) )
@slow
def __A ( self : List[str] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ , target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE_ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
| 305
| 0
|
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
A : Any = logging.getLogger()
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "all_results.json" )
if os.path.exists(__UpperCamelCase ):
with open(__UpperCamelCase , "r" ) as f:
SCREAMING_SNAKE_CASE_ = json.load(__UpperCamelCase )
else:
raise ValueError(F'''can\'t find {path}''' )
return results
A : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class lowerCamelCase (lowercase_ ):
"""simple docstring"""
def __A ( self : Tuple ) -> Tuple:
import xla_spawn
SCREAMING_SNAKE_CASE_ = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE_ = F'''\n ./examples/pytorch/text-classification/run_glue.py\n --num_cores=8\n ./examples/pytorch/text-classification/run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --do_train\n --do_eval\n --debug tpu_metrics_debug\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --max_steps=10\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n '''.split()
with patch.object(a__ , "argv" , a__ ):
SCREAMING_SNAKE_CASE_ = time()
xla_spawn.main()
SCREAMING_SNAKE_CASE_ = time()
SCREAMING_SNAKE_CASE_ = get_results(a__ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.75 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def __A ( self : Union[str, Any] ) -> Optional[Any]:
import xla_spawn
SCREAMING_SNAKE_CASE_ = "\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n ".split()
with patch.object(a__ , "argv" , a__ ):
xla_spawn.main()
| 354
|
from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = self._trie
for char in text:
if char not in trie:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = trie[char]
SCREAMING_SNAKE_CASE_ = True
def __A ( self : Union[str, Any] , __magic_name__ : str ) -> tuple | list:
SCREAMING_SNAKE_CASE_ = self._trie
for char in prefix:
if char in trie:
SCREAMING_SNAKE_CASE_ = trie[char]
else:
return []
return self._elements(__magic_name__ )
def __A ( self : int , __magic_name__ : dict ) -> tuple:
SCREAMING_SNAKE_CASE_ = []
for c, v in d.items():
SCREAMING_SNAKE_CASE_ = [" "] if c == END else [(c + s) for s in self._elements(__magic_name__ )]
result.extend(__magic_name__ )
return tuple(__magic_name__ )
A : Union[str, Any] = Trie()
A : Optional[int] = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
trie.insert_word(word)
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = trie.find_word(__UpperCamelCase )
return tuple(string + word for word in suffixes )
def a__ ( ):
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 305
| 0
|
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) + 1
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE_ = [[0 for i in range(__UpperCamelCase )] for j in range(__UpperCamelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE_ = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = dp[0][j - 2] if pattern[j - 1] == "*" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __UpperCamelCase ):
for j in range(1 , __UpperCamelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE_ = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE_ = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE_ = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE_ = 0
else:
SCREAMING_SNAKE_CASE_ = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
A : Any = "aab"
A : Optional[int] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f"{input_string} matches the given pattern {pattern}")
else:
print(f"{input_string} does not match with the given pattern {pattern}")
| 355
|
from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name # process name
SCREAMING_SNAKE_CASE_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
SCREAMING_SNAKE_CASE_ = arrival_time
SCREAMING_SNAKE_CASE_ = burst_time # remaining burst time
SCREAMING_SNAKE_CASE_ = 0 # total time of the process wait in ready queue
SCREAMING_SNAKE_CASE_ = 0 # time from arrival time to completion time
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Tuple , __magic_name__ : int , __magic_name__ : list[int] , __magic_name__ : deque[Process] , __magic_name__ : int , ) -> None:
# total number of mlfq's queues
SCREAMING_SNAKE_CASE_ = number_of_queues
# time slice of queues that round robin algorithm applied
SCREAMING_SNAKE_CASE_ = time_slices
# unfinished process is in this ready_queue
SCREAMING_SNAKE_CASE_ = queue
# current time
SCREAMING_SNAKE_CASE_ = current_time
# finished process is in this sequence queue
SCREAMING_SNAKE_CASE_ = deque()
def __A ( self : Dict ) -> list[str]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def __A ( self : Tuple , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def __A ( self : str , __magic_name__ : deque[Process] ) -> list[int]:
return [q.burst_time for q in queue]
def __A ( self : Optional[Any] , __magic_name__ : Process ) -> int:
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def __A ( self : Optional[Any] , __magic_name__ : deque[Process] ) -> deque[Process]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of finished process
while len(__magic_name__ ) != 0:
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(__magic_name__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
SCREAMING_SNAKE_CASE_ = 0
# set the process's turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# set the completion time
SCREAMING_SNAKE_CASE_ = self.current_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def __A ( self : Any , __magic_name__ : deque[Process] , __magic_name__ : int ) -> tuple[deque[Process], deque[Process]]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(__magic_name__ ) ):
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(__magic_name__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
SCREAMING_SNAKE_CASE_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(__magic_name__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
SCREAMING_SNAKE_CASE_ = 0
# set the finish time
SCREAMING_SNAKE_CASE_ = self.current_time
# update the process' turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def __A ( self : Any ) -> deque[Process]:
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
A : Dict = Process("P1", 0, 53)
A : str = Process("P2", 0, 17)
A : List[Any] = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Dict = 3
A : Any = [17, 25]
A : Dict = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
A : Union[str, Any] = Process("P1", 0, 53)
A : Any = Process("P2", 0, 17)
A : Dict = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Optional[int] = 3
A : int = [17, 25]
A : Union[str, Any] = deque([Pa, Pa, Pa, Pa])
A : Tuple = MLFQ(number_of_queues, time_slices, queue, 0)
A : Tuple = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| 305
| 0
|
"""simple docstring"""
import torch
from torch import nn
class lowerCamelCase (nn.Module ):
"""simple docstring"""
def __init__( self : Dict , __magic_name__ : Dict , __magic_name__ : Any , __magic_name__ : Union[str, Any] , __magic_name__ : List[Any] , __magic_name__ : Optional[int]=1 , __magic_name__ : int=False ) -> int:
super().__init__()
SCREAMING_SNAKE_CASE_ = n_token
SCREAMING_SNAKE_CASE_ = d_embed
SCREAMING_SNAKE_CASE_ = d_proj
SCREAMING_SNAKE_CASE_ = cutoffs + [n_token]
SCREAMING_SNAKE_CASE_ = [0] + self.cutoffs
SCREAMING_SNAKE_CASE_ = div_val
SCREAMING_SNAKE_CASE_ = self.cutoffs[0]
SCREAMING_SNAKE_CASE_ = len(self.cutoffs ) - 1
SCREAMING_SNAKE_CASE_ = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
SCREAMING_SNAKE_CASE_ = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
SCREAMING_SNAKE_CASE_ = nn.Parameter(torch.zeros(self.n_clusters ) )
SCREAMING_SNAKE_CASE_ = nn.ModuleList()
SCREAMING_SNAKE_CASE_ = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(__lowerCamelCase , __lowerCamelCase ) ) )
else:
self.out_projs.append(__lowerCamelCase )
self.out_layers.append(nn.Linear(__lowerCamelCase , __lowerCamelCase ) )
else:
for i in range(len(self.cutoffs ) ):
SCREAMING_SNAKE_CASE_ = self.cutoff_ends[i], self.cutoff_ends[i + 1]
SCREAMING_SNAKE_CASE_ = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(__lowerCamelCase , __lowerCamelCase ) ) )
self.out_layers.append(nn.Linear(__lowerCamelCase , r_idx - l_idx ) )
SCREAMING_SNAKE_CASE_ = keep_order
def __A ( self : Dict , __magic_name__ : str , __magic_name__ : Optional[int] , __magic_name__ : Union[str, Any] , __magic_name__ : List[Any] ) -> Union[str, Any]:
if proj is None:
SCREAMING_SNAKE_CASE_ = nn.functional.linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
SCREAMING_SNAKE_CASE_ = nn.functional.linear(__lowerCamelCase , proj.t().contiguous() )
SCREAMING_SNAKE_CASE_ = nn.functional.linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def __A ( self : Dict , __magic_name__ : List[str] , __magic_name__ : int=None , __magic_name__ : Optional[Any]=False ) -> int:
if labels is not None:
# Shift so that tokens < n predict n
SCREAMING_SNAKE_CASE_ = hidden[..., :-1, :].contiguous()
SCREAMING_SNAKE_CASE_ = labels[..., 1:].contiguous()
SCREAMING_SNAKE_CASE_ = hidden.view(-1 , hidden.size(-1 ) )
SCREAMING_SNAKE_CASE_ = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError("Input and labels should have the same size in the batch dimension." )
else:
SCREAMING_SNAKE_CASE_ = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
SCREAMING_SNAKE_CASE_ = self._compute_logit(__lowerCamelCase , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
SCREAMING_SNAKE_CASE_ = labels != -100
SCREAMING_SNAKE_CASE_ = torch.zeros_like(__lowerCamelCase , dtype=hidden.dtype , device=hidden.device )
SCREAMING_SNAKE_CASE_ = (
-nn.functional.log_softmax(__lowerCamelCase , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
SCREAMING_SNAKE_CASE_ = nn.functional.log_softmax(__lowerCamelCase , dim=-1 )
else:
# construct weights and biases
SCREAMING_SNAKE_CASE_ = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
SCREAMING_SNAKE_CASE_ = self.cutoff_ends[i], self.cutoff_ends[i + 1]
SCREAMING_SNAKE_CASE_ = self.out_layers[0].weight[l_idx:r_idx]
SCREAMING_SNAKE_CASE_ = self.out_layers[0].bias[l_idx:r_idx]
else:
SCREAMING_SNAKE_CASE_ = self.out_layers[i].weight
SCREAMING_SNAKE_CASE_ = self.out_layers[i].bias
if i == 0:
SCREAMING_SNAKE_CASE_ = torch.cat([weight_i, self.cluster_weight] , dim=0 )
SCREAMING_SNAKE_CASE_ = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(__lowerCamelCase )
biases.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = weights[0], biases[0], self.out_projs[0]
SCREAMING_SNAKE_CASE_ = self._compute_logit(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = nn.functional.log_softmax(__lowerCamelCase , dim=1 )
if labels is None:
SCREAMING_SNAKE_CASE_ = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
SCREAMING_SNAKE_CASE_ = torch.zeros_like(__lowerCamelCase , dtype=hidden.dtype , device=hidden.device )
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = [0] + self.cutoffs
for i in range(len(__lowerCamelCase ) - 1 ):
SCREAMING_SNAKE_CASE_ = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
SCREAMING_SNAKE_CASE_ = (labels >= l_idx) & (labels < r_idx)
SCREAMING_SNAKE_CASE_ = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
SCREAMING_SNAKE_CASE_ = labels.index_select(0 , __lowerCamelCase ) - l_idx
SCREAMING_SNAKE_CASE_ = head_logprob.index_select(0 , __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = hidden.index_select(0 , __lowerCamelCase )
else:
SCREAMING_SNAKE_CASE_ = hidden
if i == 0:
if labels is not None:
SCREAMING_SNAKE_CASE_ = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
SCREAMING_SNAKE_CASE_ = head_logprob[:, : self.cutoffs[0]]
else:
SCREAMING_SNAKE_CASE_ = weights[i], biases[i], self.out_projs[i]
SCREAMING_SNAKE_CASE_ = self._compute_logit(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = nn.functional.log_softmax(__lowerCamelCase , dim=1 )
SCREAMING_SNAKE_CASE_ = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
SCREAMING_SNAKE_CASE_ = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
SCREAMING_SNAKE_CASE_ = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
SCREAMING_SNAKE_CASE_ = logprob_i
if labels is not None:
if (hasattr(self , "keep_order" ) and self.keep_order) or keep_order:
out.index_copy_(0 , __lowerCamelCase , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def __A ( self : List[Any] , __magic_name__ : int ) -> List[Any]:
if self.n_clusters == 0:
SCREAMING_SNAKE_CASE_ = self._compute_logit(__lowerCamelCase , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(__lowerCamelCase , dim=-1 )
else:
# construct weights and biases
SCREAMING_SNAKE_CASE_ = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
SCREAMING_SNAKE_CASE_ = self.cutoff_ends[i], self.cutoff_ends[i + 1]
SCREAMING_SNAKE_CASE_ = self.out_layers[0].weight[l_idx:r_idx]
SCREAMING_SNAKE_CASE_ = self.out_layers[0].bias[l_idx:r_idx]
else:
SCREAMING_SNAKE_CASE_ = self.out_layers[i].weight
SCREAMING_SNAKE_CASE_ = self.out_layers[i].bias
if i == 0:
SCREAMING_SNAKE_CASE_ = torch.cat([weight_i, self.cluster_weight] , dim=0 )
SCREAMING_SNAKE_CASE_ = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(__lowerCamelCase )
biases.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = weights[0], biases[0], self.out_projs[0]
SCREAMING_SNAKE_CASE_ = self._compute_logit(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = hidden.new_empty((head_logit.size(0 ), self.n_token) )
SCREAMING_SNAKE_CASE_ = nn.functional.log_softmax(__lowerCamelCase , dim=1 )
SCREAMING_SNAKE_CASE_ = [0] + self.cutoffs
for i in range(len(__lowerCamelCase ) - 1 ):
SCREAMING_SNAKE_CASE_ = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
SCREAMING_SNAKE_CASE_ = head_logprob[:, : self.cutoffs[0]]
else:
SCREAMING_SNAKE_CASE_ = weights[i], biases[i], self.out_projs[i]
SCREAMING_SNAKE_CASE_ = self._compute_logit(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = nn.functional.log_softmax(__lowerCamelCase , dim=1 )
SCREAMING_SNAKE_CASE_ = head_logprob[:, -i] + tail_logprob_i
SCREAMING_SNAKE_CASE_ = logprob_i
return out
| 356
|
import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 305
| 0
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = LEDConfig
lowerCamelCase__ = {}
lowerCamelCase__ = """gelu"""
def __init__( self : List[str] , __magic_name__ : Any , __magic_name__ : List[str]=13 , __magic_name__ : Dict=7 , __magic_name__ : Optional[int]=True , __magic_name__ : Any=False , __magic_name__ : Dict=99 , __magic_name__ : Optional[Any]=32 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Dict=4 , __magic_name__ : str=37 , __magic_name__ : List[Any]=0.1 , __magic_name__ : str=0.1 , __magic_name__ : List[str]=20 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Union[str, Any]=1 , __magic_name__ : str=0 , __magic_name__ : Optional[Any]=4 , ) -> Any:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = batch_size
SCREAMING_SNAKE_CASE_ = seq_length
SCREAMING_SNAKE_CASE_ = is_training
SCREAMING_SNAKE_CASE_ = use_labels
SCREAMING_SNAKE_CASE_ = vocab_size
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_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = eos_token_id
SCREAMING_SNAKE_CASE_ = pad_token_id
SCREAMING_SNAKE_CASE_ = bos_token_id
SCREAMING_SNAKE_CASE_ = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
SCREAMING_SNAKE_CASE_ = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
SCREAMING_SNAKE_CASE_ = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __A ( self : List[str] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
SCREAMING_SNAKE_CASE_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
SCREAMING_SNAKE_CASE_ = tf.concat([input_ids, eos_tensor] , axis=1 )
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE_ = 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 , attention_window=self.attention_window , **self.config_updates , )
SCREAMING_SNAKE_CASE_ = prepare_led_inputs_dict(_A , _A , _A )
SCREAMING_SNAKE_CASE_ = tf.concat(
[tf.zeros_like(_A )[:, :-1], tf.ones_like(_A )[:, -1:]] , axis=-1 , )
SCREAMING_SNAKE_CASE_ = global_attention_mask
return config, inputs_dict
def __A ( self : Tuple , __magic_name__ : str , __magic_name__ : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = TFLEDModel(config=_A ).get_decoder()
SCREAMING_SNAKE_CASE_ = inputs_dict['input_ids']
SCREAMING_SNAKE_CASE_ = input_ids[:1, :]
SCREAMING_SNAKE_CASE_ = inputs_dict['attention_mask'][:1, :]
SCREAMING_SNAKE_CASE_ = 1
# first forward pass
SCREAMING_SNAKE_CASE_ = model(_A , attention_mask=_A , use_cache=_A )
SCREAMING_SNAKE_CASE_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
SCREAMING_SNAKE_CASE_ = tf.concat([input_ids, next_tokens] , axis=-1 )
SCREAMING_SNAKE_CASE_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
SCREAMING_SNAKE_CASE_ = model(_A , attention_mask=_A )[0]
SCREAMING_SNAKE_CASE_ = model(_A , attention_mask=_A , past_key_values=_A )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
SCREAMING_SNAKE_CASE_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
SCREAMING_SNAKE_CASE_ = output_from_no_past[:, -3:, random_slice_idx]
SCREAMING_SNAKE_CASE_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_A , _A , rtol=1e-3 )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , ):
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE_ = tf.cast(tf.math.not_equal(UpperCAmelCase_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE_ = 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:
SCREAMING_SNAKE_CASE_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
lowerCamelCase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
lowerCamelCase__ = (
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowerCamelCase__ = True
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def __A ( self : Optional[Any] ) -> Any:
SCREAMING_SNAKE_CASE_ = TFLEDModelTester(self )
SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=_A )
def __A ( self : Dict ) -> Optional[Any]:
self.config_tester.run_common_tests()
def __A ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_A )
def __A ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = tf.zeros_like(inputs_dict["attention_mask"] )
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["global_attention_mask"] , )
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = self.model_tester.seq_length
SCREAMING_SNAKE_CASE_ = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__magic_name__ : Any ):
SCREAMING_SNAKE_CASE_ = outputs.decoder_attentions
self.assertEqual(len(_A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__magic_name__ : str ):
SCREAMING_SNAKE_CASE_ = [t.numpy() for t in outputs.encoder_attentions]
SCREAMING_SNAKE_CASE_ = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_A ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(_A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = model_class(_A )
SCREAMING_SNAKE_CASE_ = model(self._prepare_for_class(_A , _A ) )
SCREAMING_SNAKE_CASE_ = len(_A )
self.assertEqual(config.output_hidden_states , _A )
check_encoder_attentions_output(_A )
if self.is_encoder_decoder:
SCREAMING_SNAKE_CASE_ = model_class(_A )
SCREAMING_SNAKE_CASE_ = model(self._prepare_for_class(_A , _A ) )
self.assertEqual(config.output_hidden_states , _A )
check_decoder_attentions_output(_A )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = model_class(_A )
SCREAMING_SNAKE_CASE_ = model(self._prepare_for_class(_A , _A ) )
self.assertEqual(config.output_hidden_states , _A )
check_encoder_attentions_output(_A )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = model_class(_A )
SCREAMING_SNAKE_CASE_ = model(self._prepare_for_class(_A , _A ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_A ) )
self.assertEqual(model.config.output_hidden_states , _A )
check_encoder_attentions_output(_A )
@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
def __A ( self : Tuple ) -> List[Any]:
pass
def __A ( self : str ) -> Optional[int]:
# TODO: Head-masking not yet implement
pass
def a__ ( __UpperCamelCase ):
"""simple docstring"""
return tf.constant(UpperCAmelCase_ , dtype=tf.intaa )
A : Optional[int] = 1E-4
@slow
@require_tf
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led
# change to intended input here
SCREAMING_SNAKE_CASE_ = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
SCREAMING_SNAKE_CASE_ = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
SCREAMING_SNAKE_CASE_ = prepare_led_inputs_dict(model.config , _A , _A )
SCREAMING_SNAKE_CASE_ = model(**_A )[0]
SCREAMING_SNAKE_CASE_ = (1, 1_024, 768)
self.assertEqual(output.shape , _A )
# change to expected output here
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , _A , atol=1e-3 )
def __A ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )
# change to intended input here
SCREAMING_SNAKE_CASE_ = _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
SCREAMING_SNAKE_CASE_ = _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
SCREAMING_SNAKE_CASE_ = prepare_led_inputs_dict(model.config , _A , _A )
SCREAMING_SNAKE_CASE_ = model(**_A )[0]
SCREAMING_SNAKE_CASE_ = (1, 1_024, model.config.vocab_size)
self.assertEqual(output.shape , _A )
# change to expected output here
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , _A , atol=1e-3 , rtol=1e-3 )
| 357
|
from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "08x" )[-8:]
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
return little_endian_hex
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = b""
for char in message:
bit_string += format(__UpperCamelCase , "08b" ).encode("utf-8" )
SCREAMING_SNAKE_CASE_ = format(len(__UpperCamelCase ) , "064b" ).encode("utf-8" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__UpperCamelCase ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) % 5_1_2 != 0:
raise ValueError("Input must have length that's a multiple of 512" )
for pos in range(0 , len(__UpperCamelCase ) , 5_1_2 ):
SCREAMING_SNAKE_CASE_ = bit_string[pos : pos + 5_1_2]
SCREAMING_SNAKE_CASE_ = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "032b" )
SCREAMING_SNAKE_CASE_ = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__UpperCamelCase , 2 )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return (a + b) % 2**3_2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
if shift < 0:
raise ValueError("Shift must be non-negative" )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = preprocess(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
SCREAMING_SNAKE_CASE_ = 0X67452301
SCREAMING_SNAKE_CASE_ = 0Xefcdab89
SCREAMING_SNAKE_CASE_ = 0X98badcfe
SCREAMING_SNAKE_CASE_ = 0X10325476
SCREAMING_SNAKE_CASE_ = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = aa
SCREAMING_SNAKE_CASE_ = ba
SCREAMING_SNAKE_CASE_ = ca
SCREAMING_SNAKE_CASE_ = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = d ^ (b & (c ^ d))
SCREAMING_SNAKE_CASE_ = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = c ^ (d & (b ^ c))
SCREAMING_SNAKE_CASE_ = (5 * i + 1) % 1_6
elif i <= 4_7:
SCREAMING_SNAKE_CASE_ = b ^ c ^ d
SCREAMING_SNAKE_CASE_ = (3 * i + 5) % 1_6
else:
SCREAMING_SNAKE_CASE_ = c ^ (b | not_aa(__UpperCamelCase ))
SCREAMING_SNAKE_CASE_ = (7 * i) % 1_6
SCREAMING_SNAKE_CASE_ = (f + a + added_consts[i] + block_words[g]) % 2**3_2
SCREAMING_SNAKE_CASE_ = d
SCREAMING_SNAKE_CASE_ = c
SCREAMING_SNAKE_CASE_ = b
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , left_rotate_aa(__UpperCamelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
def a__ ( __UpperCamelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 358
|
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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ) -> Any:
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = BlipImageProcessor()
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )
SCREAMING_SNAKE_CASE_ = BlipaProcessor(__magic_name__ , __magic_name__ )
processor.save_pretrained(self.tmpdirname )
def __A ( self : str , **__magic_name__ : int ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).tokenizer
def __A ( self : Dict , **__magic_name__ : List[Any] ) -> int:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).image_processor
def __A ( self : int ) -> Any:
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ = [Image.fromarray(np.moveaxis(__magic_name__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE_ = self.get_image_processor(do_normalize=__magic_name__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE_ = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__magic_name__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __magic_name__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __magic_name__ )
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = image_processor(__magic_name__ , return_tensors="np" )
SCREAMING_SNAKE_CASE_ = processor(images=__magic_name__ , 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 : str ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_token_type_ids=__magic_name__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__magic_name__ ):
processor()
def __A ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ = processor.batch_decode(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 305
| 0
|
from collections.abc import Sequence
def a__ ( __UpperCamelCase = None ):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
SCREAMING_SNAKE_CASE_ = nums[0]
for i in range(1 , len(lowerCAmelCase__ ) ):
SCREAMING_SNAKE_CASE_ = nums[i]
SCREAMING_SNAKE_CASE_ = max(lowerCAmelCase__ , ans + num , lowerCAmelCase__ )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
A : int = int(input("Enter number of elements : ").strip())
A : Any = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 359
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ["DPTFeatureExtractor"]
A : str = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[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
A : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
A : List[Any] = logging.get_logger(__name__)
@dataclass
class lowerCamelCase (__a ):
"""simple docstring"""
lowerCamelCase__ = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self : int , **__magic_name__ : List[Any] ) -> List[Any]:
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
SCREAMING_SNAKE_CASE_ = deprecated_arg[3:]
SCREAMING_SNAKE_CASE_ = not kwargs.pop(a__ )
logger.warning(
F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
F''' {positive_arg}={kwargs[positive_arg]}''' )
SCREAMING_SNAKE_CASE_ = kwargs.pop("tpu_name" , self.tpu_name )
SCREAMING_SNAKE_CASE_ = kwargs.pop("device_idx" , self.device_idx )
SCREAMING_SNAKE_CASE_ = kwargs.pop("eager_mode" , self.eager_mode )
SCREAMING_SNAKE_CASE_ = kwargs.pop("use_xla" , self.use_xla )
super().__init__(**a__ )
lowerCamelCase__ = field(
default=__a , metadata={'''help''': '''Name of TPU'''} , )
lowerCamelCase__ = field(
default=0 , metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''} , )
lowerCamelCase__ = field(default=__a , metadata={'''help''': '''Benchmark models in eager model.'''} )
lowerCamelCase__ = field(
default=__a , metadata={
'''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
} , )
@cached_property
def __A ( self : int ) -> List[str]:
requires_backends(self , ["tf"] )
SCREAMING_SNAKE_CASE_ = None
if self.tpu:
try:
if self.tpu_name:
SCREAMING_SNAKE_CASE_ = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
SCREAMING_SNAKE_CASE_ = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
SCREAMING_SNAKE_CASE_ = None
return tpu
@cached_property
def __A ( self : Optional[Any] ) -> int:
requires_backends(self , ["tf"] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
SCREAMING_SNAKE_CASE_ = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , "GPU" )
SCREAMING_SNAKE_CASE_ = tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , "GPU" ) # disable GPU
SCREAMING_SNAKE_CASE_ = tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}''' )
return strategy
@property
def __A ( self : List[str] ) -> Union[str, Any]:
requires_backends(self , ["tf"] )
return self._setup_tpu is not None
@property
def __A ( self : int ) -> Optional[int]:
requires_backends(self , ["tf"] )
return self._setup_strategy
@property
def __A ( self : str ) -> Optional[int]:
requires_backends(self , ["tf"] )
return tf.config.list_physical_devices("GPU" )
@property
def __A ( self : Dict ) -> List[Any]:
requires_backends(self , ["tf"] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __A ( self : Optional[Any] ) -> Any:
return self.n_gpu > 0
| 360
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
A : int = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Union[str, Any] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 305
| 0
|
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowerCamelCase (unittest.TestCase , lowerCamelCase__ ):
"""simple docstring"""
def __A ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = load_tool("text-to-speech" )
self.tool.setup()
def __A ( self : List[str] ) -> Any:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = self.tool("hey" )
SCREAMING_SNAKE_CASE_ = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def __A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = self.tool("hey" )
SCREAMING_SNAKE_CASE_ = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 361
|
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_text_model'''
def __init__( self : Optional[Any] , __magic_name__ : Union[str, Any]=30_522 , __magic_name__ : Tuple=768 , __magic_name__ : List[str]=12 , __magic_name__ : Optional[Any]=12 , __magic_name__ : str=3_072 , __magic_name__ : Dict="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : Optional[int]=0.1 , __magic_name__ : List[str]=512 , __magic_name__ : Any=2 , __magic_name__ : Optional[Any]=0.02 , __magic_name__ : int=1e-12 , __magic_name__ : str=0 , __magic_name__ : Optional[Any]="absolute" , __magic_name__ : Optional[Any]=True , **__magic_name__ : Tuple , ) -> Union[str, Any]:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = pad_token_id
@classmethod
def __A ( cls : Any , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_vision_model'''
def __init__( self : List[str] , __magic_name__ : int = 3 , __magic_name__ : int = 600 , __magic_name__ : float = 2.0 , __magic_name__ : float = 3.1 , __magic_name__ : int = 8 , __magic_name__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ : List[int] = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ : List[int] = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ : List[int] = [] , __magic_name__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ : float = 0.25 , __magic_name__ : str = "swish" , __magic_name__ : int = 2_560 , __magic_name__ : str = "mean" , __magic_name__ : float = 0.02 , __magic_name__ : float = 0.001 , __magic_name__ : float = 0.99 , __magic_name__ : float = 0.2 , **__magic_name__ : List[Any] , ) -> Tuple:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = width_coefficient
SCREAMING_SNAKE_CASE_ = depth_coefficient
SCREAMING_SNAKE_CASE_ = depth_divisor
SCREAMING_SNAKE_CASE_ = kernel_sizes
SCREAMING_SNAKE_CASE_ = in_channels
SCREAMING_SNAKE_CASE_ = out_channels
SCREAMING_SNAKE_CASE_ = depthwise_padding
SCREAMING_SNAKE_CASE_ = strides
SCREAMING_SNAKE_CASE_ = num_block_repeats
SCREAMING_SNAKE_CASE_ = expand_ratios
SCREAMING_SNAKE_CASE_ = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dim
SCREAMING_SNAKE_CASE_ = pooling_type
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = batch_norm_eps
SCREAMING_SNAKE_CASE_ = batch_norm_momentum
SCREAMING_SNAKE_CASE_ = drop_connect_rate
SCREAMING_SNAKE_CASE_ = sum(__magic_name__ ) * 4
@classmethod
def __A ( cls : List[str] , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Dict ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align'''
lowerCamelCase__ = True
def __init__( self : Optional[Any] , __magic_name__ : Dict=None , __magic_name__ : List[Any]=None , __magic_name__ : str=640 , __magic_name__ : Any=1.0 , __magic_name__ : Dict=0.02 , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__(**__magic_name__ )
if text_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
SCREAMING_SNAKE_CASE_ = AlignTextConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = AlignVisionConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = projection_dim
SCREAMING_SNAKE_CASE_ = temperature_init_value
SCREAMING_SNAKE_CASE_ = initializer_range
@classmethod
def __A ( cls : List[str] , __magic_name__ : AlignTextConfig , __magic_name__ : AlignVisionConfig , **__magic_name__ : Tuple ) -> Any:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__magic_name__ )
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.text_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 305
| 0
|
"""simple docstring"""
def a__ ( __UpperCamelCase ):
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE_ = False
for j in range(__snake_case , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = unsorted[j - 1], unsorted[j]
SCREAMING_SNAKE_CASE_ = True
for j in range(__snake_case ):
if unsorted[j] > unsorted[j + 1]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = unsorted[j + 1], unsorted[j]
SCREAMING_SNAKE_CASE_ = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
A : Optional[Any] = input("Enter numbers separated by a comma:\n").strip()
A : Tuple = [int(item) for item in user_input.split(",")]
print(f"{cocktail_shaker_sort(unsorted) = }")
| 362
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
SCREAMING_SNAKE_CASE_ = "x = y"
SCREAMING_SNAKE_CASE_ = {"y": 5}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 5, "y": 5} )
def __A ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "y = add_two(x)"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result is None
assert "tried to execute add_two" in out.out
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
def __A ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "x = 3\ny = 5"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
def __A ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "text = f'This is x: {x}.'"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(__magic_name__ , {"x": 3, "text": "This is x: 3."} )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "if x <= 3:\n y = 2\nelse:\n y = 5"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 2} )
SCREAMING_SNAKE_CASE_ = {"x": 8}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 8, "y": 5} )
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertListEqual(__magic_name__ , [3, 5] )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
def __A ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "y = x"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 3} )
def __A ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]\ntest_list[1]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = "x = 0\nfor i in range(3):\n x = i"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"range": range} , state=__magic_name__ )
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 2, "i": 2} )
| 305
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A : Tuple = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = [
"UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
"UniSpeechForCTC",
"UniSpeechForPreTraining",
"UniSpeechForSequenceClassification",
"UniSpeechModel",
"UniSpeechPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
A : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 363
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__UpperCamelCase )] )
SCREAMING_SNAKE_CASE_ = np.array(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __UpperCamelCase ) ) , x.transpose() ) , __UpperCamelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = (1, 2, 1)
SCREAMING_SNAKE_CASE_ = (1, 1, 0, 7)
SCREAMING_SNAKE_CASE_ = SARIMAX(
__UpperCamelCase , exog=__UpperCamelCase , order=__UpperCamelCase , seasonal_order=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = model.fit(disp=__UpperCamelCase , maxiter=6_0_0 , method="nm" )
SCREAMING_SNAKE_CASE_ = model_fit.predict(1 , len(__UpperCamelCase ) , exog=[test_match] )
return result[0]
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = SVR(kernel="rbf" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = regressor.predict(__UpperCamelCase )
return y_pred[0]
def a__ ( __UpperCamelCase ):
train_user.sort()
SCREAMING_SNAKE_CASE_ = np.percentile(__UpperCamelCase , 2_5 )
SCREAMING_SNAKE_CASE_ = np.percentile(__UpperCamelCase , 7_5 )
SCREAMING_SNAKE_CASE_ = qa - qa
SCREAMING_SNAKE_CASE_ = qa - (iqr * 0.1)
return low_lim
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
for i in list_vote:
if i > actual_result:
SCREAMING_SNAKE_CASE_ = not_safe + 1
else:
if abs(abs(__UpperCamelCase ) - abs(__UpperCamelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
A : Optional[Any] = pd.DataFrame(
data_input, columns=["total_user", "total_even", "days"]
)
A : Union[str, Any] = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : List[str] = normalize_df[:, 0].tolist()
A : int = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Tuple = x[: len(x) - 1]
A : str = x[len(x) - 1 :]
# for linear regression & sarimax
A : Tuple = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : List[Any] = total_date[len(total_date) - 1 :]
A : List[Any] = total_user[len(total_user) - 1 :]
A : Optional[Any] = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : Optional[int] = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : str = "" if data_safety_checker(res_vote, tst_user) else "not "
print("Today's data is {not_str}safe.")
| 305
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Dict = {
"configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = ["RemBertTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = ["RemBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
"REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RemBertForCausalLM",
"RemBertForMaskedLM",
"RemBertForMultipleChoice",
"RemBertForQuestionAnswering",
"RemBertForSequenceClassification",
"RemBertForTokenClassification",
"RemBertLayer",
"RemBertModel",
"RemBertPreTrainedModel",
"load_tf_weights_in_rembert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
"TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRemBertForCausalLM",
"TFRemBertForMaskedLM",
"TFRemBertForMultipleChoice",
"TFRemBertForQuestionAnswering",
"TFRemBertForSequenceClassification",
"TFRemBertForTokenClassification",
"TFRemBertLayer",
"TFRemBertModel",
"TFRemBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 364
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
"SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwinForImageClassification",
"SwinForMaskedImageModeling",
"SwinModel",
"SwinPreTrainedModel",
"SwinBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
"TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSwinForImageClassification",
"TFSwinForMaskedImageModeling",
"TFSwinModel",
"TFSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : str = {
"facebook/data2vec-vision-base-ft": (
"https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
),
}
class lowerCamelCase (a__ ):
"""simple docstring"""
lowerCamelCase__ = "data2vec-vision"
def __init__( self : Optional[Any] , __magic_name__ : Optional[int]=768 , __magic_name__ : List[str]=12 , __magic_name__ : List[str]=12 , __magic_name__ : List[str]=3_072 , __magic_name__ : Optional[Any]="gelu" , __magic_name__ : Optional[Any]=0.0 , __magic_name__ : List[str]=0.0 , __magic_name__ : Any=0.02 , __magic_name__ : Dict=1e-12 , __magic_name__ : Optional[Any]=224 , __magic_name__ : str=16 , __magic_name__ : Optional[int]=3 , __magic_name__ : List[Any]=False , __magic_name__ : List[str]=False , __magic_name__ : Any=False , __magic_name__ : Union[str, Any]=False , __magic_name__ : List[str]=0.1 , __magic_name__ : Optional[Any]=0.1 , __magic_name__ : Dict=True , __magic_name__ : List[str]=[3, 5, 7, 11] , __magic_name__ : List[str]=[1, 2, 3, 6] , __magic_name__ : List[str]=True , __magic_name__ : Union[str, Any]=0.4 , __magic_name__ : str=256 , __magic_name__ : Optional[Any]=1 , __magic_name__ : int=False , __magic_name__ : str=255 , **__magic_name__ : List[Any] , ) -> Optional[int]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
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_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = patch_size
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = use_mask_token
SCREAMING_SNAKE_CASE_ = use_absolute_position_embeddings
SCREAMING_SNAKE_CASE_ = use_relative_position_bias
SCREAMING_SNAKE_CASE_ = use_shared_relative_position_bias
SCREAMING_SNAKE_CASE_ = layer_scale_init_value
SCREAMING_SNAKE_CASE_ = drop_path_rate
SCREAMING_SNAKE_CASE_ = use_mean_pooling
# decode head attributes (semantic segmentation)
SCREAMING_SNAKE_CASE_ = out_indices
SCREAMING_SNAKE_CASE_ = pool_scales
# auxiliary head attributes (semantic segmentation)
SCREAMING_SNAKE_CASE_ = use_auxiliary_head
SCREAMING_SNAKE_CASE_ = auxiliary_loss_weight
SCREAMING_SNAKE_CASE_ = auxiliary_channels
SCREAMING_SNAKE_CASE_ = auxiliary_num_convs
SCREAMING_SNAKE_CASE_ = auxiliary_concat_input
SCREAMING_SNAKE_CASE_ = semantic_loss_ignore_index
class lowerCamelCase (a__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __A ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def __A ( self : int ) -> float:
return 1e-4
| 365
|
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
A : Union[str, Any] = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@classmethod
def __A ( cls : Any ) -> Dict:
SCREAMING_SNAKE_CASE_ = TOKEN
HfFolder.save_token(__magic_name__ )
@classmethod
def __A ( cls : Optional[int] ) -> Tuple:
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__magic_name__ , repo_id="test-model-flax" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
__magic_name__ , repo_id="valid_org/test-model-flax-org" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
SCREAMING_SNAKE_CASE_ = False
return models_are_equal
@require_flax
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) , max_shard_size="10KB" )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def __A ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
| 305
| 0
|
def a__ ( __UpperCamelCase ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError("check_bouncy() accepts only integer arguments" )
SCREAMING_SNAKE_CASE_ = str(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ = "".join(sorted(lowerCAmelCase__ ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != str_n
def a__ ( __UpperCamelCase = 9_9 ):
if not 0 < percent < 1_0_0:
raise ValueError("solution() only accepts values from 0 to 100" )
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 1
while True:
if check_bouncy(lowerCAmelCase__ ):
bouncy_num += 1
if (bouncy_num / num) * 1_0_0 >= percent:
return num
num += 1
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"{solution(99)}")
| 366
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : Union[str, Any] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def a__ ( ):
SCREAMING_SNAKE_CASE_ = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
SCREAMING_SNAKE_CASE_ = get_sagemaker_input()
else:
SCREAMING_SNAKE_CASE_ = get_cluster_input()
return config
def a__ ( __UpperCamelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser("config" , description=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Accelerate config command" , description=__UpperCamelCase )
parser.add_argument(
"--config_file" , default=__UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_user_input()
if args.config_file is not None:
SCREAMING_SNAKE_CASE_ = args.config_file
else:
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__UpperCamelCase )
else:
config.to_yaml_file(__UpperCamelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = config_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
config_command(__UpperCamelCase )
if __name__ == "__main__":
main()
| 305
| 0
|
from __future__ import annotations
A : List[str] = 10
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = max(__UpperCamelCase )
while placement <= max_digit:
# declare and initialize empty buckets
SCREAMING_SNAKE_CASE_ = [[] for _ in range(__UpperCamelCase )]
# split list_of_ints between the buckets
for i in list_of_ints:
SCREAMING_SNAKE_CASE_ = int((i / placement) % RADIX )
buckets[tmp].append(__UpperCamelCase )
# put each buckets' contents into list_of_ints
SCREAMING_SNAKE_CASE_ = 0
for b in range(__UpperCamelCase ):
for i in buckets[b]:
SCREAMING_SNAKE_CASE_ = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(default='''summarization''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
lowerCamelCase__ = Features({'''text''': Value('''string''' )} )
lowerCamelCase__ = Features({'''summary''': Value('''string''' )} )
lowerCamelCase__ = "text"
lowerCamelCase__ = "summary"
@property
def __A ( self : Dict ) -> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 305
| 0
|
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = len(snake_case_ )
while cur > 1:
# Find the maximum number in arr
SCREAMING_SNAKE_CASE_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
SCREAMING_SNAKE_CASE_ = arr[mi::-1] + arr[mi + 1 : len(snake_case_ )]
# Reverse whole list
SCREAMING_SNAKE_CASE_ = arr[cur - 1 :: -1] + arr[cur : len(snake_case_ )]
cur -= 1
return arr
if __name__ == "__main__":
A : Any = input("Enter numbers separated by a comma:\n").strip()
A : int = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 368
|
from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None , __magic_name__ : List[str]=2_048 ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = config.__dict__
SCREAMING_SNAKE_CASE_ = modal_hidden_size
if num_labels:
SCREAMING_SNAKE_CASE_ = num_labels
| 305
| 0
|
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> str:
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(_UpperCAmelCase ) )
def __A ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE_ = [
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(_UpperCAmelCase ) )
def __A ( self : List[Any] ) -> Union[str, Any]:
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(_UpperCAmelCase ) )
def __A ( self : List[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = [
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
self.assertTrue(is_safetensors_compatible(_UpperCAmelCase ) )
def __A ( self : Dict ) -> Union[str, Any]:
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(_UpperCAmelCase ) )
def __A ( self : Tuple ) -> str:
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(_UpperCAmelCase , variant=_UpperCAmelCase ) )
def __A ( self : Tuple ) -> Dict:
SCREAMING_SNAKE_CASE_ = [
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
SCREAMING_SNAKE_CASE_ = 'fp16'
self.assertTrue(is_safetensors_compatible(_UpperCAmelCase , variant=_UpperCAmelCase ) )
def __A ( self : str ) -> str:
# 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(_UpperCAmelCase , variant=_UpperCAmelCase ) )
def __A ( self : Tuple ) -> List[Any]:
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(_UpperCAmelCase , variant=_UpperCAmelCase ) )
def __A ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = [
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
]
SCREAMING_SNAKE_CASE_ = 'fp16'
self.assertTrue(is_safetensors_compatible(_UpperCAmelCase , variant=_UpperCAmelCase ) )
def __A ( self : str ) -> List[Any]:
# 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(_UpperCAmelCase , variant=_UpperCAmelCase ) )
def __A ( self : List[Any] ) -> List[Any]:
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(_UpperCAmelCase , variant=_UpperCAmelCase ) )
| 369
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViltImageProcessor'''
lowerCamelCase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[int] , __magic_name__ : str=None , __magic_name__ : List[str]=None , **__magic_name__ : 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`." )
super().__init__(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = self.image_processor
def __call__( self : List[str] , __magic_name__ : List[str] , __magic_name__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __magic_name__ : bool = True , __magic_name__ : Union[bool, str, PaddingStrategy] = False , __magic_name__ : Union[bool, str, TruncationStrategy] = None , __magic_name__ : Optional[int] = None , __magic_name__ : int = 0 , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = True , __magic_name__ : Optional[Union[str, TensorType]] = None , **__magic_name__ : str , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE_ = self.tokenizer(
text=__magic_name__ , add_special_tokens=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , max_length=__magic_name__ , stride=__magic_name__ , pad_to_multiple_of=__magic_name__ , return_token_type_ids=__magic_name__ , return_attention_mask=__magic_name__ , return_overflowing_tokens=__magic_name__ , return_special_tokens_mask=__magic_name__ , return_offsets_mapping=__magic_name__ , return_length=__magic_name__ , verbose=__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ , )
# add pixel_values + pixel_mask
SCREAMING_SNAKE_CASE_ = self.image_processor(__magic_name__ , return_tensors=__magic_name__ )
encoding.update(__magic_name__ )
return encoding
def __A ( self : Optional[int] , *__magic_name__ : List[Any] , **__magic_name__ : Optional[Any] ) -> Any:
return self.tokenizer.batch_decode(*__magic_name__ , **__magic_name__ )
def __A ( self : Dict , *__magic_name__ : List[Any] , **__magic_name__ : Union[str, Any] ) -> str:
return self.tokenizer.decode(*__magic_name__ , **__magic_name__ )
@property
def __A ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __A ( self : Dict ) -> List[Any]:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __magic_name__ , )
return self.image_processor_class
@property
def __A ( self : int ) -> List[Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __magic_name__ , )
return self.image_processor
| 305
| 0
|
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = set({"(", "[", "{"} )
SCREAMING_SNAKE_CASE_ = set({")", "]", "}"} )
SCREAMING_SNAKE_CASE_ = {"{": "}", "[": "]", "(": ")"}
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(__SCREAMING_SNAKE_CASE ) == 0 or (len(__SCREAMING_SNAKE_CASE ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(__SCREAMING_SNAKE_CASE ) == 0
def a__ ( ):
SCREAMING_SNAKE_CASE_ = input("Enter sequence of brackets: " )
if is_balanced(__SCREAMING_SNAKE_CASE ):
print(__SCREAMING_SNAKE_CASE , "is balanced" )
else:
print(__SCREAMING_SNAKE_CASE , "is not balanced" )
if __name__ == "__main__":
main()
| 370
|
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 ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : Optional[int] = {
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''table-transformer'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : List[Any] , __magic_name__ : Optional[Any]=True , __magic_name__ : Dict=None , __magic_name__ : Any=3 , __magic_name__ : List[str]=100 , __magic_name__ : Union[str, Any]=6 , __magic_name__ : Dict=2_048 , __magic_name__ : str=8 , __magic_name__ : int=6 , __magic_name__ : List[Any]=2_048 , __magic_name__ : Optional[int]=8 , __magic_name__ : Optional[int]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Optional[Any]=True , __magic_name__ : List[Any]="relu" , __magic_name__ : List[str]=256 , __magic_name__ : List[str]=0.1 , __magic_name__ : int=0.0 , __magic_name__ : Optional[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : str=1.0 , __magic_name__ : int=False , __magic_name__ : Dict="sine" , __magic_name__ : Union[str, Any]="resnet50" , __magic_name__ : Optional[Any]=True , __magic_name__ : str=False , __magic_name__ : List[str]=1 , __magic_name__ : int=5 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Tuple=1 , __magic_name__ : Optional[int]=1 , __magic_name__ : Optional[Any]=5 , __magic_name__ : Optional[int]=2 , __magic_name__ : Union[str, Any]=0.1 , **__magic_name__ : Tuple , ) -> str:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = backbone_config.get("model_type" )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE_ = config_class.from_dict(__magic_name__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None, None, None
SCREAMING_SNAKE_CASE_ = use_timm_backbone
SCREAMING_SNAKE_CASE_ = backbone_config
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = num_queries
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = init_xavier_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = auxiliary_loss
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = backbone
SCREAMING_SNAKE_CASE_ = use_pretrained_backbone
SCREAMING_SNAKE_CASE_ = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE_ = class_cost
SCREAMING_SNAKE_CASE_ = bbox_cost
SCREAMING_SNAKE_CASE_ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE_ = mask_loss_coefficient
SCREAMING_SNAKE_CASE_ = dice_loss_coefficient
SCREAMING_SNAKE_CASE_ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE_ = giou_loss_coefficient
SCREAMING_SNAKE_CASE_ = eos_coefficient
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def __A ( self : Union[str, Any] ) -> int:
return self.encoder_attention_heads
@property
def __A ( self : Any ) -> int:
return self.d_model
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def __A ( self : Any ) -> float:
return 1e-5
@property
def __A ( self : int ) -> int:
return 12
| 305
| 0
|
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
A : List[Any] = logging.getLogger(__name__)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = np.argmax(__lowerCAmelCase , axis=1 )
return np.sum(outputs == labels )
def a__ ( __UpperCamelCase ):
with open(__lowerCAmelCase , encoding="utf_8" ) as f:
SCREAMING_SNAKE_CASE_ = csv.reader(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = []
next(__lowerCAmelCase ) # skip the first line
for line in tqdm(__lowerCAmelCase ):
output.append((" ".join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
for dataset in encoded_datasets:
SCREAMING_SNAKE_CASE_ = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
SCREAMING_SNAKE_CASE_ = np.zeros((n_batch, 2) , dtype=np.intaa )
SCREAMING_SNAKE_CASE_ = np.full((n_batch, 2, input_len) , fill_value=-1_0_0 , dtype=np.intaa )
SCREAMING_SNAKE_CASE_ = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
SCREAMING_SNAKE_CASE_ = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
SCREAMING_SNAKE_CASE_ = with_conta
SCREAMING_SNAKE_CASE_ = with_conta
SCREAMING_SNAKE_CASE_ = len(__lowerCAmelCase ) - 1
SCREAMING_SNAKE_CASE_ = len(__lowerCAmelCase ) - 1
SCREAMING_SNAKE_CASE_ = with_conta
SCREAMING_SNAKE_CASE_ = with_conta
SCREAMING_SNAKE_CASE_ = mc_label
SCREAMING_SNAKE_CASE_ = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(__lowerCAmelCase ) for t in all_inputs ) )
return tensor_datasets
def a__ ( ):
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=__lowerCAmelCase , default="openai-gpt" , help="pretrained model name" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_eval" , action="store_true" , help="Whether to run eval on the dev set." )
parser.add_argument(
"--output_dir" , default=__lowerCAmelCase , type=__lowerCAmelCase , required=__lowerCAmelCase , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument("--train_dataset" , type=__lowerCAmelCase , default="" )
parser.add_argument("--eval_dataset" , type=__lowerCAmelCase , default="" )
parser.add_argument("--seed" , type=__lowerCAmelCase , default=4_2 )
parser.add_argument("--num_train_epochs" , type=__lowerCAmelCase , default=3 )
parser.add_argument("--train_batch_size" , type=__lowerCAmelCase , default=8 )
parser.add_argument("--eval_batch_size" , type=__lowerCAmelCase , default=1_6 )
parser.add_argument("--adam_epsilon" , default=1E-8 , type=__lowerCAmelCase , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , type=__lowerCAmelCase , default=1 )
parser.add_argument(
"--max_steps" , default=-1 , type=__lowerCAmelCase , help=(
"If > 0: set total number of training steps to perform. Override num_train_epochs."
) , )
parser.add_argument(
"--gradient_accumulation_steps" , type=__lowerCAmelCase , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--learning_rate" , type=__lowerCAmelCase , default=6.25E-5 )
parser.add_argument("--warmup_steps" , default=0 , type=__lowerCAmelCase , help="Linear warmup over warmup_steps." )
parser.add_argument("--lr_schedule" , type=__lowerCAmelCase , default="warmup_linear" )
parser.add_argument("--weight_decay" , type=__lowerCAmelCase , default=0.01 )
parser.add_argument("--lm_coef" , type=__lowerCAmelCase , default=0.9 )
parser.add_argument("--n_valid" , type=__lowerCAmelCase , default=3_7_4 )
parser.add_argument("--server_ip" , type=__lowerCAmelCase , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=__lowerCAmelCase , default="" , help="Can be used for distant debugging." )
SCREAMING_SNAKE_CASE_ = parser.parse_args()
print(__lowerCAmelCase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__lowerCAmelCase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
SCREAMING_SNAKE_CASE_ = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(__lowerCAmelCase , __lowerCAmelCase ) )
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True." )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
SCREAMING_SNAKE_CASE_ = ["_start_", "_delimiter_", "_classify_"]
SCREAMING_SNAKE_CASE_ = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.convert_tokens_to_ids(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(__lowerCAmelCase ) )
model.to(__lowerCAmelCase )
# Load and encode the datasets
def tokenize_and_encode(__UpperCamelCase ):
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__lowerCAmelCase ) )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return obj
return [tokenize_and_encode(__lowerCAmelCase ) for o in obj]
logger.info("Encoding dataset..." )
SCREAMING_SNAKE_CASE_ = load_rocstories_dataset(args.train_dataset )
SCREAMING_SNAKE_CASE_ = load_rocstories_dataset(args.eval_dataset )
SCREAMING_SNAKE_CASE_ = (train_dataset, eval_dataset)
SCREAMING_SNAKE_CASE_ = tokenize_and_encode(__lowerCAmelCase )
# Compute the max input length for the Transformer
SCREAMING_SNAKE_CASE_ = model.config.n_positions // 2 - 2
SCREAMING_SNAKE_CASE_ = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
SCREAMING_SNAKE_CASE_ = min(__lowerCAmelCase , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
SCREAMING_SNAKE_CASE_ = pre_process_datasets(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , *__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = tensor_datasets[0], tensor_datasets[1]
SCREAMING_SNAKE_CASE_ = TensorDataset(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = RandomSampler(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = DataLoader(__lowerCAmelCase , sampler=__lowerCAmelCase , batch_size=args.train_batch_size )
SCREAMING_SNAKE_CASE_ = TensorDataset(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = SequentialSampler(__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = DataLoader(__lowerCAmelCase , sampler=__lowerCAmelCase , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
SCREAMING_SNAKE_CASE_ = args.max_steps
SCREAMING_SNAKE_CASE_ = args.max_steps // (len(__lowerCAmelCase ) // args.gradient_accumulation_steps) + 1
else:
SCREAMING_SNAKE_CASE_ = len(__lowerCAmelCase ) // args.gradient_accumulation_steps * args.num_train_epochs
SCREAMING_SNAKE_CASE_ = list(model.named_parameters() )
SCREAMING_SNAKE_CASE_ = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
SCREAMING_SNAKE_CASE_ = [
{
"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], "weight_decay": 0.0},
]
SCREAMING_SNAKE_CASE_ = AdamW(__lowerCAmelCase , lr=args.learning_rate , eps=args.adam_epsilon )
SCREAMING_SNAKE_CASE_ = get_linear_schedule_with_warmup(
__lowerCAmelCase , num_warmup_steps=args.warmup_steps , num_training_steps=__lowerCAmelCase )
if args.do_train:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="Epoch" ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = tqdm(__lowerCAmelCase , desc="Training" )
for step, batch in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = tuple(t.to(__lowerCAmelCase ) for t in batch )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = batch
SCREAMING_SNAKE_CASE_ = model(__lowerCAmelCase , mc_token_ids=__lowerCAmelCase , lm_labels=__lowerCAmelCase , mc_labels=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
SCREAMING_SNAKE_CASE_ = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
SCREAMING_SNAKE_CASE_ = "Training loss: {:.2e} lr: {:.2e}".format(__lowerCAmelCase , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
SCREAMING_SNAKE_CASE_ = model.module if hasattr(__lowerCAmelCase , "module" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
SCREAMING_SNAKE_CASE_ = os.path.join(args.output_dir , __lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = os.path.join(args.output_dir , __lowerCAmelCase )
torch.save(model_to_save.state_dict() , __lowerCAmelCase )
model_to_save.config.to_json_file(__lowerCAmelCase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
SCREAMING_SNAKE_CASE_ = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
SCREAMING_SNAKE_CASE_ = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(__lowerCAmelCase )
if args.do_eval:
model.eval()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0, 0
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0, 0
for batch in tqdm(__lowerCAmelCase , desc="Evaluating" ):
SCREAMING_SNAKE_CASE_ = tuple(t.to(__lowerCAmelCase ) for t in batch )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = batch
with torch.no_grad():
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = model(
__lowerCAmelCase , mc_token_ids=__lowerCAmelCase , lm_labels=__lowerCAmelCase , mc_labels=__lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = mc_logits.detach().cpu().numpy()
SCREAMING_SNAKE_CASE_ = mc_labels.to("cpu" ).numpy()
SCREAMING_SNAKE_CASE_ = accuracy(__lowerCAmelCase , __lowerCAmelCase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
SCREAMING_SNAKE_CASE_ = eval_loss / nb_eval_steps
SCREAMING_SNAKE_CASE_ = eval_accuracy / nb_eval_examples
SCREAMING_SNAKE_CASE_ = tr_loss / nb_tr_steps if args.do_train else None
SCREAMING_SNAKE_CASE_ = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}
SCREAMING_SNAKE_CASE_ = os.path.join(args.output_dir , "eval_results.txt" )
with open(__lowerCAmelCase , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , __lowerCAmelCase , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 371
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305
| 0
|
"""simple docstring"""
from __future__ import annotations
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = divmod(len(__UpperCamelCase ) , 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()
A : Tuple = [float(x) for x in input("Enter the elements of first array: ").split()]
A : List[str] = [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)}")
| 350
|
from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to be of square shaped array but got a "
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
SCREAMING_SNAKE_CASE_ = (table[i][j] - total) / upper[j][j]
SCREAMING_SNAKE_CASE_ = 1
for j in range(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[int] = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotSmallConfig",
"BlenderbotSmallOnnxConfig",
],
"tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[str] = ["BlenderbotSmallTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
"BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotSmallForCausalLM",
"BlenderbotSmallForConditionalGeneration",
"BlenderbotSmallModel",
"BlenderbotSmallPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = [
"TFBlenderbotSmallForConditionalGeneration",
"TFBlenderbotSmallModel",
"TFBlenderbotSmallPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = [
"FlaxBlenderbotSmallForConditionalGeneration",
"FlaxBlenderbotSmallModel",
"FlaxBlenderbotSmallPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
A : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 351
|
from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values" )
return 4 * pi * radius**2
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values" )
return 3 * pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values" )
SCREAMING_SNAKE_CASE_ = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values" )
return 2 * pi * radius * (height + radius)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values" )
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori" )
return 4 * pow(__UpperCamelCase , 2 ) * torus_radius * tube_radius
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values" )
return length * width
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values" )
return side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values" )
return (base * height) / 2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle" )
SCREAMING_SNAKE_CASE_ = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE_ = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values" )
return base * height
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values" )
return 1 / 2 * (basea + basea) * height
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values" )
return pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values" )
return pi * radius_x * radius_y
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values" )
return 1 / 2 * diagonal_a * diagonal_a
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides" )
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 305
| 0
|
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
A : Tuple = {
"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
"bert": (BertConfig, BertForMaskedLM, BertTokenizer),
"gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def a__ ( __UpperCamelCase ):
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if args.student_type == "roberta":
SCREAMING_SNAKE_CASE_ = False
elif args.student_type == "gpt2":
SCREAMING_SNAKE_CASE_ = False
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if args.student_type == "roberta":
SCREAMING_SNAKE_CASE_ = False
def a__ ( ):
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser(description="Training" )
parser.add_argument("--force" , action="store_true" , help="Overwrite dump_path if it already exists." )
parser.add_argument(
"--dump_path" , type=__UpperCamelCase , required=__UpperCamelCase , help="The output directory (log, checkpoints, parameters, etc.)" )
parser.add_argument(
"--data_file" , type=__UpperCamelCase , required=__UpperCamelCase , help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence." , )
parser.add_argument(
"--student_type" , type=__UpperCamelCase , choices=["distilbert", "roberta", "gpt2"] , required=__UpperCamelCase , help="The student type (DistilBERT, RoBERTa)." , )
parser.add_argument("--student_config" , type=__UpperCamelCase , required=__UpperCamelCase , help="Path to the student configuration." )
parser.add_argument(
"--student_pretrained_weights" , default=__UpperCamelCase , type=__UpperCamelCase , help="Load student initialization checkpoint." )
parser.add_argument(
"--teacher_type" , choices=["bert", "roberta", "gpt2"] , required=__UpperCamelCase , help="Teacher type (BERT, RoBERTa)." )
parser.add_argument("--teacher_name" , type=__UpperCamelCase , required=__UpperCamelCase , help="The teacher model." )
parser.add_argument("--temperature" , default=2.0 , type=__UpperCamelCase , help="Temperature for the softmax temperature." )
parser.add_argument(
"--alpha_ce" , default=0.5 , type=__UpperCamelCase , help="Linear weight for the distillation loss. Must be >=0." )
parser.add_argument(
"--alpha_mlm" , default=0.0 , type=__UpperCamelCase , help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag." , )
parser.add_argument("--alpha_clm" , default=0.5 , type=__UpperCamelCase , help="Linear weight for the CLM loss. Must be >=0." )
parser.add_argument("--alpha_mse" , default=0.0 , type=__UpperCamelCase , help="Linear weight of the MSE loss. Must be >=0." )
parser.add_argument(
"--alpha_cos" , default=0.0 , type=__UpperCamelCase , help="Linear weight of the cosine embedding loss. Must be >=0." )
parser.add_argument(
"--mlm" , action="store_true" , help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
parser.add_argument(
"--mlm_mask_prop" , default=0.15 , type=__UpperCamelCase , help="Proportion of tokens for which we need to make a prediction." , )
parser.add_argument("--word_mask" , default=0.8 , type=__UpperCamelCase , help="Proportion of tokens to mask out." )
parser.add_argument("--word_keep" , default=0.1 , type=__UpperCamelCase , help="Proportion of tokens to keep." )
parser.add_argument("--word_rand" , default=0.1 , type=__UpperCamelCase , help="Proportion of tokens to randomly replace." )
parser.add_argument(
"--mlm_smoothing" , default=0.7 , type=__UpperCamelCase , help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)." , )
parser.add_argument("--token_counts" , type=__UpperCamelCase , help="The token counts in the data_file for MLM." )
parser.add_argument(
"--restrict_ce_to_mask" , action="store_true" , help="If true, compute the distillation loss only the [MLM] prediction distribution." , )
parser.add_argument(
"--freeze_pos_embs" , action="store_true" , help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only." , )
parser.add_argument(
"--freeze_token_type_embds" , action="store_true" , help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only." , )
parser.add_argument("--n_epoch" , type=__UpperCamelCase , default=3 , help="Number of pass on the whole dataset." )
parser.add_argument("--batch_size" , type=__UpperCamelCase , default=5 , help="Batch size (for each process)." )
parser.add_argument(
"--group_by_size" , action="store_false" , help="If true, group sequences that have similar length into the same batch. Default is true." , )
parser.add_argument(
"--gradient_accumulation_steps" , type=__UpperCamelCase , default=5_0 , help="Gradient accumulation for larger training batches." , )
parser.add_argument("--warmup_prop" , default=0.05 , type=__UpperCamelCase , help="Linear warmup proportion." )
parser.add_argument("--weight_decay" , default=0.0 , type=__UpperCamelCase , help="Weight decay if we apply some." )
parser.add_argument("--learning_rate" , default=5E-4 , type=__UpperCamelCase , help="The initial learning rate for Adam." )
parser.add_argument("--adam_epsilon" , default=1E-6 , type=__UpperCamelCase , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , default=5.0 , type=__UpperCamelCase , help="Max gradient norm." )
parser.add_argument("--initializer_range" , default=0.02 , type=__UpperCamelCase , help="Random initialization range." )
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=__UpperCamelCase , default="O1" , help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_gpu" , type=__UpperCamelCase , default=1 , help="Number of GPUs in the node." )
parser.add_argument("--local_rank" , type=__UpperCamelCase , default=-1 , help="Distributed training - Local rank" )
parser.add_argument("--seed" , type=__UpperCamelCase , default=5_6 , help="Random seed" )
parser.add_argument("--log_interval" , type=__UpperCamelCase , default=5_0_0 , help="Tensorboard logging interval." )
parser.add_argument("--checkpoint_interval" , type=__UpperCamelCase , default=4_0_0_0 , help="Checkpoint interval." )
SCREAMING_SNAKE_CASE_ = parser.parse_args()
sanity_checks(__UpperCamelCase )
# ARGS #
init_gpu_params(__UpperCamelCase )
set_seed(__UpperCamelCase )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
" itUse `--force` if you want to overwrite it" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(F'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(F'''Param: {args}''' )
with open(os.path.join(args.dump_path , "parameters.json" ) , "w" ) as f:
json.dump(vars(__UpperCamelCase ) , __UpperCamelCase , indent=4 )
git_log(args.dump_path )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = MODEL_CLASSES[args.student_type]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
SCREAMING_SNAKE_CASE_ = teacher_tokenizer_class.from_pretrained(args.teacher_name )
SCREAMING_SNAKE_CASE_ = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
SCREAMING_SNAKE_CASE_ = tokenizer.all_special_tokens.index(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.all_special_ids[idx]
logger.info(F'''Special tokens {special_tok_ids}''' )
SCREAMING_SNAKE_CASE_ = special_tok_ids
SCREAMING_SNAKE_CASE_ = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(F'''Loading data from {args.data_file}''' )
with open(args.data_file , "rb" ) as fp:
SCREAMING_SNAKE_CASE_ = pickle.load(__UpperCamelCase )
if args.mlm:
logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , "rb" ) as fp:
SCREAMING_SNAKE_CASE_ = pickle.load(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.maximum(__UpperCamelCase , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
SCREAMING_SNAKE_CASE_ = 0.0 # do not predict special tokens
SCREAMING_SNAKE_CASE_ = torch.from_numpy(__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = LmSeqsDataset(params=__UpperCamelCase , data=__UpperCamelCase )
logger.info("Data loader created." )
# STUDENT #
logger.info(F'''Loading student config from {args.student_config}''' )
SCREAMING_SNAKE_CASE_ = student_config_class.from_pretrained(args.student_config )
SCREAMING_SNAKE_CASE_ = True
if args.student_pretrained_weights is not None:
logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}''' )
SCREAMING_SNAKE_CASE_ = student_model_class.from_pretrained(args.student_pretrained_weights , config=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = student_model_class(__UpperCamelCase )
if args.n_gpu > 0:
student.to(F'''cuda:{args.local_rank}''' )
logger.info("Student loaded." )
# TEACHER #
SCREAMING_SNAKE_CASE_ = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=__UpperCamelCase )
if args.n_gpu > 0:
teacher.to(F'''cuda:{args.local_rank}''' )
logger.info(F'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(__UpperCamelCase , __UpperCamelCase )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(__UpperCamelCase , __UpperCamelCase )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
SCREAMING_SNAKE_CASE_ = Distiller(
params=__UpperCamelCase , dataset=__UpperCamelCase , token_probs=__UpperCamelCase , student=__UpperCamelCase , teacher=__UpperCamelCase )
distiller.train()
logger.info("Let's go get some drinks." )
if __name__ == "__main__":
main()
| 352
|
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
A : List[str] = logging.get_logger(__name__)
A : int = {
"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 lowerCamelCase (SCREAMING_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 : Dict , __magic_name__ : Dict=50_265 , __magic_name__ : str=512 , __magic_name__ : List[Any]=8 , __magic_name__ : Any=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Any=8 , __magic_name__ : Optional[int]=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Tuple=0.0 , __magic_name__ : Dict=0.0 , __magic_name__ : Optional[int]=True , __magic_name__ : Any=True , __magic_name__ : Dict="gelu" , __magic_name__ : Tuple=512 , __magic_name__ : List[str]=0.1 , __magic_name__ : List[Any]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : Union[str, Any]=1 , __magic_name__ : List[Any]=False , __magic_name__ : str=0 , __magic_name__ : Dict=1 , __magic_name__ : str=2 , __magic_name__ : Union[str, Any]=2 , **__magic_name__ : Optional[Any] , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , is_encoder_decoder=__magic_name__ , decoder_start_token_id=__magic_name__ , forced_eos_token_id=__magic_name__ , **__magic_name__ , )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def __A ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ = {0: "batch"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = 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 __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super().outputs
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __A ( self : int , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# Generate decoder inputs
SCREAMING_SNAKE_CASE_ = seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE_ = dict(**__magic_name__ , **__magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
SCREAMING_SNAKE_CASE_ = common_inputs["decoder_input_ids"].shape[1]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = decoder_seq_length + 3
SCREAMING_SNAKE_CASE_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(__magic_name__ , __magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ = min(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = max(__magic_name__ , __magic_name__ ) - min_num_layers
SCREAMING_SNAKE_CASE_ = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(__magic_name__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE_ = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(__magic_name__ , __magic_name__ ):
common_inputs["past_key_values"].append((torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) )
return common_inputs
def __A ( self : Union[str, Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE_ = seqlen + 2
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = common_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["attention_mask"], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(__magic_name__ )
]
return common_inputs
def __A ( self : Dict , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , 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
SCREAMING_SNAKE_CASE_ = tokenizer.num_special_tokens_to_add(__magic_name__ )
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__magic_name__ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE_ = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE_ = dict(tokenizer(__magic_name__ , return_tensors=__magic_name__ ) )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
elif self.task == "causal-lm":
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_causal_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : List[Any] , __magic_name__ : str , __magic_name__ : List[str] ) -> List[str]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super()._flatten_past_key_values_(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self )._flatten_past_key_values_(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
| 305
| 0
|
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 IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
SCREAMING_SNAKE_CASE_ = 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] ) )
SCREAMING_SNAKE_CASE_ = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
"image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , __magic_name__ )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(__magic_name__ , __magic_name__ )
def __A ( self : Optional[int] , **__magic_name__ : Tuple ) -> Dict:
return BertTokenizer.from_pretrained(self.tmpdirname , **__magic_name__ )
def __A ( self : Union[str, Any] , **__magic_name__ : Dict ) -> str:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **__magic_name__ )
def __A ( self : Any , **__magic_name__ : Tuple ) -> Dict:
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **__magic_name__ )
def __A ( self : Any ) -> int:
shutil.rmtree(self.tmpdirname )
def __A ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ = [Image.fromarray(np.moveaxis(__magic_name__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : List[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = AlignProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=__magic_name__ )
SCREAMING_SNAKE_CASE_ = AlignProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = AlignProcessor.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 , __magic_name__ )
self.assertIsInstance(processor_fast.tokenizer , __magic_name__ )
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 , __magic_name__ )
self.assertIsInstance(processor_fast.image_processor , __magic_name__ )
def __A ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE_ = self.get_image_processor(do_normalize=__magic_name__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE_ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__magic_name__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __magic_name__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __magic_name__ )
def __A ( self : str ) -> Any:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = AlignProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = image_processor(__magic_name__ , return_tensors="np" )
SCREAMING_SNAKE_CASE_ = processor(images=__magic_name__ , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = AlignProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , padding="max_length" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = AlignProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
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(__magic_name__ ):
processor()
def __A ( self : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = AlignProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ = processor.batch_decode(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = AlignProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 353
|
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , __magic_name__ : List[str] , __magic_name__ : int=100 , __magic_name__ : Optional[Any]=13 , __magic_name__ : Dict=30 , __magic_name__ : Tuple=2 , __magic_name__ : str=3 , __magic_name__ : str=True , __magic_name__ : Optional[int]=True , __magic_name__ : Union[str, Any]=32 , __magic_name__ : Optional[int]=4 , __magic_name__ : Dict=4 , __magic_name__ : Tuple=37 , __magic_name__ : Any="gelu" , __magic_name__ : int=0.1 , __magic_name__ : List[str]=0.1 , __magic_name__ : Optional[int]=10 , __magic_name__ : Tuple=0.02 , __magic_name__ : Optional[int]=3 , __magic_name__ : List[str]=None , __magic_name__ : Tuple=[0, 1, 2, 3] , ) -> List[str]:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = 100
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
SCREAMING_SNAKE_CASE_ = scope
SCREAMING_SNAKE_CASE_ = out_indices
SCREAMING_SNAKE_CASE_ = num_labels
# in BeiT, 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 __A ( self : Any ) -> int:
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __A ( self : Dict ) -> Optional[int]:
return BeitConfig(
vocab_size=self.vocab_size , 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=__magic_name__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def __A ( self : Optional[int] , __magic_name__ : List[str] , __magic_name__ : Optional[int] , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = BeitModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : str ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __A ( self : Dict , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : Tuple , __magic_name__ : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = self.type_sequence_label_size
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self : Tuple , __magic_name__ : Any , __magic_name__ : Optional[Any] , __magic_name__ : Tuple , __magic_name__ : int ) -> int:
SCREAMING_SNAKE_CASE_ = self.num_labels
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def __A ( self : str ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = config_and_inputs
SCREAMING_SNAKE_CASE_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = BeitModelTester(self )
SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def __A ( self : Dict ) -> List[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="BEiT does not use inputs_embeds" )
def __A ( self : List[str] ) -> Optional[Any]:
pass
@require_torch_multi_gpu
@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def __A ( self : str ) -> List[str]:
pass
def __A ( self : List[Any] ) -> List[str]:
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(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def __A ( self : Union[str, Any] ) -> int:
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(__magic_name__ )
SCREAMING_SNAKE_CASE_ = inspect.signature(model.forward )
# 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] , __magic_name__ )
def __A ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__magic_name__ )
def __A ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def __A ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__magic_name__ )
def __A ( self : int ) -> Optional[int]:
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : Any ) -> Tuple:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.gradient_checkpointing_enable()
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = _config_zero_init(__magic_name__ )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ = model_class(config=__magic_name__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def __A ( self : int ) -> Optional[int]:
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ = BeitModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self : List[Any] ) -> str:
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def __A ( self : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).pixel_values.to(__magic_name__ )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE_ = torch.ones((1, 196) , dtype=torch.bool ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(pixel_values=__magic_name__ , bool_masked_pos=__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , __magic_name__ , atol=1e-2 ) )
@slow
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 1_000) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([-1.2385, -1.0987, -1.0108] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 281
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to(
__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 21_841) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([1.6881, -0.2787, 0.5901] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 2_396
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Tuple ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = version.parse(PIL.__version__ ) < version.parse("9.0.0" )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=__magic_name__ , )
else:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=__magic_name__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __magic_name__ , atol=1e-4 ) )
@slow
def __A ( self : List[str] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ , target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE_ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
| 305
| 0
|
"""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
A : Union[str, Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCamelCase (datasets.BuilderConfig ):
"""simple docstring"""
lowerCamelCase__ = None
lowerCamelCase__ = '''utf-8'''
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = True # deprecated
lowerCamelCase__ = None # deprecated
lowerCamelCase__ = 1_0 << 2_0 # 10MB
lowerCamelCase__ = None
class lowerCamelCase (datasets.ArrowBasedBuilder ):
"""simple docstring"""
lowerCamelCase__ = JsonConfig
def __A ( self : Optional[Any] ) -> List[Any]:
if self.config.block_size is not None:
logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead" )
SCREAMING_SNAKE_CASE_ = 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 __A ( self : Dict , __magic_name__ : List[str] ) -> List[Any]:
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}''' )
SCREAMING_SNAKE_CASE_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__magic_name__ , (str, list, tuple) ):
SCREAMING_SNAKE_CASE_ = data_files
if isinstance(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = [files]
SCREAMING_SNAKE_CASE_ = [dl_manager.iter_files(__magic_name__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
SCREAMING_SNAKE_CASE_ = []
for split_name, files in data_files.items():
if isinstance(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = [files]
SCREAMING_SNAKE_CASE_ = [dl_manager.iter_files(__magic_name__ ) for file in files]
splits.append(datasets.SplitGenerator(name=__magic_name__ , gen_kwargs={"files": files} ) )
return splits
def __A ( self : int , __magic_name__ : pa.Table ) -> pa.Table:
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
SCREAMING_SNAKE_CASE_ = self.config.features.arrow_schema.field(__magic_name__ ).type
SCREAMING_SNAKE_CASE_ = pa_table.append_column(__magic_name__ , pa.array([None] * len(__magic_name__ ) , type=__magic_name__ ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
SCREAMING_SNAKE_CASE_ = table_cast(__magic_name__ , self.config.features.arrow_schema )
return pa_table
def __A ( self : Tuple , __magic_name__ : Any ) -> int:
for file_idx, file in enumerate(itertools.chain.from_iterable(__magic_name__ ) ):
# 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(__magic_name__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
SCREAMING_SNAKE_CASE_ = json.load(__magic_name__ )
# We keep only the field we are interested in
SCREAMING_SNAKE_CASE_ = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(__magic_name__ , (list, tuple) ):
SCREAMING_SNAKE_CASE_ = set().union(*[row.keys() for row in dataset] )
SCREAMING_SNAKE_CASE_ = {col: [row.get(__magic_name__ ) for row in dataset] for col in keys}
else:
SCREAMING_SNAKE_CASE_ = dataset
SCREAMING_SNAKE_CASE_ = pa.Table.from_pydict(__magic_name__ )
yield file_idx, self._cast_table(__magic_name__ )
# If the file has one json object per line
else:
with open(__magic_name__ , "rb" ) as f:
SCREAMING_SNAKE_CASE_ = 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
SCREAMING_SNAKE_CASE_ = max(self.config.chunksize // 32 , 16 << 10 )
SCREAMING_SNAKE_CASE_ = (
self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
)
while True:
SCREAMING_SNAKE_CASE_ = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(__magic_name__ )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
SCREAMING_SNAKE_CASE_ = batch.decode(self.config.encoding , errors=__magic_name__ ).encode("utf-8" )
try:
while True:
try:
SCREAMING_SNAKE_CASE_ = paj.read_json(
io.BytesIO(__magic_name__ ) , read_options=paj.ReadOptions(block_size=__magic_name__ ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(__magic_name__ , pa.ArrowInvalid )
and "straddling" not in str(__magic_name__ )
or block_size > len(__magic_name__ )
):
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(__magic_name__ )} 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(
__magic_name__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
SCREAMING_SNAKE_CASE_ = json.load(__magic_name__ )
except json.JSONDecodeError:
logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__ )}: {e}''' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(__magic_name__ , __magic_name__ ): # list is the only sequence type supported in JSON
try:
SCREAMING_SNAKE_CASE_ = set().union(*[row.keys() for row in dataset] )
SCREAMING_SNAKE_CASE_ = {col: [row.get(__magic_name__ ) for row in dataset] for col in keys}
SCREAMING_SNAKE_CASE_ = pa.Table.from_pydict(__magic_name__ )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__ )}: {e}''' )
raise ValueError(F'''Not able to read records in the JSON file at {file}.''' ) from None
yield file_idx, self._cast_table(__magic_name__ )
break
else:
logger.error(F'''Failed to read file \'{file}\' with error {type(__magic_name__ )}: {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(__magic_name__ )
batch_idx += 1
| 354
|
from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = self._trie
for char in text:
if char not in trie:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = trie[char]
SCREAMING_SNAKE_CASE_ = True
def __A ( self : Union[str, Any] , __magic_name__ : str ) -> tuple | list:
SCREAMING_SNAKE_CASE_ = self._trie
for char in prefix:
if char in trie:
SCREAMING_SNAKE_CASE_ = trie[char]
else:
return []
return self._elements(__magic_name__ )
def __A ( self : int , __magic_name__ : dict ) -> tuple:
SCREAMING_SNAKE_CASE_ = []
for c, v in d.items():
SCREAMING_SNAKE_CASE_ = [" "] if c == END else [(c + s) for s in self._elements(__magic_name__ )]
result.extend(__magic_name__ )
return tuple(__magic_name__ )
A : Union[str, Any] = Trie()
A : Optional[int] = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
trie.insert_word(word)
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = trie.find_word(__UpperCamelCase )
return tuple(string + word for word in suffixes )
def a__ ( ):
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 305
| 0
|
from __future__ import annotations
import pandas as pd
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = burst_time[i]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 9_9_9_9_9_9_9_9_9
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(__UpperCamelCase ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
SCREAMING_SNAKE_CASE_ = remaining_time[j]
SCREAMING_SNAKE_CASE_ = j
SCREAMING_SNAKE_CASE_ = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
SCREAMING_SNAKE_CASE_ = remaining_time[short]
if minm == 0:
SCREAMING_SNAKE_CASE_ = 9_9_9_9_9_9_9_9_9
if remaining_time[short] == 0:
complete += 1
SCREAMING_SNAKE_CASE_ = False
# Find finish time of current process
SCREAMING_SNAKE_CASE_ = increment_time + 1
# Calculate waiting time
SCREAMING_SNAKE_CASE_ = finish_time - arrival_time[short]
SCREAMING_SNAKE_CASE_ = finar - burst_time[short]
if waiting_time[short] < 0:
SCREAMING_SNAKE_CASE_ = 0
# Increment time
increment_time += 1
return waiting_time
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = burst_time[i] + waiting_time[i]
return turn_around_time
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = total_waiting_time + waiting_time[i]
SCREAMING_SNAKE_CASE_ = total_turn_around_time + turn_around_time[i]
print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}''' )
print("Average turn around time =" , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print("Enter how many process you want to analyze")
A : int = int(input())
A : Union[str, Any] = [0] * no_of_processes
A : Optional[int] = [0] * no_of_processes
A : List[Any] = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("Enter the arrival time and burst time for process:--" + str(i + 1))
A : Tuple = map(int, input().split())
A : Any = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
A : List[str] = burst_time
A : Optional[Any] = no_of_processes
A : Any = waiting_time
A : List[str] = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
A : Any = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"Process",
"BurstTime",
"ArrivalTime",
"WaitingTime",
"TurnAroundTime",
],
)
# Printing the dataFrame
pd.set_option("display.max_rows", fcfs.shape[0] + 1)
print(fcfs)
| 355
|
from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name # process name
SCREAMING_SNAKE_CASE_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
SCREAMING_SNAKE_CASE_ = arrival_time
SCREAMING_SNAKE_CASE_ = burst_time # remaining burst time
SCREAMING_SNAKE_CASE_ = 0 # total time of the process wait in ready queue
SCREAMING_SNAKE_CASE_ = 0 # time from arrival time to completion time
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Tuple , __magic_name__ : int , __magic_name__ : list[int] , __magic_name__ : deque[Process] , __magic_name__ : int , ) -> None:
# total number of mlfq's queues
SCREAMING_SNAKE_CASE_ = number_of_queues
# time slice of queues that round robin algorithm applied
SCREAMING_SNAKE_CASE_ = time_slices
# unfinished process is in this ready_queue
SCREAMING_SNAKE_CASE_ = queue
# current time
SCREAMING_SNAKE_CASE_ = current_time
# finished process is in this sequence queue
SCREAMING_SNAKE_CASE_ = deque()
def __A ( self : Dict ) -> list[str]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def __A ( self : Tuple , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def __A ( self : str , __magic_name__ : deque[Process] ) -> list[int]:
return [q.burst_time for q in queue]
def __A ( self : Optional[Any] , __magic_name__ : Process ) -> int:
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def __A ( self : Optional[Any] , __magic_name__ : deque[Process] ) -> deque[Process]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of finished process
while len(__magic_name__ ) != 0:
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(__magic_name__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
SCREAMING_SNAKE_CASE_ = 0
# set the process's turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# set the completion time
SCREAMING_SNAKE_CASE_ = self.current_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def __A ( self : Any , __magic_name__ : deque[Process] , __magic_name__ : int ) -> tuple[deque[Process], deque[Process]]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(__magic_name__ ) ):
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(__magic_name__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
SCREAMING_SNAKE_CASE_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(__magic_name__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
SCREAMING_SNAKE_CASE_ = 0
# set the finish time
SCREAMING_SNAKE_CASE_ = self.current_time
# update the process' turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def __A ( self : Any ) -> deque[Process]:
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
A : Dict = Process("P1", 0, 53)
A : str = Process("P2", 0, 17)
A : List[Any] = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Dict = 3
A : Any = [17, 25]
A : Dict = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
A : Union[str, Any] = Process("P1", 0, 53)
A : Any = Process("P2", 0, 17)
A : Dict = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Optional[int] = 3
A : int = [17, 25]
A : Union[str, Any] = deque([Pa, Pa, Pa, Pa])
A : Tuple = MLFQ(number_of_queues, time_slices, queue, 0)
A : Tuple = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| 305
| 0
|
"""simple docstring"""
import math
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__UpperCamelCase )
if number < 1:
SCREAMING_SNAKE_CASE_ = F'''Input value of [number={number}] must be > 0'''
raise ValueError(__UpperCamelCase )
elif number == 1:
return 3
elif number == 2:
return 5
else:
SCREAMING_SNAKE_CASE_ = int(math.log(number // 3 , 2 ) ) + 2
SCREAMING_SNAKE_CASE_ = [3, 5]
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = 3
for block in range(1 , __UpperCamelCase ):
for _ in range(__UpperCamelCase ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
A : Dict = 0
try:
A : Optional[Any] = proth(number)
except ValueError:
print(f"ValueError: there is no {number}th Proth number")
continue
print(f"The {number}th Proth number: {value}")
| 356
|
import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 305
| 0
|
from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A : Optional[Any] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''pixel_values''']
def __init__( self : Any , __magic_name__ : bool = True , __magic_name__ : Optional[Dict[str, int]] = None , __magic_name__ : PILImageResampling = PILImageResampling.BILINEAR , __magic_name__ : bool = True , __magic_name__ : Dict[str, int] = None , __magic_name__ : bool = True , __magic_name__ : Union[int, float] = 1 / 255 , __magic_name__ : bool = True , __magic_name__ : Optional[Union[float, List[float]]] = None , __magic_name__ : Optional[Union[float, List[float]]] = None , **__magic_name__ : int , ) -> None:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = size if size is not None else {"shortest_edge": 256}
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
SCREAMING_SNAKE_CASE_ = crop_size if crop_size is not None else {"height": 224, "width": 224}
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ , param_name="crop_size" )
SCREAMING_SNAKE_CASE_ = do_resize
SCREAMING_SNAKE_CASE_ = size
SCREAMING_SNAKE_CASE_ = resample
SCREAMING_SNAKE_CASE_ = do_center_crop
SCREAMING_SNAKE_CASE_ = crop_size
SCREAMING_SNAKE_CASE_ = do_rescale
SCREAMING_SNAKE_CASE_ = rescale_factor
SCREAMING_SNAKE_CASE_ = do_normalize
SCREAMING_SNAKE_CASE_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __A ( self : Any , __magic_name__ : np.ndarray , __magic_name__ : Dict[str, int] , __magic_name__ : PILImageResampling = PILImageResampling.BICUBIC , __magic_name__ : Optional[Union[str, ChannelDimension]] = None , **__magic_name__ : str , ) -> np.ndarray:
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
SCREAMING_SNAKE_CASE_ = get_resize_output_image_size(__magic_name__ , size=size["shortest_edge"] , default_to_square=__magic_name__ )
return resize(__magic_name__ , size=__magic_name__ , resample=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __A ( self : Tuple , __magic_name__ : np.ndarray , __magic_name__ : Dict[str, int] , __magic_name__ : Optional[Union[str, ChannelDimension]] = None , **__magic_name__ : Any , ) -> np.ndarray:
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''' )
return center_crop(__magic_name__ , size=(size["height"], size["width"]) , data_format=__magic_name__ , **__magic_name__ )
def __A ( self : Dict , __magic_name__ : np.ndarray , __magic_name__ : float , __magic_name__ : Optional[Union[str, ChannelDimension]] = None , **__magic_name__ : Tuple ) -> np.ndarray:
return rescale(__magic_name__ , scale=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __A ( self : Any , __magic_name__ : np.ndarray , __magic_name__ : Union[float, List[float]] , __magic_name__ : Union[float, List[float]] , __magic_name__ : Optional[Union[str, ChannelDimension]] = None , **__magic_name__ : List[str] , ) -> np.ndarray:
return normalize(__magic_name__ , mean=__magic_name__ , std=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __A ( self : Optional[Any] , __magic_name__ : ImageInput , __magic_name__ : Optional[bool] = None , __magic_name__ : Dict[str, int] = None , __magic_name__ : PILImageResampling = None , __magic_name__ : bool = None , __magic_name__ : Dict[str, int] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[float] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[Union[float, List[float]]] = None , __magic_name__ : Optional[Union[float, List[float]]] = None , __magic_name__ : Optional[Union[str, TensorType]] = None , __magic_name__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **__magic_name__ : Tuple , ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE_ = size if size is not None else self.size
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
SCREAMING_SNAKE_CASE_ = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE_ = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE_ = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ , param_name="crop_size" )
SCREAMING_SNAKE_CASE_ = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE_ = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE_ = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE_ = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE_ = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE_ = make_list_of_images(__magic_name__ )
if not valid_images(__magic_name__ ):
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." )
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.
SCREAMING_SNAKE_CASE_ = [to_numpy_array(__magic_name__ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE_ = [self.resize(image=__magic_name__ , size=__magic_name__ , resample=__magic_name__ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE_ = [self.center_crop(image=__magic_name__ , size=__magic_name__ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE_ = [self.rescale(image=__magic_name__ , scale=__magic_name__ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE_ = [self.normalize(image=__magic_name__ , mean=__magic_name__ , std=__magic_name__ ) for image in images]
SCREAMING_SNAKE_CASE_ = [to_channel_dimension_format(__magic_name__ , __magic_name__ ) for image in images]
SCREAMING_SNAKE_CASE_ = {"pixel_values": images}
return BatchFeature(data=__magic_name__ , tensor_type=__magic_name__ )
def __A ( self : Optional[Any] , __magic_name__ : Dict , __magic_name__ : List[Tuple] = None ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = target_sizes.numpy()
SCREAMING_SNAKE_CASE_ = []
for idx in range(len(__magic_name__ ) ):
SCREAMING_SNAKE_CASE_ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=__magic_name__ )
SCREAMING_SNAKE_CASE_ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = logits.argmax(dim=1 )
SCREAMING_SNAKE_CASE_ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 357
|
from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "08x" )[-8:]
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
return little_endian_hex
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = b""
for char in message:
bit_string += format(__UpperCamelCase , "08b" ).encode("utf-8" )
SCREAMING_SNAKE_CASE_ = format(len(__UpperCamelCase ) , "064b" ).encode("utf-8" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__UpperCamelCase ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) % 5_1_2 != 0:
raise ValueError("Input must have length that's a multiple of 512" )
for pos in range(0 , len(__UpperCamelCase ) , 5_1_2 ):
SCREAMING_SNAKE_CASE_ = bit_string[pos : pos + 5_1_2]
SCREAMING_SNAKE_CASE_ = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "032b" )
SCREAMING_SNAKE_CASE_ = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__UpperCamelCase , 2 )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return (a + b) % 2**3_2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
if shift < 0:
raise ValueError("Shift must be non-negative" )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = preprocess(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
SCREAMING_SNAKE_CASE_ = 0X67452301
SCREAMING_SNAKE_CASE_ = 0Xefcdab89
SCREAMING_SNAKE_CASE_ = 0X98badcfe
SCREAMING_SNAKE_CASE_ = 0X10325476
SCREAMING_SNAKE_CASE_ = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = aa
SCREAMING_SNAKE_CASE_ = ba
SCREAMING_SNAKE_CASE_ = ca
SCREAMING_SNAKE_CASE_ = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = d ^ (b & (c ^ d))
SCREAMING_SNAKE_CASE_ = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = c ^ (d & (b ^ c))
SCREAMING_SNAKE_CASE_ = (5 * i + 1) % 1_6
elif i <= 4_7:
SCREAMING_SNAKE_CASE_ = b ^ c ^ d
SCREAMING_SNAKE_CASE_ = (3 * i + 5) % 1_6
else:
SCREAMING_SNAKE_CASE_ = c ^ (b | not_aa(__UpperCamelCase ))
SCREAMING_SNAKE_CASE_ = (7 * i) % 1_6
SCREAMING_SNAKE_CASE_ = (f + a + added_consts[i] + block_words[g]) % 2**3_2
SCREAMING_SNAKE_CASE_ = d
SCREAMING_SNAKE_CASE_ = c
SCREAMING_SNAKE_CASE_ = b
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , left_rotate_aa(__UpperCamelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
def a__ ( ):
return [
a * b * (1_0_0_0 - a - b)
for a in range(1 , 9_9_9 )
for b in range(__UpperCamelCase , 9_9_9 )
if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"{solution() = }")
| 358
|
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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ) -> Any:
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = BlipImageProcessor()
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )
SCREAMING_SNAKE_CASE_ = BlipaProcessor(__magic_name__ , __magic_name__ )
processor.save_pretrained(self.tmpdirname )
def __A ( self : str , **__magic_name__ : int ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).tokenizer
def __A ( self : Dict , **__magic_name__ : List[Any] ) -> int:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).image_processor
def __A ( self : int ) -> Any:
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ = [Image.fromarray(np.moveaxis(__magic_name__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE_ = self.get_image_processor(do_normalize=__magic_name__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE_ = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__magic_name__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __magic_name__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __magic_name__ )
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = image_processor(__magic_name__ , return_tensors="np" )
SCREAMING_SNAKE_CASE_ = processor(images=__magic_name__ , 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 : str ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_token_type_ids=__magic_name__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__magic_name__ ):
processor()
def __A ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ = processor.batch_decode(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 305
| 0
|
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Union[str, Any] = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''data2vec-audio'''
def __init__( self : Any , __magic_name__ : List[str]=32 , __magic_name__ : Tuple=768 , __magic_name__ : str=12 , __magic_name__ : Tuple=12 , __magic_name__ : Tuple=3_072 , __magic_name__ : str="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : str=0.1 , __magic_name__ : List[Any]=0.1 , __magic_name__ : Union[str, Any]=0.0 , __magic_name__ : List[str]=0.1 , __magic_name__ : Union[str, Any]=0.1 , __magic_name__ : List[Any]=0.02 , __magic_name__ : List[str]=1e-5 , __magic_name__ : Union[str, Any]="gelu" , __magic_name__ : int=(512, 512, 512, 512, 512, 512, 512) , __magic_name__ : Optional[int]=(5, 2, 2, 2, 2, 2, 2) , __magic_name__ : List[Any]=(10, 3, 3, 3, 3, 2, 2) , __magic_name__ : Tuple=False , __magic_name__ : Optional[int]=16 , __magic_name__ : List[Any]=19 , __magic_name__ : Optional[Any]=5 , __magic_name__ : str=0.05 , __magic_name__ : str=10 , __magic_name__ : Dict=2 , __magic_name__ : int=0.0 , __magic_name__ : Optional[int]=10 , __magic_name__ : List[str]=0 , __magic_name__ : List[Any]="sum" , __magic_name__ : Union[str, Any]=False , __magic_name__ : Optional[int]=False , __magic_name__ : str=256 , __magic_name__ : Dict=(512, 512, 512, 512, 1_500) , __magic_name__ : List[Any]=(5, 3, 3, 1, 1) , __magic_name__ : Optional[Any]=(1, 2, 3, 1, 1) , __magic_name__ : int=512 , __magic_name__ : Tuple=0 , __magic_name__ : Optional[int]=1 , __magic_name__ : Optional[int]=2 , __magic_name__ : Dict=False , __magic_name__ : Tuple=3 , __magic_name__ : Tuple=2 , __magic_name__ : str=3 , __magic_name__ : Any=None , **__magic_name__ : Union[str, Any] , ) -> Union[str, Any]:
super().__init__(**__magic_name__ , pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ )
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = feat_extract_activation
SCREAMING_SNAKE_CASE_ = list(__magic_name__ )
SCREAMING_SNAKE_CASE_ = list(__magic_name__ )
SCREAMING_SNAKE_CASE_ = list(__magic_name__ )
SCREAMING_SNAKE_CASE_ = conv_bias
SCREAMING_SNAKE_CASE_ = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE_ = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE_ = conv_pos_kernel_size
SCREAMING_SNAKE_CASE_ = len(self.conv_dim )
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = feat_proj_dropout
SCREAMING_SNAKE_CASE_ = final_dropout
SCREAMING_SNAKE_CASE_ = layerdrop
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE_ = mask_time_prob
SCREAMING_SNAKE_CASE_ = mask_time_length
SCREAMING_SNAKE_CASE_ = mask_time_min_masks
SCREAMING_SNAKE_CASE_ = mask_feature_prob
SCREAMING_SNAKE_CASE_ = mask_feature_length
SCREAMING_SNAKE_CASE_ = mask_feature_min_masks
# ctc loss
SCREAMING_SNAKE_CASE_ = ctc_loss_reduction
SCREAMING_SNAKE_CASE_ = ctc_zero_infinity
# adapter
SCREAMING_SNAKE_CASE_ = add_adapter
SCREAMING_SNAKE_CASE_ = adapter_kernel_size
SCREAMING_SNAKE_CASE_ = adapter_stride
SCREAMING_SNAKE_CASE_ = num_adapter_layers
SCREAMING_SNAKE_CASE_ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE_ = list(__magic_name__ )
SCREAMING_SNAKE_CASE_ = list(__magic_name__ )
SCREAMING_SNAKE_CASE_ = list(__magic_name__ )
SCREAMING_SNAKE_CASE_ = xvector_output_dim
@property
def __A ( self : Tuple ) -> Optional[int]:
return math.prod(self.conv_stride )
| 359
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ["DPTFeatureExtractor"]
A : str = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[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
A : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = BertJapaneseTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
def __A ( self : List[Any] ) -> Dict:
super().setUp()
SCREAMING_SNAKE_CASE_ = [
"[UNK]",
"[CLS]",
"[SEP]",
"こんにちは",
"こん",
"にちは",
"ばんは",
"##こん",
"##にちは",
"##ばんは",
"世界",
"##世界",
"、",
"##、",
"。",
"##。",
]
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def __A ( self : Tuple , __magic_name__ : str ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "こんにちは、世界。 \nこんばんは、世界。"
SCREAMING_SNAKE_CASE_ = "こんにちは 、 世界 。 こんばんは 、 世界 。"
return input_text, output_text
def __A ( self : int , __magic_name__ : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.get_input_output_texts(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.decode(__magic_name__ , clean_up_tokenization_spaces=__magic_name__ )
return text, ids
def __A ( self : List[str] ) -> List[Any]:
pass # TODO add if relevant
def __A ( self : Any ) -> Optional[int]:
pass # TODO add if relevant
def __A ( self : Dict ) -> int:
pass # TODO add if relevant
def __A ( self : List[str] ) -> Any:
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file )
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize("こんにちは、世界。\nこんばんは、世界。" )
self.assertListEqual(__magic_name__ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def __A ( self : Optional[int] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="mecab" )
self.assertIsNotNone(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "こんにちは、世界。\nこんばんは、世界。"
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , "tokenizer.bin" )
with open(__magic_name__ , "wb" ) as handle:
pickle.dump(__magic_name__ , __magic_name__ )
with open(__magic_name__ , "rb" ) as handle:
SCREAMING_SNAKE_CASE_ = pickle.load(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer_new.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : Tuple ) -> str:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
def __A ( self : Dict ) -> List[Any]:
try:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(mecab_dic="unidic_lite" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
def __A ( self : Any ) -> Optional[Any]:
try:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(mecab_dic="unidic" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
def __A ( self : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(do_lower_case=__magic_name__ , mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップルストア", "で", "iphone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
def __A ( self : int ) -> Any:
try:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(
do_lower_case=__magic_name__ , normalize_text=__magic_name__ , mecab_option="-d /usr/local/lib/mecab/dic/jumandic" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れた", "\u3000", "。"] , )
def __A ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE_ = MecabTokenizer(normalize_text=__magic_name__ , mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れ", "た", " ", "。"] , )
@require_sudachi
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="sudachi" )
self.assertIsNotNone(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "こんにちは、世界。\nこんばんは、世界。"
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , "tokenizer.bin" )
with open(__magic_name__ , "wb" ) as handle:
pickle.dump(__magic_name__ , __magic_name__ )
with open(__magic_name__ , "rb" ) as handle:
SCREAMING_SNAKE_CASE_ = pickle.load(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer_new.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
@require_sudachi
def __A ( self : Any ) -> int:
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , [" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "] , )
@require_sudachi
def __A ( self : Optional[int] ) -> Any:
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="A" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国", "人", "参政", "権"] )
@require_sudachi
def __A ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="B" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国人", "参政権"] )
@require_sudachi
def __A ( self : List[Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="C" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国人参政権"] )
@require_sudachi
def __A ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(do_lower_case=__magic_name__ , sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , [" ", "\t", "アップル", "ストア", "で", "iphone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "] , )
@require_sudachi
def __A ( self : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(normalize_text=__magic_name__ , sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , [" ", "\t", "ｱｯﾌﾟﾙ", "ストア", "で", "iPhone", "８", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", "\u3000", "。", " ", " "] , )
@require_sudachi
def __A ( self : Optional[Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = SudachiTokenizer(trim_whitespace=__magic_name__ , sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
@require_jumanpp
def __A ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="jumanpp" )
self.assertIsNotNone(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "こんにちは、世界。\nこんばんは、世界。"
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , "tokenizer.bin" )
with open(__magic_name__ , "wb" ) as handle:
pickle.dump(__magic_name__ , __magic_name__ )
with open(__magic_name__ , "rb" ) as handle:
SCREAMING_SNAKE_CASE_ = pickle.load(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer_new.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
@require_jumanpp
def __A ( self : Union[str, Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , )
@require_jumanpp
def __A ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = JumanppTokenizer(do_lower_case=__magic_name__ )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iphone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , )
@require_jumanpp
def __A ( self : Optional[Any] ) -> Any:
SCREAMING_SNAKE_CASE_ = JumanppTokenizer(normalize_text=__magic_name__ )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["ｱ", "ｯ", "ﾌ", "ﾟ", "ﾙ", "ストア", "で", "iPhone", "８", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , )
@require_jumanpp
def __A ( self : Union[str, Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = JumanppTokenizer(trim_whitespace=__magic_name__ )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れた", "。"] , )
@require_jumanpp
def __A ( self : List[str] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("ありがとうございますm(_ _)ｍ見つけるのが大変です。" ) , ["ありがとう", "ございます", "m(_ _)m", "見つける", "の", "が", "大変です", "。"] , )
def __A ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = ["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"]
SCREAMING_SNAKE_CASE_ = {}
for i, token in enumerate(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = i
SCREAMING_SNAKE_CASE_ = WordpieceTokenizer(vocab=__magic_name__ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("こんにちは" ) , ["こんにちは"] )
self.assertListEqual(tokenizer.tokenize("こんばんは" ) , ["こん", "##ばんは"] )
self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは" ) , ["こん", "##ばんは", "[UNK]", "こんにちは"] )
def __A ( self : List[str] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp" )
SCREAMING_SNAKE_CASE_ = tokenizer.subword_tokenizer
SCREAMING_SNAKE_CASE_ = subword_tokenizer.tokenize("国境 の 長い トンネル を 抜ける と 雪国 であった 。" )
self.assertListEqual(__magic_name__ , ["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"] )
SCREAMING_SNAKE_CASE_ = subword_tokenizer.tokenize("こんばんは こんばん にち は こんにちは" )
self.assertListEqual(__magic_name__ , ["▁こん", "ばん", "は", "▁こん", "ばん", "▁に", "ち", "▁は", "▁こんにちは"] )
def __A ( self : Optional[int] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese" )
SCREAMING_SNAKE_CASE_ = tokenizer.encode("ありがとう。" , add_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.encode("どういたしまして。" , add_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.build_inputs_with_special_tokens(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.build_inputs_with_special_tokens(__magic_name__ , __magic_name__ )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = BertJapaneseTokenizer
lowerCamelCase__ = False
def __A ( self : Dict ) -> List[Any]:
super().setUp()
SCREAMING_SNAKE_CASE_ = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def __A ( self : Optional[Any] , **__magic_name__ : Dict ) -> Any:
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="character" , **__magic_name__ )
def __A ( self : Tuple , __magic_name__ : int ) -> Any:
SCREAMING_SNAKE_CASE_ = "こんにちは、世界。 \nこんばんは、世界。"
SCREAMING_SNAKE_CASE_ = "こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
return input_text, output_text
def __A ( self : Optional[Any] ) -> str:
pass # TODO add if relevant
def __A ( self : List[Any] ) -> List[str]:
pass # TODO add if relevant
def __A ( self : List[str] ) -> Optional[Any]:
pass # TODO add if relevant
def __A ( self : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="character" )
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。" )
self.assertListEqual(
__magic_name__ , ["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__magic_name__ ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def __A ( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE_ = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
SCREAMING_SNAKE_CASE_ = {}
for i, token in enumerate(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = i
SCREAMING_SNAKE_CASE_ = CharacterTokenizer(vocab=__magic_name__ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("こんにちは" ) , ["こ", "ん", "に", "ち", "は"] )
self.assertListEqual(tokenizer.tokenize("こんにちほ" ) , ["こ", "ん", "に", "ち", "[UNK]"] )
def __A ( self : Dict ) -> str:
SCREAMING_SNAKE_CASE_ = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char" )
SCREAMING_SNAKE_CASE_ = tokenizer.encode("ありがとう。" , add_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.encode("どういたしまして。" , add_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.build_inputs_with_special_tokens(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.build_inputs_with_special_tokens(__magic_name__ , __magic_name__ )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = "cl-tohoku/bert-base-japanese"
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(__magic_name__ )
self.assertIsInstance(__magic_name__ , __magic_name__ )
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[str] ) -> Any:
SCREAMING_SNAKE_CASE_ = "cl-tohoku/bert-base-japanese"
with self.assertLogs("transformers" , level="WARNING" ) as cm:
BertTokenizer.from_pretrained(__magic_name__ )
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class this function"
" is called from." ) )
SCREAMING_SNAKE_CASE_ = "bert-base-cased"
with self.assertLogs("transformers" , level="WARNING" ) as cm:
BertJapaneseTokenizer.from_pretrained(__magic_name__ )
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class this function"
" is called from." ) )
| 360
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
A : int = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Union[str, Any] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 305
| 0
|
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
A : Dict = TypeVar("KT")
A : List[Any] = TypeVar("VT")
class lowerCamelCase (Generic[KT, VT] ):
"""simple docstring"""
def __init__( self : List[Any] , __magic_name__ : KT | str = "root" , __magic_name__ : VT | None = None ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = key
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ = []
def __repr__( self : Any ) -> str:
"""simple docstring"""
return F'''Node({self.key}: {self.value})'''
@property
def __A ( self : Any ) -> int:
"""simple docstring"""
return len(self.forward )
class lowerCamelCase (Generic[KT, VT] ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __magic_name__ : float = 0.5 , __magic_name__ : int = 16 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = Node[KT, VT]()
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = p
SCREAMING_SNAKE_CASE_ = max_level
def __str__( self : Tuple ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = list(self )
if len(__magic_name__ ) == 0:
return F'''SkipList(level={self.level})'''
SCREAMING_SNAKE_CASE_ = max((len(str(__magic_name__ ) ) for item in items) , default=4 )
SCREAMING_SNAKE_CASE_ = max(__magic_name__ , 4 ) + 4
SCREAMING_SNAKE_CASE_ = self.head
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = node.forward.copy()
lines.append(F'''[{node.key}]'''.ljust(__magic_name__ , "-" ) + "* " * len(__magic_name__ ) )
lines.append(" " * label_size + "| " * len(__magic_name__ ) )
while len(node.forward ) != 0:
SCREAMING_SNAKE_CASE_ = node.forward[0]
lines.append(
F'''[{node.key}]'''.ljust(__magic_name__ , "-" )
+ " ".join(str(n.key ) if n.key == node.key else "|" for n in forwards ) )
lines.append(" " * label_size + "| " * len(__magic_name__ ) )
SCREAMING_SNAKE_CASE_ = node.forward
lines.append("None".ljust(__magic_name__ ) + "* " * len(__magic_name__ ) )
return F'''SkipList(level={self.level})\n''' + "\n".join(__magic_name__ )
def __iter__( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
SCREAMING_SNAKE_CASE_ = node.forward[0]
def __A ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def __A ( self : int , __magic_name__ : Any ) -> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
SCREAMING_SNAKE_CASE_ = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(__magic_name__ )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def __A ( self : Tuple , __magic_name__ : KT ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self._locate_node(__magic_name__ )
if node is not None:
for i, update_node in enumerate(__magic_name__ ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
SCREAMING_SNAKE_CASE_ = node.forward[i]
else:
SCREAMING_SNAKE_CASE_ = update_node.forward[:i]
def __A ( self : int , __magic_name__ : KT , __magic_name__ : VT ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self._locate_node(__magic_name__ )
if node is not None:
SCREAMING_SNAKE_CASE_ = value
else:
SCREAMING_SNAKE_CASE_ = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , __magic_name__ ):
update_vector.append(self.head )
SCREAMING_SNAKE_CASE_ = level
SCREAMING_SNAKE_CASE_ = Node(__magic_name__ , __magic_name__ )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = new_node
def __A ( self : int , __magic_name__ : VT ) -> VT | None:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self._locate_node(__magic_name__ )
if node is not None:
return node.value
return None
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
skip_list.insert("Key1" , 3 )
skip_list.insert("Key2" , 1_2 )
skip_list.insert("Key3" , 4_1 )
skip_list.insert("Key4" , -1_9 )
SCREAMING_SNAKE_CASE_ = skip_list.head
SCREAMING_SNAKE_CASE_ = {}
while node.level != 0:
SCREAMING_SNAKE_CASE_ = node.forward[0]
SCREAMING_SNAKE_CASE_ = node.value
assert len(__UpperCamelCase ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 1_2
assert all_values["Key3"] == 4_1
assert all_values["Key4"] == -1_9
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
skip_list.insert("Key1" , 1_0 )
skip_list.insert("Key1" , 1_2 )
skip_list.insert("Key5" , 7 )
skip_list.insert("Key7" , 1_0 )
skip_list.insert("Key10" , 5 )
skip_list.insert("Key7" , 7 )
skip_list.insert("Key5" , 5 )
skip_list.insert("Key10" , 1_0 )
SCREAMING_SNAKE_CASE_ = skip_list.head
SCREAMING_SNAKE_CASE_ = {}
while node.level != 0:
SCREAMING_SNAKE_CASE_ = node.forward[0]
SCREAMING_SNAKE_CASE_ = node.value
if len(__UpperCamelCase ) != 4:
print()
assert len(__UpperCamelCase ) == 4
assert all_values["Key1"] == 1_2
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 1_0
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
assert skip_list.find("Some key" ) is None
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
skip_list.insert("Key2" , 2_0 )
assert skip_list.find("Key2" ) == 2_0
skip_list.insert("Some Key" , 1_0 )
skip_list.insert("Key2" , 8 )
skip_list.insert("V" , 1_3 )
assert skip_list.find("Y" ) is None
assert skip_list.find("Key2" ) == 8
assert skip_list.find("Some Key" ) == 1_0
assert skip_list.find("V" ) == 1_3
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
skip_list.delete("Some key" )
assert len(skip_list.head.forward ) == 0
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
skip_list.insert("Key1" , 1_2 )
skip_list.insert("V" , 1_3 )
skip_list.insert("X" , 1_4 )
skip_list.insert("Key2" , 1_5 )
skip_list.delete("V" )
skip_list.delete("Key2" )
assert skip_list.find("V" ) is None
assert skip_list.find("Key2" ) is None
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
skip_list.insert("Key1" , 1_2 )
skip_list.insert("V" , 1_3 )
skip_list.insert("X" , 1_4 )
skip_list.insert("Key2" , 1_5 )
skip_list.delete("V" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) == 1_4
assert skip_list.find("Key1" ) == 1_2
assert skip_list.find("Key2" ) == 1_5
skip_list.delete("X" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) == 1_2
assert skip_list.find("Key2" ) == 1_5
skip_list.delete("Key1" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) is None
assert skip_list.find("Key2" ) == 1_5
skip_list.delete("Key2" )
assert skip_list.find("V" ) is None
assert skip_list.find("X" ) is None
assert skip_list.find("Key1" ) is None
assert skip_list.find("Key2" ) is None
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
skip_list.insert("Key1" , 1_2 )
skip_list.insert("V" , 1_3 )
skip_list.insert("X" , 1_4_2 )
skip_list.insert("Key2" , 1_5 )
skip_list.delete("X" )
def traverse_keys(__UpperCamelCase ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(__UpperCamelCase )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def a__ ( ):
def is_sorted(__UpperCamelCase ):
return all(next_item >= item for item, next_item in zip(__UpperCamelCase , lst[1:] ) )
SCREAMING_SNAKE_CASE_ = SkipList()
for i in range(1_0 ):
skip_list.insert(__UpperCamelCase , __UpperCamelCase )
assert is_sorted(list(__UpperCamelCase ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(__UpperCamelCase ) )
skip_list.insert(-1_2 , -1_2 )
skip_list.insert(7_7 , 7_7 )
assert is_sorted(list(__UpperCamelCase ) )
def a__ ( ):
for _ in range(1_0_0 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def a__ ( ):
SCREAMING_SNAKE_CASE_ = SkipList()
skip_list.insert(2 , "2" )
skip_list.insert(4 , "4" )
skip_list.insert(6 , "4" )
skip_list.insert(4 , "5" )
skip_list.insert(8 , "4" )
skip_list.insert(9 , "4" )
skip_list.delete(4 )
print(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 361
|
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_text_model'''
def __init__( self : Optional[Any] , __magic_name__ : Union[str, Any]=30_522 , __magic_name__ : Tuple=768 , __magic_name__ : List[str]=12 , __magic_name__ : Optional[Any]=12 , __magic_name__ : str=3_072 , __magic_name__ : Dict="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : Optional[int]=0.1 , __magic_name__ : List[str]=512 , __magic_name__ : Any=2 , __magic_name__ : Optional[Any]=0.02 , __magic_name__ : int=1e-12 , __magic_name__ : str=0 , __magic_name__ : Optional[Any]="absolute" , __magic_name__ : Optional[Any]=True , **__magic_name__ : Tuple , ) -> Union[str, Any]:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = pad_token_id
@classmethod
def __A ( cls : Any , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_vision_model'''
def __init__( self : List[str] , __magic_name__ : int = 3 , __magic_name__ : int = 600 , __magic_name__ : float = 2.0 , __magic_name__ : float = 3.1 , __magic_name__ : int = 8 , __magic_name__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ : List[int] = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ : List[int] = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ : List[int] = [] , __magic_name__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ : float = 0.25 , __magic_name__ : str = "swish" , __magic_name__ : int = 2_560 , __magic_name__ : str = "mean" , __magic_name__ : float = 0.02 , __magic_name__ : float = 0.001 , __magic_name__ : float = 0.99 , __magic_name__ : float = 0.2 , **__magic_name__ : List[Any] , ) -> Tuple:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = width_coefficient
SCREAMING_SNAKE_CASE_ = depth_coefficient
SCREAMING_SNAKE_CASE_ = depth_divisor
SCREAMING_SNAKE_CASE_ = kernel_sizes
SCREAMING_SNAKE_CASE_ = in_channels
SCREAMING_SNAKE_CASE_ = out_channels
SCREAMING_SNAKE_CASE_ = depthwise_padding
SCREAMING_SNAKE_CASE_ = strides
SCREAMING_SNAKE_CASE_ = num_block_repeats
SCREAMING_SNAKE_CASE_ = expand_ratios
SCREAMING_SNAKE_CASE_ = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dim
SCREAMING_SNAKE_CASE_ = pooling_type
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = batch_norm_eps
SCREAMING_SNAKE_CASE_ = batch_norm_momentum
SCREAMING_SNAKE_CASE_ = drop_connect_rate
SCREAMING_SNAKE_CASE_ = sum(__magic_name__ ) * 4
@classmethod
def __A ( cls : List[str] , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Dict ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align'''
lowerCamelCase__ = True
def __init__( self : Optional[Any] , __magic_name__ : Dict=None , __magic_name__ : List[Any]=None , __magic_name__ : str=640 , __magic_name__ : Any=1.0 , __magic_name__ : Dict=0.02 , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__(**__magic_name__ )
if text_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
SCREAMING_SNAKE_CASE_ = AlignTextConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = AlignVisionConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = projection_dim
SCREAMING_SNAKE_CASE_ = temperature_init_value
SCREAMING_SNAKE_CASE_ = initializer_range
@classmethod
def __A ( cls : List[str] , __magic_name__ : AlignTextConfig , __magic_name__ : AlignVisionConfig , **__magic_name__ : Tuple ) -> Any:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__magic_name__ )
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.text_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 305
| 0
|
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
SCREAMING_SNAKE_CASE_ = "x = y"
SCREAMING_SNAKE_CASE_ = {"y": 5}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 5, "y": 5} )
def __A ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "y = add_two(x)"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result is None
assert "tried to execute add_two" in out.out
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
def __A ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "x = 3\ny = 5"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
def __A ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "text = f'This is x: {x}.'"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(__magic_name__ , {"x": 3, "text": "This is x: 3."} )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "if x <= 3:\n y = 2\nelse:\n y = 5"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 2} )
SCREAMING_SNAKE_CASE_ = {"x": 8}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 8, "y": 5} )
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertListEqual(__magic_name__ , [3, 5] )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
def __A ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "y = x"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 3} )
def __A ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]\ntest_list[1]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = "x = 0\nfor i in range(3):\n x = i"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"range": range} , state=__magic_name__ )
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 2, "i": 2} )
| 362
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
SCREAMING_SNAKE_CASE_ = "x = y"
SCREAMING_SNAKE_CASE_ = {"y": 5}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 5, "y": 5} )
def __A ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "y = add_two(x)"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result is None
assert "tried to execute add_two" in out.out
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
def __A ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "x = 3\ny = 5"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
def __A ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "text = f'This is x: {x}.'"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(__magic_name__ , {"x": 3, "text": "This is x: 3."} )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "if x <= 3:\n y = 2\nelse:\n y = 5"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 2} )
SCREAMING_SNAKE_CASE_ = {"x": 8}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 8, "y": 5} )
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertListEqual(__magic_name__ , [3, 5] )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
def __A ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "y = x"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 3} )
def __A ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]\ntest_list[1]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = "x = 0\nfor i in range(3):\n x = i"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"range": range} , state=__magic_name__ )
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 2, "i": 2} )
| 305
| 0
|
from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = self._trie
for char in text:
if char not in trie:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = trie[char]
SCREAMING_SNAKE_CASE_ = True
def __A ( self : Union[str, Any] , __magic_name__ : str ) -> tuple | list:
SCREAMING_SNAKE_CASE_ = self._trie
for char in prefix:
if char in trie:
SCREAMING_SNAKE_CASE_ = trie[char]
else:
return []
return self._elements(__magic_name__ )
def __A ( self : int , __magic_name__ : dict ) -> tuple:
SCREAMING_SNAKE_CASE_ = []
for c, v in d.items():
SCREAMING_SNAKE_CASE_ = [" "] if c == END else [(c + s) for s in self._elements(__magic_name__ )]
result.extend(__magic_name__ )
return tuple(__magic_name__ )
A : Union[str, Any] = Trie()
A : Optional[int] = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
trie.insert_word(word)
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = trie.find_word(__UpperCamelCase )
return tuple(string + word for word in suffixes )
def a__ ( ):
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 363
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__UpperCamelCase )] )
SCREAMING_SNAKE_CASE_ = np.array(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __UpperCamelCase ) ) , x.transpose() ) , __UpperCamelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = (1, 2, 1)
SCREAMING_SNAKE_CASE_ = (1, 1, 0, 7)
SCREAMING_SNAKE_CASE_ = SARIMAX(
__UpperCamelCase , exog=__UpperCamelCase , order=__UpperCamelCase , seasonal_order=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = model.fit(disp=__UpperCamelCase , maxiter=6_0_0 , method="nm" )
SCREAMING_SNAKE_CASE_ = model_fit.predict(1 , len(__UpperCamelCase ) , exog=[test_match] )
return result[0]
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = SVR(kernel="rbf" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = regressor.predict(__UpperCamelCase )
return y_pred[0]
def a__ ( __UpperCamelCase ):
train_user.sort()
SCREAMING_SNAKE_CASE_ = np.percentile(__UpperCamelCase , 2_5 )
SCREAMING_SNAKE_CASE_ = np.percentile(__UpperCamelCase , 7_5 )
SCREAMING_SNAKE_CASE_ = qa - qa
SCREAMING_SNAKE_CASE_ = qa - (iqr * 0.1)
return low_lim
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
for i in list_vote:
if i > actual_result:
SCREAMING_SNAKE_CASE_ = not_safe + 1
else:
if abs(abs(__UpperCamelCase ) - abs(__UpperCamelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
A : Optional[Any] = pd.DataFrame(
data_input, columns=["total_user", "total_even", "days"]
)
A : Union[str, Any] = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : List[str] = normalize_df[:, 0].tolist()
A : int = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Tuple = x[: len(x) - 1]
A : str = x[len(x) - 1 :]
# for linear regression & sarimax
A : Tuple = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : List[Any] = total_date[len(total_date) - 1 :]
A : List[Any] = total_user[len(total_user) - 1 :]
A : Optional[Any] = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : Optional[int] = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : str = "" if data_safety_checker(res_vote, tst_user) else "not "
print("Today's data is {not_str}safe.")
| 305
| 0
|
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=1_0_2_4 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = [], []
SCREAMING_SNAKE_CASE_ = list(zip(__UpperCamelCase , __UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = sorted_examples[0]
def is_too_big(__UpperCamelCase ):
return tok(__UpperCamelCase , return_tensors="pt" ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
SCREAMING_SNAKE_CASE_ = new_src + " " + src
SCREAMING_SNAKE_CASE_ = new_tgt + " " + tgt
if is_too_big(__UpperCamelCase ) or is_too_big(__UpperCamelCase ): # cant fit, finalize example
finished_src.append(__UpperCamelCase )
finished_tgt.append(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = src, tgt
else: # can fit, keep adding
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(__UpperCamelCase )
finished_tgt.append(__UpperCamelCase )
return finished_src, finished_tgt
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = Path(__UpperCamelCase )
save_path.mkdir(exist_ok=__UpperCamelCase )
for split in ["train"]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
SCREAMING_SNAKE_CASE_ = [x.rstrip() for x in Path(__UpperCamelCase ).open().readlines()]
SCREAMING_SNAKE_CASE_ = [x.rstrip() for x in Path(__UpperCamelCase ).open().readlines()]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = pack_examples(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
print(F'''packed {split} split from {len(__UpperCamelCase )} examples -> {len(__UpperCamelCase )}.''' )
Path(save_path / F'''{split}.source''' ).open("w" ).write("\n".join(__UpperCamelCase ) )
Path(save_path / F'''{split}.target''' ).open("w" ).write("\n".join(__UpperCamelCase ) )
for split in ["val", "test"]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
shutil.copyfile(__UpperCamelCase , save_path / F'''{split}.source''' )
shutil.copyfile(__UpperCamelCase , save_path / F'''{split}.target''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument("--tok_name" , type=__UpperCamelCase , help="like facebook/bart-large-cnn,t5-base, etc." )
parser.add_argument("--max_seq_len" , type=__UpperCamelCase , default=1_2_8 )
parser.add_argument("--data_dir" , type=__UpperCamelCase )
parser.add_argument("--save_path" , type=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = parser.parse_args()
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(__UpperCamelCase , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 364
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
"SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwinForImageClassification",
"SwinForMaskedImageModeling",
"SwinModel",
"SwinPreTrainedModel",
"SwinBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
"TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSwinForImageClassification",
"TFSwinForMaskedImageModeling",
"TFSwinModel",
"TFSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
"""simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
A : List[str] = logging.get_logger(__name__)
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = UniSpeechSatForSequenceClassification.from_pretrained(__UpperCamelCase , config=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = downstream_dict["projector.weight"]
SCREAMING_SNAKE_CASE_ = downstream_dict["projector.bias"]
SCREAMING_SNAKE_CASE_ = downstream_dict["model.post_net.linear.weight"]
SCREAMING_SNAKE_CASE_ = downstream_dict["model.post_net.linear.bias"]
return model
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = UniSpeechSatForAudioFrameClassification.from_pretrained(__UpperCamelCase , config=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = downstream_dict["model.linear.weight"]
SCREAMING_SNAKE_CASE_ = downstream_dict["model.linear.bias"]
return model
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = UniSpeechSatForXVector.from_pretrained(__UpperCamelCase , config=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = downstream_dict["connector.weight"]
SCREAMING_SNAKE_CASE_ = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
SCREAMING_SNAKE_CASE_ = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
SCREAMING_SNAKE_CASE_ = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
SCREAMING_SNAKE_CASE_ = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
SCREAMING_SNAKE_CASE_ = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
SCREAMING_SNAKE_CASE_ = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
SCREAMING_SNAKE_CASE_ = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
SCREAMING_SNAKE_CASE_ = downstream_dict["objective.W"]
return model
@torch.no_grad()
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = torch.load(__UpperCamelCase , map_location="cpu" )
SCREAMING_SNAKE_CASE_ = checkpoint["Downstream"]
SCREAMING_SNAKE_CASE_ = UniSpeechSatConfig.from_pretrained(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = WavaVecaFeatureExtractor.from_pretrained(
__UpperCamelCase , return_attention_mask=__UpperCamelCase , do_normalize=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
SCREAMING_SNAKE_CASE_ = convert_classification(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
elif arch.endswith("ForAudioFrameClassification" ):
SCREAMING_SNAKE_CASE_ = convert_diarization(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
elif arch.endswith("ForXVector" ):
SCREAMING_SNAKE_CASE_ = convert_xvector(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
SCREAMING_SNAKE_CASE_ = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(__UpperCamelCase )
hf_model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
A : Any = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
A : Any = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 365
|
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
A : Union[str, Any] = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@classmethod
def __A ( cls : Any ) -> Dict:
SCREAMING_SNAKE_CASE_ = TOKEN
HfFolder.save_token(__magic_name__ )
@classmethod
def __A ( cls : Optional[int] ) -> Tuple:
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__magic_name__ , repo_id="test-model-flax" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
__magic_name__ , repo_id="valid_org/test-model-flax-org" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
SCREAMING_SNAKE_CASE_ = False
return models_are_equal
@require_flax
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) , max_shard_size="10KB" )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def __A ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
| 305
| 0
|
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Distribution , __magic_name__ : Tuple=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Optional[int]=0 ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = 1.0 if scale is None else scale
SCREAMING_SNAKE_CASE_ = 0.0 if loc is None else loc
super().__init__(__magic_name__ , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=__magic_name__ )] )
@property
def __A ( self : Dict ) -> Any:
return self.base_dist.mean * self.scale + self.loc
@property
def __A ( self : List[Any] ) -> Dict:
return self.base_dist.variance * self.scale**2
@property
def __A ( self : str ) -> Any:
return self.variance.sqrt()
class lowerCamelCase (nn.Module ):
"""simple docstring"""
def __init__( self : Dict , __magic_name__ : int , __magic_name__ : Dict[str, int] , __magic_name__ : Callable[..., Tuple[torch.Tensor]] , **__magic_name__ : int ) -> None:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = args_dim
SCREAMING_SNAKE_CASE_ = nn.ModuleList([nn.Linear(__magic_name__ , __magic_name__ ) for dim in args_dim.values()] )
SCREAMING_SNAKE_CASE_ = domain_map
def __A ( self : Any , __magic_name__ : torch.Tensor ) -> Tuple[torch.Tensor]:
SCREAMING_SNAKE_CASE_ = [proj(__magic_name__ ) for proj in self.proj]
return self.domain_map(*__magic_name__ )
class lowerCamelCase (nn.Module ):
"""simple docstring"""
def __init__( self : str , __magic_name__ : List[Any] ) -> str:
super().__init__()
SCREAMING_SNAKE_CASE_ = function
def __A ( self : Union[str, Any] , __magic_name__ : Tuple , *__magic_name__ : Tuple ) -> List[Any]:
return self.function(__magic_name__ , *__magic_name__ )
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
def __init__( self : List[Any] , __magic_name__ : int = 1 ) -> None:
SCREAMING_SNAKE_CASE_ = dim
SCREAMING_SNAKE_CASE_ = {k: dim * self.args_dim[k] for k in self.args_dim}
def __A ( self : int , __magic_name__ : Optional[int] ) -> Dict:
if self.dim == 1:
return self.distribution_class(*__magic_name__ )
else:
return Independent(self.distribution_class(*__magic_name__ ) , 1 )
def __A ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : Optional[torch.Tensor] = None , __magic_name__ : Optional[torch.Tensor] = None , ) -> Distribution:
SCREAMING_SNAKE_CASE_ = self._base_distribution(__magic_name__ )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(__magic_name__ , loc=__magic_name__ , scale=__magic_name__ , event_dim=self.event_dim )
@property
def __A ( self : str ) -> Tuple:
return () if self.dim == 1 else (self.dim,)
@property
def __A ( self : str ) -> int:
return len(self.event_shape )
@property
def __A ( self : Union[str, Any] ) -> float:
return 0.0
def __A ( self : Union[str, Any] , __magic_name__ : int ) -> nn.Module:
return ParameterProjection(
in_features=__magic_name__ , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def __A ( self : Tuple , *__magic_name__ : torch.Tensor ) -> Union[str, Any]:
raise NotImplementedError()
@staticmethod
def __A ( __magic_name__ : torch.Tensor ) -> torch.Tensor:
return (x + torch.sqrt(torch.square(__magic_name__ ) + 4.0 )) / 2.0
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = {'''df''': 1, '''loc''': 1, '''scale''': 1}
lowerCamelCase__ = StudentT
@classmethod
def __A ( cls : Any , __magic_name__ : torch.Tensor , __magic_name__ : torch.Tensor , __magic_name__ : torch.Tensor ) -> Tuple:
SCREAMING_SNAKE_CASE_ = cls.squareplus(__magic_name__ ).clamp_min(torch.finfo(scale.dtype ).eps )
SCREAMING_SNAKE_CASE_ = 2.0 + cls.squareplus(__magic_name__ )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = {'''loc''': 1, '''scale''': 1}
lowerCamelCase__ = Normal
@classmethod
def __A ( cls : Union[str, Any] , __magic_name__ : torch.Tensor , __magic_name__ : torch.Tensor ) -> Tuple:
SCREAMING_SNAKE_CASE_ = cls.squareplus(__magic_name__ ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = {'''total_count''': 1, '''logits''': 1}
lowerCamelCase__ = NegativeBinomial
@classmethod
def __A ( cls : Any , __magic_name__ : torch.Tensor , __magic_name__ : torch.Tensor ) -> str:
SCREAMING_SNAKE_CASE_ = cls.squareplus(__magic_name__ )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def __A ( self : Tuple , __magic_name__ : Union[str, Any] ) -> Distribution:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = distr_args
if self.dim == 1:
return self.distribution_class(total_count=__magic_name__ , logits=__magic_name__ )
else:
return Independent(self.distribution_class(total_count=__magic_name__ , logits=__magic_name__ ) , 1 )
def __A ( self : int , __magic_name__ : List[str] , __magic_name__ : Optional[torch.Tensor] = None , __magic_name__ : Optional[torch.Tensor] = None ) -> Distribution:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 366
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : Union[str, Any] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def a__ ( ):
SCREAMING_SNAKE_CASE_ = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
SCREAMING_SNAKE_CASE_ = get_sagemaker_input()
else:
SCREAMING_SNAKE_CASE_ = get_cluster_input()
return config
def a__ ( __UpperCamelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser("config" , description=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Accelerate config command" , description=__UpperCamelCase )
parser.add_argument(
"--config_file" , default=__UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_user_input()
if args.config_file is not None:
SCREAMING_SNAKE_CASE_ = args.config_file
else:
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__UpperCamelCase )
else:
config.to_yaml_file(__UpperCamelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = config_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
config_command(__UpperCamelCase )
if __name__ == "__main__":
main()
| 305
| 0
|
from collections.abc import Callable
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Any , __magic_name__ : Callable | None = None ) -> None:
# Stores actual heap items.
SCREAMING_SNAKE_CASE_ = []
# Stores indexes of each item for supporting updates and deletion.
SCREAMING_SNAKE_CASE_ = {}
# Stores current size of heap.
SCREAMING_SNAKE_CASE_ = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
SCREAMING_SNAKE_CASE_ = key or (lambda __magic_name__ : x)
def __A ( self : Any , __magic_name__ : int ) -> int | None:
return int((i - 1) / 2 ) if i > 0 else None
def __A ( self : List[Any] , __magic_name__ : int ) -> int | None:
SCREAMING_SNAKE_CASE_ = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __A ( self : Tuple , __magic_name__ : int ) -> int | None:
SCREAMING_SNAKE_CASE_ = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __A ( self : Union[str, Any] , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.arr[j], self.arr[i]
def __A ( self : Optional[Any] , __magic_name__ : int , __magic_name__ : int ) -> bool:
return self.arr[i][1] < self.arr[j][1]
def __A ( self : int , __magic_name__ : int ) -> int:
SCREAMING_SNAKE_CASE_ = self._left(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self._right(__magic_name__ )
SCREAMING_SNAKE_CASE_ = i
if left is not None and not self._cmp(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = left
if right is not None and not self._cmp(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = right
return valid_parent
def __A ( self : Any , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = self._parent(__magic_name__ )
while parent is not None and not self._cmp(__magic_name__ , __magic_name__ ):
self._swap(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = parent, self._parent(__magic_name__ )
def __A ( self : List[Any] , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = self._get_valid_parent(__magic_name__ )
while valid_parent != index:
self._swap(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = valid_parent, self._get_valid_parent(__magic_name__ )
def __A ( self : Optional[Any] , __magic_name__ : int , __magic_name__ : int ) -> None:
if item not in self.pos_map:
return
SCREAMING_SNAKE_CASE_ = self.pos_map[item]
SCREAMING_SNAKE_CASE_ = [item, self.key(__magic_name__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(__magic_name__ )
self._heapify_down(__magic_name__ )
def __A ( self : Tuple , __magic_name__ : int ) -> None:
if item not in self.pos_map:
return
SCREAMING_SNAKE_CASE_ = self.pos_map[item]
del self.pos_map[item]
SCREAMING_SNAKE_CASE_ = self.arr[self.size - 1]
SCREAMING_SNAKE_CASE_ = 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(__magic_name__ )
self._heapify_down(__magic_name__ )
def __A ( self : Union[str, Any] , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(__magic_name__ )] )
else:
SCREAMING_SNAKE_CASE_ = [item, self.key(__magic_name__ )]
SCREAMING_SNAKE_CASE_ = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __A ( self : Optional[int] ) -> tuple | None:
return self.arr[0] if self.size else None
def __A ( self : Union[str, Any] ) -> tuple | None:
SCREAMING_SNAKE_CASE_ = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def a__ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(default='''summarization''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
lowerCamelCase__ = Features({'''text''': Value('''string''' )} )
lowerCamelCase__ = Features({'''summary''': Value('''string''' )} )
lowerCamelCase__ = "text"
lowerCamelCase__ = "summary"
@property
def __A ( self : Dict ) -> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 305
| 0
|
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [1]
for i in range(2 , __UpperCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = list(range(__UpperCamelCase ) )
# Find permutation
while factorials:
SCREAMING_SNAKE_CASE_ = factorials.pop()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = divmod(__UpperCamelCase , __UpperCamelCase )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368
|
from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None , __magic_name__ : List[str]=2_048 ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = config.__dict__
SCREAMING_SNAKE_CASE_ = modal_hidden_size
if num_labels:
SCREAMING_SNAKE_CASE_ = num_labels
| 305
| 0
|
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : int , __magic_name__ : Union[str, Any] , __magic_name__ : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = params
SCREAMING_SNAKE_CASE_ = np.array(__magic_name__ )
SCREAMING_SNAKE_CASE_ = np.array([len(__magic_name__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : str , __magic_name__ : Tuple ) -> Tuple:
return (self.token_ids[index], self.lengths[index])
def __len__( self : Union[str, Any] ) -> Any:
return len(self.lengths )
def __A ( self : Tuple ) -> List[str]:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def __A ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.params.max_model_input_size
SCREAMING_SNAKE_CASE_ = self.lengths > max_len
logger.info(F'''Splitting {sum(__magic_name__ )} too long sequences.''' )
def divide_chunks(__magic_name__ : str , __magic_name__ : List[str] ):
return [l[i : i + n] for i in range(0 , len(__magic_name__ ) , __magic_name__ )]
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
if self.params.mlm:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
SCREAMING_SNAKE_CASE_ = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
SCREAMING_SNAKE_CASE_ = np.insert(__magic_name__ , 0 , __magic_name__ )
if sub_s[-1] != sep_id:
SCREAMING_SNAKE_CASE_ = np.insert(__magic_name__ , len(__magic_name__ ) , __magic_name__ )
assert len(__magic_name__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(__magic_name__ )
new_tok_ids.extend(__magic_name__ )
new_lengths.extend([len(__magic_name__ ) for l in sub_seqs] )
SCREAMING_SNAKE_CASE_ = np.array(__magic_name__ )
SCREAMING_SNAKE_CASE_ = np.array(__magic_name__ )
def __A ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE_ = len(self )
SCREAMING_SNAKE_CASE_ = self.lengths > 11
SCREAMING_SNAKE_CASE_ = self.token_ids[indices]
SCREAMING_SNAKE_CASE_ = self.lengths[indices]
SCREAMING_SNAKE_CASE_ = len(self )
logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def __A ( self : Optional[int] ) -> Tuple:
if "unk_token" not in self.params.special_tok_ids:
return
else:
SCREAMING_SNAKE_CASE_ = self.params.special_tok_ids["unk_token"]
SCREAMING_SNAKE_CASE_ = len(self )
SCREAMING_SNAKE_CASE_ = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
SCREAMING_SNAKE_CASE_ = (unk_occs / self.lengths) < 0.5
SCREAMING_SNAKE_CASE_ = self.token_ids[indices]
SCREAMING_SNAKE_CASE_ = self.lengths[indices]
SCREAMING_SNAKE_CASE_ = len(self )
logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def __A ( self : Any ) -> Any:
if not self.params.is_master:
return
logger.info(F'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def __A ( self : Tuple , __magic_name__ : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE_ = [t[0] for t in batch]
SCREAMING_SNAKE_CASE_ = [t[1] for t in batch]
assert len(__magic_name__ ) == len(__magic_name__ )
# Max for paddings
SCREAMING_SNAKE_CASE_ = max(__magic_name__ )
# Pad token ids
if self.params.mlm:
SCREAMING_SNAKE_CASE_ = self.params.special_tok_ids["pad_token"]
else:
SCREAMING_SNAKE_CASE_ = self.params.special_tok_ids["unk_token"]
SCREAMING_SNAKE_CASE_ = [list(t.astype(__magic_name__ ) ) + [pad_idx] * (max_seq_len_ - len(__magic_name__ )) for t in token_ids]
assert len(tk_ ) == len(__magic_name__ )
assert all(len(__magic_name__ ) == max_seq_len_ for t in tk_ )
SCREAMING_SNAKE_CASE_ = torch.tensor(tk_ ) # (bs, max_seq_len_)
SCREAMING_SNAKE_CASE_ = torch.tensor(__magic_name__ ) # (bs)
return tk_t, lg_t
| 369
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViltImageProcessor'''
lowerCamelCase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[int] , __magic_name__ : str=None , __magic_name__ : List[str]=None , **__magic_name__ : 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`." )
super().__init__(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = self.image_processor
def __call__( self : List[str] , __magic_name__ : List[str] , __magic_name__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __magic_name__ : bool = True , __magic_name__ : Union[bool, str, PaddingStrategy] = False , __magic_name__ : Union[bool, str, TruncationStrategy] = None , __magic_name__ : Optional[int] = None , __magic_name__ : int = 0 , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = True , __magic_name__ : Optional[Union[str, TensorType]] = None , **__magic_name__ : str , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE_ = self.tokenizer(
text=__magic_name__ , add_special_tokens=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , max_length=__magic_name__ , stride=__magic_name__ , pad_to_multiple_of=__magic_name__ , return_token_type_ids=__magic_name__ , return_attention_mask=__magic_name__ , return_overflowing_tokens=__magic_name__ , return_special_tokens_mask=__magic_name__ , return_offsets_mapping=__magic_name__ , return_length=__magic_name__ , verbose=__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ , )
# add pixel_values + pixel_mask
SCREAMING_SNAKE_CASE_ = self.image_processor(__magic_name__ , return_tensors=__magic_name__ )
encoding.update(__magic_name__ )
return encoding
def __A ( self : Optional[int] , *__magic_name__ : List[Any] , **__magic_name__ : Optional[Any] ) -> Any:
return self.tokenizer.batch_decode(*__magic_name__ , **__magic_name__ )
def __A ( self : Dict , *__magic_name__ : List[Any] , **__magic_name__ : Union[str, Any] ) -> str:
return self.tokenizer.decode(*__magic_name__ , **__magic_name__ )
@property
def __A ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __A ( self : Dict ) -> List[Any]:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __magic_name__ , )
return self.image_processor_class
@property
def __A ( self : int ) -> List[Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __magic_name__ , )
return self.image_processor
| 305
| 0
|
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
A : Dict = logging.get_logger(__name__)
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} )
lowerCamelCase__ = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
lowerCamelCase__ = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def __A ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = self.task_name.lower()
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''train'''
lowerCamelCase__ = '''dev'''
lowerCamelCase__ = '''test'''
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
def __init__( self : int , __magic_name__ : GlueDataTrainingArguments , __magic_name__ : PreTrainedTokenizerBase , __magic_name__ : Optional[int] = None , __magic_name__ : Union[str, Split] = Split.train , __magic_name__ : Optional[str] = None , ) -> str:
warnings.warn(
"This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
"library. You can have a look at this example script for pointers: "
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py" , __magic_name__ , )
SCREAMING_SNAKE_CASE_ = args
SCREAMING_SNAKE_CASE_ = glue_processors[args.task_name]()
SCREAMING_SNAKE_CASE_ = glue_output_modes[args.task_name]
if isinstance(__magic_name__ , __magic_name__ ):
try:
SCREAMING_SNAKE_CASE_ = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
# Load data features from cache or dataset file
SCREAMING_SNAKE_CASE_ = 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}_{args.task_name}''' , )
SCREAMING_SNAKE_CASE_ = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = label_list[2], label_list[1]
SCREAMING_SNAKE_CASE_ = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
SCREAMING_SNAKE_CASE_ = cached_features_file + ".lock"
with FileLock(__magic_name__ ):
if os.path.exists(__magic_name__ ) and not args.overwrite_cache:
SCREAMING_SNAKE_CASE_ = time.time()
SCREAMING_SNAKE_CASE_ = torch.load(__magic_name__ )
logger.info(
F'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
else:
logger.info(F'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
SCREAMING_SNAKE_CASE_ = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
SCREAMING_SNAKE_CASE_ = self.processor.get_test_examples(args.data_dir )
else:
SCREAMING_SNAKE_CASE_ = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
SCREAMING_SNAKE_CASE_ = examples[:limit_length]
SCREAMING_SNAKE_CASE_ = glue_convert_examples_to_features(
__magic_name__ , __magic_name__ , max_length=args.max_seq_length , label_list=__magic_name__ , output_mode=self.output_mode , )
SCREAMING_SNAKE_CASE_ = time.time()
torch.save(self.features , __magic_name__ )
# ^ 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 : Dict ) -> List[Any]:
return len(self.features )
def __getitem__( self : Union[str, Any] , __magic_name__ : Optional[int] ) -> InputFeatures:
return self.features[i]
def __A ( self : str ) -> Optional[Any]:
return self.label_list
| 370
|
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 ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : Optional[int] = {
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''table-transformer'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : List[Any] , __magic_name__ : Optional[Any]=True , __magic_name__ : Dict=None , __magic_name__ : Any=3 , __magic_name__ : List[str]=100 , __magic_name__ : Union[str, Any]=6 , __magic_name__ : Dict=2_048 , __magic_name__ : str=8 , __magic_name__ : int=6 , __magic_name__ : List[Any]=2_048 , __magic_name__ : Optional[int]=8 , __magic_name__ : Optional[int]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Optional[Any]=True , __magic_name__ : List[Any]="relu" , __magic_name__ : List[str]=256 , __magic_name__ : List[str]=0.1 , __magic_name__ : int=0.0 , __magic_name__ : Optional[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : str=1.0 , __magic_name__ : int=False , __magic_name__ : Dict="sine" , __magic_name__ : Union[str, Any]="resnet50" , __magic_name__ : Optional[Any]=True , __magic_name__ : str=False , __magic_name__ : List[str]=1 , __magic_name__ : int=5 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Tuple=1 , __magic_name__ : Optional[int]=1 , __magic_name__ : Optional[Any]=5 , __magic_name__ : Optional[int]=2 , __magic_name__ : Union[str, Any]=0.1 , **__magic_name__ : Tuple , ) -> str:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = backbone_config.get("model_type" )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE_ = config_class.from_dict(__magic_name__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None, None, None
SCREAMING_SNAKE_CASE_ = use_timm_backbone
SCREAMING_SNAKE_CASE_ = backbone_config
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = num_queries
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = init_xavier_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = auxiliary_loss
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = backbone
SCREAMING_SNAKE_CASE_ = use_pretrained_backbone
SCREAMING_SNAKE_CASE_ = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE_ = class_cost
SCREAMING_SNAKE_CASE_ = bbox_cost
SCREAMING_SNAKE_CASE_ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE_ = mask_loss_coefficient
SCREAMING_SNAKE_CASE_ = dice_loss_coefficient
SCREAMING_SNAKE_CASE_ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE_ = giou_loss_coefficient
SCREAMING_SNAKE_CASE_ = eos_coefficient
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def __A ( self : Union[str, Any] ) -> int:
return self.encoder_attention_heads
@property
def __A ( self : Any ) -> int:
return self.d_model
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def __A ( self : Any ) -> float:
return 1e-5
@property
def __A ( self : int ) -> int:
return 12
| 305
| 0
|
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Union[str, Any] = 1_00
A : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0 )
def a__ ( __UpperCamelCase ):
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
SCREAMING_SNAKE_CASE_ = set()
SCREAMING_SNAKE_CASE_ = 4_2
SCREAMING_SNAKE_CASE_ = 4_2
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def a__ ( __UpperCamelCase = 5_0_0_0 ):
for number_to_partition in range(1 , __UpperCamelCase ):
if len(partition(__UpperCamelCase ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(f"{solution() = }")
| 371
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305
| 0
|
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
A : Any = {"UserAgent": UserAgent().random}
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = script.contents[0]
SCREAMING_SNAKE_CASE_ = json.loads(data[data.find("{\"config\"" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict , __magic_name__ : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ = F'''https://www.instagram.com/{username}/'''
SCREAMING_SNAKE_CASE_ = self.get_json()
def __A ( self : int ) -> dict:
SCREAMING_SNAKE_CASE_ = requests.get(self.url , headers=__magic_name__ ).text
SCREAMING_SNAKE_CASE_ = BeautifulSoup(__magic_name__ , "html.parser" ).find_all("script" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : int ) -> str:
return F'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self : str ) -> str:
return F'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def __A ( self : Optional[Any] ) -> str:
return self.user_data["username"]
@property
def __A ( self : Any ) -> str:
return self.user_data["full_name"]
@property
def __A ( self : int ) -> str:
return self.user_data["biography"]
@property
def __A ( self : List[str] ) -> str:
return self.user_data["business_email"]
@property
def __A ( self : Tuple ) -> str:
return self.user_data["external_url"]
@property
def __A ( self : Dict ) -> int:
return self.user_data["edge_followed_by"]["count"]
@property
def __A ( self : Any ) -> int:
return self.user_data["edge_follow"]["count"]
@property
def __A ( self : Optional[int] ) -> int:
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def __A ( self : int ) -> str:
return self.user_data["profile_pic_url_hd"]
@property
def __A ( self : Tuple ) -> bool:
return self.user_data["is_verified"]
@property
def __A ( self : Optional[Any] ) -> bool:
return self.user_data["is_private"]
def a__ ( __UpperCamelCase = "github" ):
import os
if os.environ.get("CI" ):
return # test failing on GitHub Actions
SCREAMING_SNAKE_CASE_ = InstagramUser(__UpperCamelCase )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , __UpperCamelCase )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_5_0
assert instagram_user.number_of_followers > 1_2_0_0_0_0
assert instagram_user.number_of_followings > 1_5
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("https://instagram." )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
A : List[Any] = InstagramUser("github")
print(instagram_user)
print(f"{instagram_user.number_of_posts = }")
print(f"{instagram_user.number_of_followers = }")
print(f"{instagram_user.number_of_followings = }")
print(f"{instagram_user.email = }")
print(f"{instagram_user.website = }")
print(f"{instagram_user.profile_picture_url = }")
print(f"{instagram_user.is_verified = }")
print(f"{instagram_user.is_private = }")
| 350
|
from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to be of square shaped array but got a "
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
SCREAMING_SNAKE_CASE_ = (table[i][j] - total) / upper[j][j]
SCREAMING_SNAKE_CASE_ = 1
for j in range(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
"""simple docstring"""
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
A : List[str] = "\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
A : Union[str, Any] = "\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
A : Dict = "\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\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.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase (datasets.Metric ):
"""simple docstring"""
def __A ( self : Any ) -> Dict:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://github.com/krishnap25/mauve" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/krishnap25/mauve"] , reference_urls=[
"https://arxiv.org/abs/2102.01454",
"https://github.com/krishnap25/mauve",
] , )
def __A ( self : List[str] , __magic_name__ : List[Any] , __magic_name__ : Dict , __magic_name__ : Optional[Any]=None , __magic_name__ : int=None , __magic_name__ : List[Any]=None , __magic_name__ : Optional[int]=None , __magic_name__ : Union[str, Any]="auto" , __magic_name__ : Dict=-1 , __magic_name__ : Optional[int]=0.9 , __magic_name__ : List[Any]=5 , __magic_name__ : List[Any]=500 , __magic_name__ : Tuple="gpt2-large" , __magic_name__ : List[str]=-1 , __magic_name__ : str=1_024 , __magic_name__ : Optional[Any]=25 , __magic_name__ : List[Any]=5 , __magic_name__ : Any=True , __magic_name__ : int=25 , ) -> int:
SCREAMING_SNAKE_CASE_ = compute_mauve(
p_text=__magic_name__ , q_text=__magic_name__ , p_features=__magic_name__ , q_features=__magic_name__ , p_tokens=__magic_name__ , q_tokens=__magic_name__ , num_buckets=__magic_name__ , pca_max_data=__magic_name__ , kmeans_explained_var=__magic_name__ , kmeans_num_redo=__magic_name__ , kmeans_max_iter=__magic_name__ , featurize_model_name=__magic_name__ , device_id=__magic_name__ , max_text_length=__magic_name__ , divergence_curve_discretization_size=__magic_name__ , mauve_scaling_factor=__magic_name__ , verbose=__magic_name__ , seed=__magic_name__ , )
return out
| 351
|
from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values" )
return 4 * pi * radius**2
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values" )
return 3 * pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values" )
SCREAMING_SNAKE_CASE_ = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values" )
return 2 * pi * radius * (height + radius)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values" )
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori" )
return 4 * pow(__UpperCamelCase , 2 ) * torus_radius * tube_radius
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values" )
return length * width
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values" )
return side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values" )
return (base * height) / 2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle" )
SCREAMING_SNAKE_CASE_ = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE_ = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values" )
return base * height
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values" )
return 1 / 2 * (basea + basea) * height
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values" )
return pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values" )
return pi * radius_x * radius_y
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values" )
return 1 / 2 * diagonal_a * diagonal_a
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides" )
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 305
| 0
|
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
A : Any = logging.get_logger(__name__)
def a__ ( __UpperCamelCase ):
if isinstance(__UpperCamelCase , np.ndarray ):
return list(tensor.shape )
SCREAMING_SNAKE_CASE_ = tf.shape(__UpperCamelCase )
if tensor.shape == tf.TensorShape(__UpperCamelCase ):
return dynamic
SCREAMING_SNAKE_CASE_ = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCamelCase )]
def a__ ( __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None ):
return tf.nn.softmax(logits=logits + 1E-9 , axis=__UpperCamelCase , name=__UpperCamelCase )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=1E-5 , __UpperCamelCase=-1 ):
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )
# Get mean and variance on the axis to be normalized
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = tf.nn.moments(__UpperCamelCase , axes=[axis] , keepdims=__UpperCamelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
SCREAMING_SNAKE_CASE_ = [1] * inputs.shape.rank
SCREAMING_SNAKE_CASE_ = shape_list(__UpperCamelCase )[axis]
SCREAMING_SNAKE_CASE_ = tf.reshape(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = tf.reshape(__UpperCamelCase , __UpperCamelCase )
# Compute layer normalization using the batch_normalization
# function.
SCREAMING_SNAKE_CASE_ = tf.nn.batch_normalization(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , offset=__UpperCamelCase , scale=__UpperCamelCase , variance_epsilon=__UpperCamelCase , )
return outputs
def a__ ( __UpperCamelCase , __UpperCamelCase=0 , __UpperCamelCase=-1 ):
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
SCREAMING_SNAKE_CASE_ = tf.shape(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
SCREAMING_SNAKE_CASE_ = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(__UpperCamelCase , __UpperCamelCase )
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , tf.Tensor ):
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(__UpperCamelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
SCREAMING_SNAKE_CASE_ = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
SCREAMING_SNAKE_CASE_ = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
SCREAMING_SNAKE_CASE_ = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = "input_ids" ):
tf.debugging.assert_less(
__UpperCamelCase , tf.cast(__UpperCamelCase , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(__UpperCamelCase )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 6_4_5_1_2
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
SCREAMING_SNAKE_CASE_ = [x for x in data if len(__UpperCamelCase ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"The following attributes cannot be saved to HDF5 file because "
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
SCREAMING_SNAKE_CASE_ = np.asarray(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = np.array_split(__UpperCamelCase , __UpperCamelCase )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
SCREAMING_SNAKE_CASE_ = np.array_split(__UpperCamelCase , __UpperCamelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = chunk_data
else:
SCREAMING_SNAKE_CASE_ = data
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if name in group.attrs:
SCREAMING_SNAKE_CASE_ = [n.decode("utf8" ) if hasattr(__UpperCamelCase , "decode" ) else n for n in group.attrs[name]]
else:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("utf8" ) if hasattr(__UpperCamelCase , "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
chunk_id += 1
return data
def a__ ( __UpperCamelCase ):
def _expand_single_ad_tensor(__UpperCamelCase ):
if isinstance(__UpperCamelCase , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(__UpperCamelCase , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , __UpperCamelCase )
| 352
|
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
A : List[str] = logging.get_logger(__name__)
A : int = {
"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 lowerCamelCase (SCREAMING_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 : Dict , __magic_name__ : Dict=50_265 , __magic_name__ : str=512 , __magic_name__ : List[Any]=8 , __magic_name__ : Any=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Any=8 , __magic_name__ : Optional[int]=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Tuple=0.0 , __magic_name__ : Dict=0.0 , __magic_name__ : Optional[int]=True , __magic_name__ : Any=True , __magic_name__ : Dict="gelu" , __magic_name__ : Tuple=512 , __magic_name__ : List[str]=0.1 , __magic_name__ : List[Any]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : Union[str, Any]=1 , __magic_name__ : List[Any]=False , __magic_name__ : str=0 , __magic_name__ : Dict=1 , __magic_name__ : str=2 , __magic_name__ : Union[str, Any]=2 , **__magic_name__ : Optional[Any] , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , is_encoder_decoder=__magic_name__ , decoder_start_token_id=__magic_name__ , forced_eos_token_id=__magic_name__ , **__magic_name__ , )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def __A ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ = {0: "batch"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = 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 __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super().outputs
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __A ( self : int , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# Generate decoder inputs
SCREAMING_SNAKE_CASE_ = seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE_ = dict(**__magic_name__ , **__magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
SCREAMING_SNAKE_CASE_ = common_inputs["decoder_input_ids"].shape[1]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = decoder_seq_length + 3
SCREAMING_SNAKE_CASE_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(__magic_name__ , __magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ = min(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = max(__magic_name__ , __magic_name__ ) - min_num_layers
SCREAMING_SNAKE_CASE_ = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(__magic_name__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE_ = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(__magic_name__ , __magic_name__ ):
common_inputs["past_key_values"].append((torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) )
return common_inputs
def __A ( self : Union[str, Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE_ = seqlen + 2
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = common_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["attention_mask"], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(__magic_name__ )
]
return common_inputs
def __A ( self : Dict , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , 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
SCREAMING_SNAKE_CASE_ = tokenizer.num_special_tokens_to_add(__magic_name__ )
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__magic_name__ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE_ = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE_ = dict(tokenizer(__magic_name__ , return_tensors=__magic_name__ ) )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
elif self.task == "causal-lm":
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_causal_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : List[Any] , __magic_name__ : str , __magic_name__ : List[str] ) -> List[str]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super()._flatten_past_key_values_(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self )._flatten_past_key_values_(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
| 305
| 0
|
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
A : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
A : int = 2_56
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''melgan''']
def __init__( self : Any , __magic_name__ : SpectrogramNotesEncoder , __magic_name__ : SpectrogramContEncoder , __magic_name__ : TaFilmDecoder , __magic_name__ : DDPMScheduler , __magic_name__ : OnnxRuntimeModel if is_onnx_available() else Any , ) -> None:
super().__init__()
# From MELGAN
SCREAMING_SNAKE_CASE_ = math.log(1e-5 ) # Matches MelGAN training.
SCREAMING_SNAKE_CASE_ = 4.0 # Largest value for most examples
SCREAMING_SNAKE_CASE_ = 128
self.register_modules(
notes_encoder=__magic_name__ , continuous_encoder=__magic_name__ , decoder=__magic_name__ , scheduler=__magic_name__ , melgan=__magic_name__ , )
def __A ( self : List[Any] , __magic_name__ : str , __magic_name__ : List[Any]=(-1.0, 1.0) , __magic_name__ : Dict=False ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = output_range
if clip:
SCREAMING_SNAKE_CASE_ = torch.clip(__magic_name__ , self.min_value , self.max_value )
# Scale to [0, 1].
SCREAMING_SNAKE_CASE_ = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def __A ( self : Tuple , __magic_name__ : List[Any] , __magic_name__ : Dict=(-1.0, 1.0) , __magic_name__ : int=False ) -> str:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = input_range
SCREAMING_SNAKE_CASE_ = torch.clip(__magic_name__ , __magic_name__ , __magic_name__ ) if clip else outputs
# Scale to [0, 1].
SCREAMING_SNAKE_CASE_ = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def __A ( self : Any , __magic_name__ : Dict , __magic_name__ : Optional[int] , __magic_name__ : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = input_tokens > 0
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.notes_encoder(
encoder_input_tokens=__magic_name__ , encoder_inputs_mask=__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.continuous_encoder(
encoder_inputs=__magic_name__ , encoder_inputs_mask=__magic_name__ )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def __A ( self : Any , __magic_name__ : List[str] , __magic_name__ : List[str] , __magic_name__ : Any ) -> str:
SCREAMING_SNAKE_CASE_ = noise_time
if not torch.is_tensor(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(__magic_name__ ) and len(timesteps.shape ) == 0:
SCREAMING_SNAKE_CASE_ = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
SCREAMING_SNAKE_CASE_ = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
SCREAMING_SNAKE_CASE_ = self.decoder(
encodings_and_masks=__magic_name__ , decoder_input_tokens=__magic_name__ , decoder_noise_time=__magic_name__ )
return logits
@torch.no_grad()
def __call__( self : Union[str, Any] , __magic_name__ : List[List[int]] , __magic_name__ : Optional[torch.Generator] = None , __magic_name__ : int = 100 , __magic_name__ : bool = True , __magic_name__ : str = "numpy" , __magic_name__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __magic_name__ : int = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__magic_name__ , __magic_name__ ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(__magic_name__ )}.''' )
SCREAMING_SNAKE_CASE_ = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = np.zeros([1, 0, self.n_dims] , np.floataa )
SCREAMING_SNAKE_CASE_ = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__magic_name__ , device=self.device )
for i, encoder_input_tokens in enumerate(__magic_name__ ):
if i == 0:
SCREAMING_SNAKE_CASE_ = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
SCREAMING_SNAKE_CASE_ = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__magic_name__ , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
SCREAMING_SNAKE_CASE_ = ones
SCREAMING_SNAKE_CASE_ = self.scale_features(
__magic_name__ , output_range=[-1.0, 1.0] , clip=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=__magic_name__ , continuous_mask=__magic_name__ , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
SCREAMING_SNAKE_CASE_ = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=__magic_name__ , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(__magic_name__ )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
SCREAMING_SNAKE_CASE_ = self.decode(
encodings_and_masks=__magic_name__ , input_tokens=__magic_name__ , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
SCREAMING_SNAKE_CASE_ = self.scheduler.step(__magic_name__ , __magic_name__ , __magic_name__ , generator=__magic_name__ ).prev_sample
SCREAMING_SNAKE_CASE_ = self.scale_to_features(__magic_name__ , input_range=[-1.0, 1.0] )
SCREAMING_SNAKE_CASE_ = mel[:1]
SCREAMING_SNAKE_CASE_ = mel.cpu().float().numpy()
SCREAMING_SNAKE_CASE_ = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__magic_name__ , __magic_name__ )
logger.info("Generated segment" , __magic_name__ )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'." )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'." )
if output_type == "numpy":
SCREAMING_SNAKE_CASE_ = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
SCREAMING_SNAKE_CASE_ = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=__magic_name__ )
| 353
|
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , __magic_name__ : List[str] , __magic_name__ : int=100 , __magic_name__ : Optional[Any]=13 , __magic_name__ : Dict=30 , __magic_name__ : Tuple=2 , __magic_name__ : str=3 , __magic_name__ : str=True , __magic_name__ : Optional[int]=True , __magic_name__ : Union[str, Any]=32 , __magic_name__ : Optional[int]=4 , __magic_name__ : Dict=4 , __magic_name__ : Tuple=37 , __magic_name__ : Any="gelu" , __magic_name__ : int=0.1 , __magic_name__ : List[str]=0.1 , __magic_name__ : Optional[int]=10 , __magic_name__ : Tuple=0.02 , __magic_name__ : Optional[int]=3 , __magic_name__ : List[str]=None , __magic_name__ : Tuple=[0, 1, 2, 3] , ) -> List[str]:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = 100
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
SCREAMING_SNAKE_CASE_ = scope
SCREAMING_SNAKE_CASE_ = out_indices
SCREAMING_SNAKE_CASE_ = num_labels
# in BeiT, 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 __A ( self : Any ) -> int:
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __A ( self : Dict ) -> Optional[int]:
return BeitConfig(
vocab_size=self.vocab_size , 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=__magic_name__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def __A ( self : Optional[int] , __magic_name__ : List[str] , __magic_name__ : Optional[int] , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = BeitModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : str ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __A ( self : Dict , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : Tuple , __magic_name__ : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = self.type_sequence_label_size
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self : Tuple , __magic_name__ : Any , __magic_name__ : Optional[Any] , __magic_name__ : Tuple , __magic_name__ : int ) -> int:
SCREAMING_SNAKE_CASE_ = self.num_labels
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def __A ( self : str ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = config_and_inputs
SCREAMING_SNAKE_CASE_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = BeitModelTester(self )
SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def __A ( self : Dict ) -> List[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="BEiT does not use inputs_embeds" )
def __A ( self : List[str] ) -> Optional[Any]:
pass
@require_torch_multi_gpu
@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def __A ( self : str ) -> List[str]:
pass
def __A ( self : List[Any] ) -> List[str]:
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(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def __A ( self : Union[str, Any] ) -> int:
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(__magic_name__ )
SCREAMING_SNAKE_CASE_ = inspect.signature(model.forward )
# 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] , __magic_name__ )
def __A ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__magic_name__ )
def __A ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def __A ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__magic_name__ )
def __A ( self : int ) -> Optional[int]:
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : Any ) -> Tuple:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.gradient_checkpointing_enable()
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = _config_zero_init(__magic_name__ )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ = model_class(config=__magic_name__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def __A ( self : int ) -> Optional[int]:
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ = BeitModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self : List[Any] ) -> str:
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def __A ( self : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).pixel_values.to(__magic_name__ )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE_ = torch.ones((1, 196) , dtype=torch.bool ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(pixel_values=__magic_name__ , bool_masked_pos=__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , __magic_name__ , atol=1e-2 ) )
@slow
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 1_000) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([-1.2385, -1.0987, -1.0108] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 281
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to(
__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 21_841) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([1.6881, -0.2787, 0.5901] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 2_396
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Tuple ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = version.parse(PIL.__version__ ) < version.parse("9.0.0" )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=__magic_name__ , )
else:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=__magic_name__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __magic_name__ , atol=1e-4 ) )
@slow
def __A ( self : List[str] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ , target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE_ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
| 305
| 0
|
"""simple docstring"""
from __future__ import annotations
A : List[str] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __magic_name__ : dict[str, list[str]] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = graph
# mapping node to its parent in resulting breadth first tree
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = source_vertex
def __A ( self : Optional[Any] ) -> None:
SCREAMING_SNAKE_CASE_ = {self.source_vertex}
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = [self.source_vertex] # first in first out queue
while queue:
SCREAMING_SNAKE_CASE_ = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(__magic_name__ )
SCREAMING_SNAKE_CASE_ = vertex
queue.append(__magic_name__ )
def __A ( self : Union[str, Any] , __magic_name__ : str ) -> str:
if target_vertex == self.source_vertex:
return self.source_vertex
SCREAMING_SNAKE_CASE_ = self.parent.get(__magic_name__ )
if target_vertex_parent is None:
SCREAMING_SNAKE_CASE_ = (
F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
)
raise ValueError(__magic_name__ )
return self.shortest_path(__magic_name__ ) + F'''->{target_vertex}'''
if __name__ == "__main__":
A : List[str] = Graph(graph, "G")
g.breath_first_search()
print(g.shortest_path("D"))
print(g.shortest_path("G"))
print(g.shortest_path("Foo"))
| 354
|
from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = self._trie
for char in text:
if char not in trie:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = trie[char]
SCREAMING_SNAKE_CASE_ = True
def __A ( self : Union[str, Any] , __magic_name__ : str ) -> tuple | list:
SCREAMING_SNAKE_CASE_ = self._trie
for char in prefix:
if char in trie:
SCREAMING_SNAKE_CASE_ = trie[char]
else:
return []
return self._elements(__magic_name__ )
def __A ( self : int , __magic_name__ : dict ) -> tuple:
SCREAMING_SNAKE_CASE_ = []
for c, v in d.items():
SCREAMING_SNAKE_CASE_ = [" "] if c == END else [(c + s) for s in self._elements(__magic_name__ )]
result.extend(__magic_name__ )
return tuple(__magic_name__ )
A : Union[str, Any] = Trie()
A : Optional[int] = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
trie.insert_word(word)
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = trie.find_word(__UpperCamelCase )
return tuple(string + word for word in suffixes )
def a__ ( ):
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 305
| 0
|
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A : str = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
A : List[str] = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A : Optional[Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''} , )
lowerCamelCase__ = field(default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''A folder containing the training data.'''} )
lowerCamelCase__ = field(default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''A folder containing the validation data.'''} )
lowerCamelCase__ = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
lowerCamelCase__ = field(default=3_2 , metadata={'''help''': '''The size of the square patches to use for masking.'''} )
lowerCamelCase__ = field(
default=0.6 , metadata={'''help''': '''Percentage of patches to mask.'''} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def __A ( self : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = {}
if self.train_dir is not None:
SCREAMING_SNAKE_CASE_ = self.train_dir
if self.validation_dir is not None:
SCREAMING_SNAKE_CASE_ = self.validation_dir
SCREAMING_SNAKE_CASE_ = data_files if data_files else None
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
'''checkpoint identifier on the hub. '''
'''Don\'t set if you want to train a model from scratch.'''
)
} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(SCREAMING_SNAKE_CASE__ )} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''} , )
lowerCamelCase__ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
lowerCamelCase__ = field(default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Name or path of preprocessor config.'''} )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
)
} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
)
} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Stride to use for the encoder.'''} , )
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : Union[str, Any]=192 , __magic_name__ : Optional[int]=32 , __magic_name__ : Optional[int]=4 , __magic_name__ : Optional[int]=0.6 ) -> Dict:
SCREAMING_SNAKE_CASE_ = input_size
SCREAMING_SNAKE_CASE_ = mask_patch_size
SCREAMING_SNAKE_CASE_ = model_patch_size
SCREAMING_SNAKE_CASE_ = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError("Input size must be divisible by mask patch size" )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError("Mask patch size must be divisible by model patch size" )
SCREAMING_SNAKE_CASE_ = self.input_size // self.mask_patch_size
SCREAMING_SNAKE_CASE_ = self.mask_patch_size // self.model_patch_size
SCREAMING_SNAKE_CASE_ = self.rand_size**2
SCREAMING_SNAKE_CASE_ = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = np.random.permutation(self.token_count )[: self.mask_count]
SCREAMING_SNAKE_CASE_ = np.zeros(self.token_count , dtype=__magic_name__ )
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = mask.reshape((self.rand_size, self.rand_size) )
SCREAMING_SNAKE_CASE_ = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = torch.stack([example["pixel_values"] for example in examples] )
SCREAMING_SNAKE_CASE_ = torch.stack([example["mask"] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def a__ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_mim" , __UpperCamelCase , __UpperCamelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = training_args.get_process_log_level()
logger.setLevel(__UpperCamelCase )
transformers.utils.logging.set_verbosity(__UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Initialize our dataset.
SCREAMING_SNAKE_CASE_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
SCREAMING_SNAKE_CASE_ = None if "validation" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __UpperCamelCase ) and data_args.train_val_split > 0.0:
SCREAMING_SNAKE_CASE_ = ds["train"].train_test_split(data_args.train_val_split )
SCREAMING_SNAKE_CASE_ = split["train"]
SCREAMING_SNAKE_CASE_ = split["test"]
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE_ = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(model_args.config_name_or_path , **__UpperCamelCase )
elif model_args.model_name_or_path:
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.config_overrides is not None:
logger.info(F'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(F'''New config: {config}''' )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(__UpperCamelCase , "decoder_type" ):
SCREAMING_SNAKE_CASE_ = "simmim"
# adapt config
SCREAMING_SNAKE_CASE_ = model_args.image_size if model_args.image_size is not None else config.image_size
SCREAMING_SNAKE_CASE_ = model_args.patch_size if model_args.patch_size is not None else config.patch_size
SCREAMING_SNAKE_CASE_ = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
"image_size": model_args.image_size,
"patch_size": model_args.patch_size,
"encoder_stride": model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **__UpperCamelCase )
elif model_args.model_name_or_path:
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
SCREAMING_SNAKE_CASE_ = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
SCREAMING_SNAKE_CASE_ = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=__UpperCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("Training new model from scratch" )
SCREAMING_SNAKE_CASE_ = AutoModelForMaskedImageModeling.from_config(__UpperCamelCase )
if training_args.do_train:
SCREAMING_SNAKE_CASE_ = ds["train"].column_names
else:
SCREAMING_SNAKE_CASE_ = ds["validation"].column_names
if data_args.image_column_name is not None:
SCREAMING_SNAKE_CASE_ = data_args.image_column_name
elif "image" in column_names:
SCREAMING_SNAKE_CASE_ = "image"
elif "img" in column_names:
SCREAMING_SNAKE_CASE_ = "img"
else:
SCREAMING_SNAKE_CASE_ = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
SCREAMING_SNAKE_CASE_ = Compose(
[
Lambda(lambda __UpperCamelCase : img.convert("RGB" ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
SCREAMING_SNAKE_CASE_ = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [transforms(__UpperCamelCase ) for image in examples[image_column_name]]
SCREAMING_SNAKE_CASE_ = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("--do_train requires a train dataset" )
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE_ = ds["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__UpperCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("--do_eval requires a validation dataset" )
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE_ = (
ds["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__UpperCamelCase )
# Initialize our trainer
SCREAMING_SNAKE_CASE_ = Trainer(
model=__UpperCamelCase , args=__UpperCamelCase , train_dataset=ds["train"] if training_args.do_train else None , eval_dataset=ds["validation"] if training_args.do_eval else None , tokenizer=__UpperCamelCase , data_collator=__UpperCamelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE_ = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE_ = last_checkpoint
SCREAMING_SNAKE_CASE_ = trainer.train(resume_from_checkpoint=__UpperCamelCase )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
SCREAMING_SNAKE_CASE_ = trainer.evaluate()
trainer.log_metrics("eval" , __UpperCamelCase )
trainer.save_metrics("eval" , __UpperCamelCase )
# Write model card and (optionally) push to hub
SCREAMING_SNAKE_CASE_ = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "masked-image-modeling",
"dataset": data_args.dataset_name,
"tags": ["masked-image-modeling"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__UpperCamelCase )
else:
trainer.create_model_card(**__UpperCamelCase )
if __name__ == "__main__":
main()
| 355
|
from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name # process name
SCREAMING_SNAKE_CASE_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
SCREAMING_SNAKE_CASE_ = arrival_time
SCREAMING_SNAKE_CASE_ = burst_time # remaining burst time
SCREAMING_SNAKE_CASE_ = 0 # total time of the process wait in ready queue
SCREAMING_SNAKE_CASE_ = 0 # time from arrival time to completion time
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Tuple , __magic_name__ : int , __magic_name__ : list[int] , __magic_name__ : deque[Process] , __magic_name__ : int , ) -> None:
# total number of mlfq's queues
SCREAMING_SNAKE_CASE_ = number_of_queues
# time slice of queues that round robin algorithm applied
SCREAMING_SNAKE_CASE_ = time_slices
# unfinished process is in this ready_queue
SCREAMING_SNAKE_CASE_ = queue
# current time
SCREAMING_SNAKE_CASE_ = current_time
# finished process is in this sequence queue
SCREAMING_SNAKE_CASE_ = deque()
def __A ( self : Dict ) -> list[str]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def __A ( self : Tuple , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def __A ( self : str , __magic_name__ : deque[Process] ) -> list[int]:
return [q.burst_time for q in queue]
def __A ( self : Optional[Any] , __magic_name__ : Process ) -> int:
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def __A ( self : Optional[Any] , __magic_name__ : deque[Process] ) -> deque[Process]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of finished process
while len(__magic_name__ ) != 0:
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(__magic_name__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
SCREAMING_SNAKE_CASE_ = 0
# set the process's turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# set the completion time
SCREAMING_SNAKE_CASE_ = self.current_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def __A ( self : Any , __magic_name__ : deque[Process] , __magic_name__ : int ) -> tuple[deque[Process], deque[Process]]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(__magic_name__ ) ):
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(__magic_name__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
SCREAMING_SNAKE_CASE_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(__magic_name__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
SCREAMING_SNAKE_CASE_ = 0
# set the finish time
SCREAMING_SNAKE_CASE_ = self.current_time
# update the process' turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def __A ( self : Any ) -> deque[Process]:
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
A : Dict = Process("P1", 0, 53)
A : str = Process("P2", 0, 17)
A : List[Any] = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Dict = 3
A : Any = [17, 25]
A : Dict = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
A : Union[str, Any] = Process("P1", 0, 53)
A : Any = Process("P2", 0, 17)
A : Dict = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Optional[int] = 3
A : int = [17, 25]
A : Union[str, Any] = deque([Pa, Pa, Pa, Pa])
A : Tuple = MLFQ(number_of_queues, time_slices, queue, 0)
A : Tuple = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| 305
| 0
|
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None ):
if attention_mask is None:
SCREAMING_SNAKE_CASE_ = tf.cast(tf.math.not_equal(__UpperCamelCase , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = OPTConfig
lowerCamelCase__ = {}
lowerCamelCase__ = '''gelu'''
def __init__( self : int , __magic_name__ : Optional[Any] , __magic_name__ : int=13 , __magic_name__ : Any=7 , __magic_name__ : int=True , __magic_name__ : Tuple=False , __magic_name__ : str=99 , __magic_name__ : str=16 , __magic_name__ : int=2 , __magic_name__ : List[str]=4 , __magic_name__ : Tuple=4 , __magic_name__ : str="gelu" , __magic_name__ : Union[str, Any]=0.1 , __magic_name__ : str=0.1 , __magic_name__ : Union[str, Any]=20 , __magic_name__ : Any=2 , __magic_name__ : Optional[int]=1 , __magic_name__ : Optional[Any]=0 , __magic_name__ : Union[str, Any]=16 , __magic_name__ : List[Any]=16 , ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = batch_size
SCREAMING_SNAKE_CASE_ = seq_length
SCREAMING_SNAKE_CASE_ = is_training
SCREAMING_SNAKE_CASE_ = use_labels
SCREAMING_SNAKE_CASE_ = vocab_size
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_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = eos_token_id
SCREAMING_SNAKE_CASE_ = pad_token_id
SCREAMING_SNAKE_CASE_ = bos_token_id
SCREAMING_SNAKE_CASE_ = embed_dim
SCREAMING_SNAKE_CASE_ = word_embed_proj_dim
SCREAMING_SNAKE_CASE_ = False
def __A ( self : str ) -> int:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
SCREAMING_SNAKE_CASE_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
SCREAMING_SNAKE_CASE_ = tf.concat([input_ids, eos_tensor] , axis=1 )
SCREAMING_SNAKE_CASE_ = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__magic_name__ , **self.config_updates , )
SCREAMING_SNAKE_CASE_ = prepare_opt_inputs_dict(__magic_name__ , __magic_name__ )
return config, inputs_dict
def __A ( self : Dict , __magic_name__ : Any , __magic_name__ : str ) -> List[str]:
SCREAMING_SNAKE_CASE_ = TFOPTModel(config=__magic_name__ )
SCREAMING_SNAKE_CASE_ = inputs_dict["input_ids"]
SCREAMING_SNAKE_CASE_ = input_ids[:1, :]
SCREAMING_SNAKE_CASE_ = inputs_dict["attention_mask"][:1, :]
SCREAMING_SNAKE_CASE_ = 1
# first forward pass
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , attention_mask=__magic_name__ , use_cache=__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
SCREAMING_SNAKE_CASE_ = tf.concat([input_ids, next_tokens] , axis=-1 )
SCREAMING_SNAKE_CASE_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , attention_mask=__magic_name__ )[0]
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , attention_mask=__magic_name__ , past_key_values=__magic_name__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
SCREAMING_SNAKE_CASE_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
SCREAMING_SNAKE_CASE_ = output_from_no_past[:, -3:, random_slice_idx]
SCREAMING_SNAKE_CASE_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__magic_name__ , __magic_name__ , rtol=1e-3 )
@require_tf
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCamelCase__ = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = 1_0
def __A ( self : List[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = TFOPTModelTester(self )
SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=__magic_name__ )
def __A ( self : Union[str, Any] ) -> Tuple:
self.config_tester.run_common_tests()
def __A ( self : Any ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__ )
def __A ( self : Optional[int] ) -> Any:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__magic_name__ : int , __magic_name__ : List[str] ):
if hasattr(__magic_name__ , "weight" ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__magic_name__ , "weight" ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
SCREAMING_SNAKE_CASE_ = model_class(config=__magic_name__ )
SCREAMING_SNAKE_CASE_ = _get_word_embedding_weight(__magic_name__ , model.get_input_embeddings() )
SCREAMING_SNAKE_CASE_ = _get_word_embedding_weight(__magic_name__ , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__magic_name__ )
SCREAMING_SNAKE_CASE_ = _get_word_embedding_weight(__magic_name__ , model.get_input_embeddings() )
SCREAMING_SNAKE_CASE_ = _get_word_embedding_weight(__magic_name__ , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
SCREAMING_SNAKE_CASE_ = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __magic_name__ )
# check that weights remain the same after resizing
SCREAMING_SNAKE_CASE_ = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
SCREAMING_SNAKE_CASE_ = False
self.assertTrue(__magic_name__ )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __magic_name__ )
SCREAMING_SNAKE_CASE_ = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
SCREAMING_SNAKE_CASE_ = False
self.assertTrue(__magic_name__ )
def a__ ( __UpperCamelCase ):
return tf.constant(__UpperCamelCase , dtype=tf.intaa )
@require_tf
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = 9_9
def __A ( self : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = tf.ones((4, 1) , dtype=tf.intaa ) * 2
SCREAMING_SNAKE_CASE_ = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
SCREAMING_SNAKE_CASE_ = input_ids.shape[0]
SCREAMING_SNAKE_CASE_ = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self : str ) -> Any:
SCREAMING_SNAKE_CASE_ = TFOPTModel.from_pretrained("facebook/opt-350m" )
SCREAMING_SNAKE_CASE_ = _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
SCREAMING_SNAKE_CASE_ = tf.not_equal(__magic_name__ , model.config.pad_token_id )
with tf.GradientTape():
SCREAMING_SNAKE_CASE_ = model(input_ids=__magic_name__ , attention_mask=__magic_name__ ).last_hidden_state
SCREAMING_SNAKE_CASE_ = (1, 11, 512)
self.assertEqual(output.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = tf.constant(
[[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , __magic_name__ , atol=4e-3 ) )
SCREAMING_SNAKE_CASE_ = tf.function(__magic_name__ , jit_compile=__magic_name__ )
SCREAMING_SNAKE_CASE_ = xla_generate(__magic_name__ , __magic_name__ )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __magic_name__ , atol=4e-2 ) )
@require_tf
@slow
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ) -> Tuple:
super().setUp()
SCREAMING_SNAKE_CASE_ = "facebook/opt-350m"
def __A ( self : int ) -> List[str]:
SCREAMING_SNAKE_CASE_ = TFOPTForCausalLM.from_pretrained(self.path_model )
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained(self.path_model )
SCREAMING_SNAKE_CASE_ = [
"Today is a beautiful day and I want to",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_tensors="tf" , padding=__magic_name__ , add_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
SCREAMING_SNAKE_CASE_ = tf.constant(
[
[1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
[-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
[0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
[6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
] )
self.assertTrue(np.allclose(__magic_name__ , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = tf.function(__magic_name__ , jit_compile=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(__magic_name__ , __magic_name__ , atol=1e-4 ) )
@require_tf
@slow
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@property
def __A ( self : Dict ) -> int:
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def __A ( self : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = "facebook/opt-125m"
SCREAMING_SNAKE_CASE_ = [
"Today is a beautiful day and I want to",
"In the city of New York, the city",
"Paris is the capital of France and the capital",
"Computers and mobile phones have taken over the",
]
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = TFOPTForCausalLM.from_pretrained(__magic_name__ )
for prompt in self.prompts:
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_tensors="tf" ).input_ids
SCREAMING_SNAKE_CASE_ = model.generate(__magic_name__ , max_length=10 )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ )
predicted_outputs += generated_string
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "facebook/opt-350m"
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = TFOPTForCausalLM.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "left"
# use different length sentences to test batching
SCREAMING_SNAKE_CASE_ = [
"Hello, my dog is a little",
"Today, I",
]
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_tensors="tf" , padding=__magic_name__ )
SCREAMING_SNAKE_CASE_ = inputs["input_ids"]
SCREAMING_SNAKE_CASE_ = model.generate(input_ids=__magic_name__ , attention_mask=inputs["attention_mask"] )
SCREAMING_SNAKE_CASE_ = tokenizer(sentences[0] , return_tensors="tf" ).input_ids
SCREAMING_SNAKE_CASE_ = model.generate(input_ids=__magic_name__ )
SCREAMING_SNAKE_CASE_ = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs["attention_mask"][-1] , tf.intaa ) )
SCREAMING_SNAKE_CASE_ = tokenizer(sentences[1] , return_tensors="tf" ).input_ids
SCREAMING_SNAKE_CASE_ = model.generate(input_ids=__magic_name__ , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.decode(output_padded[0] , skip_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [
"Hello, my dog is a little bit of a dork.\nI'm a little bit",
"Today, I was in the middle of a conversation with a friend about the",
]
self.assertListEqual(__magic_name__ , __magic_name__ )
self.assertListEqual(__magic_name__ , [non_padded_sentence, padded_sentence] )
def __A ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "facebook/opt-350m"
SCREAMING_SNAKE_CASE_ = [
"Today is a beautiful day and I want to",
"In the city of San Francisco, the city",
"Paris is the capital of France and the capital",
"Computers and mobile phones have taken over the",
]
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = TFOPTForCausalLM.from_pretrained(__magic_name__ )
for prompt in self.prompts:
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_tensors="tf" ).input_ids
SCREAMING_SNAKE_CASE_ = model.generate(__magic_name__ , max_length=10 )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ )
predicted_outputs += generated_string
self.assertListEqual(__magic_name__ , __magic_name__ )
| 356
|
import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 305
| 0
|
import math
def a__ ( __UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = int(math.sqrt(__UpperCamelCase ) ) # Size of every segment
SCREAMING_SNAKE_CASE_ = [True] * (end + 1)
SCREAMING_SNAKE_CASE_ = []
while start <= end:
if temp[start] is True:
in_prime.append(__UpperCamelCase )
for i in range(start * start , end + 1 , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = False
start += 1
prime += in_prime
SCREAMING_SNAKE_CASE_ = end + 1
SCREAMING_SNAKE_CASE_ = min(2 * end , __UpperCamelCase )
while low <= n:
SCREAMING_SNAKE_CASE_ = [True] * (high - low + 1)
for each in in_prime:
SCREAMING_SNAKE_CASE_ = math.floor(low / each ) * each
if t < low:
t += each
for j in range(__UpperCamelCase , high + 1 , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = False
for j in range(len(__UpperCamelCase ) ):
if temp[j] is True:
prime.append(j + low )
SCREAMING_SNAKE_CASE_ = high + 1
SCREAMING_SNAKE_CASE_ = min(high + end , __UpperCamelCase )
return prime
print(sieve(10**6))
| 357
|
from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "08x" )[-8:]
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
return little_endian_hex
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = b""
for char in message:
bit_string += format(__UpperCamelCase , "08b" ).encode("utf-8" )
SCREAMING_SNAKE_CASE_ = format(len(__UpperCamelCase ) , "064b" ).encode("utf-8" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__UpperCamelCase ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) % 5_1_2 != 0:
raise ValueError("Input must have length that's a multiple of 512" )
for pos in range(0 , len(__UpperCamelCase ) , 5_1_2 ):
SCREAMING_SNAKE_CASE_ = bit_string[pos : pos + 5_1_2]
SCREAMING_SNAKE_CASE_ = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "032b" )
SCREAMING_SNAKE_CASE_ = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__UpperCamelCase , 2 )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return (a + b) % 2**3_2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
if shift < 0:
raise ValueError("Shift must be non-negative" )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = preprocess(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
SCREAMING_SNAKE_CASE_ = 0X67452301
SCREAMING_SNAKE_CASE_ = 0Xefcdab89
SCREAMING_SNAKE_CASE_ = 0X98badcfe
SCREAMING_SNAKE_CASE_ = 0X10325476
SCREAMING_SNAKE_CASE_ = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = aa
SCREAMING_SNAKE_CASE_ = ba
SCREAMING_SNAKE_CASE_ = ca
SCREAMING_SNAKE_CASE_ = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = d ^ (b & (c ^ d))
SCREAMING_SNAKE_CASE_ = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = c ^ (d & (b ^ c))
SCREAMING_SNAKE_CASE_ = (5 * i + 1) % 1_6
elif i <= 4_7:
SCREAMING_SNAKE_CASE_ = b ^ c ^ d
SCREAMING_SNAKE_CASE_ = (3 * i + 5) % 1_6
else:
SCREAMING_SNAKE_CASE_ = c ^ (b | not_aa(__UpperCamelCase ))
SCREAMING_SNAKE_CASE_ = (7 * i) % 1_6
SCREAMING_SNAKE_CASE_ = (f + a + added_consts[i] + block_words[g]) % 2**3_2
SCREAMING_SNAKE_CASE_ = d
SCREAMING_SNAKE_CASE_ = c
SCREAMING_SNAKE_CASE_ = b
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , left_rotate_aa(__UpperCamelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : List[Any] = {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''roberta'''
def __init__( self : int , __magic_name__ : Optional[int]=50_265 , __magic_name__ : List[str]=768 , __magic_name__ : Any=12 , __magic_name__ : List[str]=12 , __magic_name__ : Union[str, Any]=3_072 , __magic_name__ : List[str]="gelu" , __magic_name__ : Union[str, Any]=0.1 , __magic_name__ : Any=0.1 , __magic_name__ : Tuple=512 , __magic_name__ : List[str]=2 , __magic_name__ : Union[str, Any]=0.02 , __magic_name__ : List[str]=1e-12 , __magic_name__ : int=1 , __magic_name__ : Tuple=0 , __magic_name__ : Tuple=2 , __magic_name__ : str="absolute" , __magic_name__ : Optional[Any]=True , __magic_name__ : Any=None , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = classifier_dropout
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def __A ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 358
|
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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ) -> Any:
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = BlipImageProcessor()
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )
SCREAMING_SNAKE_CASE_ = BlipaProcessor(__magic_name__ , __magic_name__ )
processor.save_pretrained(self.tmpdirname )
def __A ( self : str , **__magic_name__ : int ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).tokenizer
def __A ( self : Dict , **__magic_name__ : List[Any] ) -> int:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).image_processor
def __A ( self : int ) -> Any:
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ = [Image.fromarray(np.moveaxis(__magic_name__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE_ = self.get_image_processor(do_normalize=__magic_name__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE_ = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__magic_name__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __magic_name__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __magic_name__ )
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = image_processor(__magic_name__ , return_tensors="np" )
SCREAMING_SNAKE_CASE_ = processor(images=__magic_name__ , 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 : str ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_token_type_ids=__magic_name__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__magic_name__ ):
processor()
def __A ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ = processor.batch_decode(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 305
| 0
|
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Any , __magic_name__ : Optional[int] ) -> Tuple:
if isinstance(__magic_name__ , __magic_name__ ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
SCREAMING_SNAKE_CASE_ = deepcopy(__magic_name__ )
elif os.path.exists(__magic_name__ ):
with io.open(__magic_name__ , "r" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ = json.load(__magic_name__ )
else:
try:
SCREAMING_SNAKE_CASE_ = baseaa.urlsafe_baadecode(__magic_name__ ).decode("utf-8" )
SCREAMING_SNAKE_CASE_ = json.loads(__magic_name__ )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
F'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}''' )
SCREAMING_SNAKE_CASE_ = config
self.set_stage_and_offload()
def __A ( self : int ) -> Union[str, Any]:
# zero stage - this is done as early as possible, before model is created, to allow
# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
SCREAMING_SNAKE_CASE_ = self.get_value("zero_optimization.stage" , -1 )
# offload
SCREAMING_SNAKE_CASE_ = False
if self.is_zeroa() or self.is_zeroa():
SCREAMING_SNAKE_CASE_ = set(["cpu", "nvme"] )
SCREAMING_SNAKE_CASE_ = set(
[
self.get_value("zero_optimization.offload_optimizer.device" ),
self.get_value("zero_optimization.offload_param.device" ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
SCREAMING_SNAKE_CASE_ = True
def __A ( self : Union[str, Any] , __magic_name__ : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.config
# find the config node of interest if it exists
SCREAMING_SNAKE_CASE_ = ds_key_long.split("." )
SCREAMING_SNAKE_CASE_ = nodes.pop()
for node in nodes:
SCREAMING_SNAKE_CASE_ = config.get(__magic_name__ )
if config is None:
return None, ds_key
return config, ds_key
def __A ( self : Any , __magic_name__ : Any , __magic_name__ : int=None ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.find_config_node(__magic_name__ )
if config is None:
return default
return config.get(__magic_name__ , __magic_name__ )
def __A ( self : Dict , __magic_name__ : Union[str, Any] , __magic_name__ : Any=False ) -> int:
SCREAMING_SNAKE_CASE_ = self.config
# find the config node of interest if it exists
SCREAMING_SNAKE_CASE_ = ds_key_long.split("." )
for node in nodes:
SCREAMING_SNAKE_CASE_ = config
SCREAMING_SNAKE_CASE_ = config.get(__magic_name__ )
if config is None:
if must_exist:
raise ValueError(F'''Can\'t find {ds_key_long} entry in the config: {self.config}''' )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(__magic_name__ )
def __A ( self : Any , __magic_name__ : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = self.get_value(__magic_name__ )
return False if value is None else bool(__magic_name__ )
def __A ( self : Optional[int] , __magic_name__ : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = self.get_value(__magic_name__ )
return False if value is None else not bool(__magic_name__ )
def __A ( self : Any ) -> Dict:
return self._stage == 2
def __A ( self : List[str] ) -> Dict:
return self._stage == 3
def __A ( self : List[Any] ) -> Any:
return self._offload
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __magic_name__ : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = engine
def __A ( self : Optional[Any] , __magic_name__ : Optional[int] , **__magic_name__ : Tuple ) -> Optional[Any]:
# runs backpropagation and handles mixed precision
self.engine.backward(__magic_name__ , **__magic_name__ )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : Dict , __magic_name__ : List[str] ) -> Tuple:
super().__init__(__magic_name__ , device_placement=__magic_name__ , scaler=__magic_name__ )
SCREAMING_SNAKE_CASE_ = hasattr(self.optimizer , "overflow" )
def __A ( self : str , __magic_name__ : Tuple=None ) -> Tuple:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def __A ( self : Optional[Any] ) -> Optional[int]:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def __A ( self : Optional[int] ) -> Optional[int]:
if self.__has_overflow__:
return self.optimizer.overflow
return False
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Dict , __magic_name__ : str ) -> str:
super().__init__(__magic_name__ , __magic_name__ )
def __A ( self : Optional[Any] ) -> str:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __magic_name__ : int , __magic_name__ : List[str]=0.001 , __magic_name__ : Tuple=0 , **__magic_name__ : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = params
SCREAMING_SNAKE_CASE_ = lr
SCREAMING_SNAKE_CASE_ = weight_decay
SCREAMING_SNAKE_CASE_ = kwargs
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __magic_name__ : List[Any] , __magic_name__ : Dict=None , __magic_name__ : Optional[int]=0 , **__magic_name__ : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = optimizer
SCREAMING_SNAKE_CASE_ = total_num_steps
SCREAMING_SNAKE_CASE_ = warmup_num_steps
SCREAMING_SNAKE_CASE_ = kwargs
| 359
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ["DPTFeatureExtractor"]
A : str = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[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
A : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : List[str] = {
"google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
"google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''mobilenet_v1'''
def __init__( self : List[Any] , __magic_name__ : Optional[int]=3 , __magic_name__ : List[str]=224 , __magic_name__ : Optional[Any]=1.0 , __magic_name__ : List[str]=8 , __magic_name__ : Any="relu6" , __magic_name__ : List[Any]=True , __magic_name__ : Any=0.999 , __magic_name__ : Any=0.02 , __magic_name__ : Dict=0.001 , **__magic_name__ : List[Any] , ) -> Optional[Any]:
super().__init__(**__magic_name__ )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = depth_multiplier
SCREAMING_SNAKE_CASE_ = min_depth
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = tf_padding
SCREAMING_SNAKE_CASE_ = classifier_dropout_prob
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __A ( self : Any ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def __A ( self : str ) -> Mapping[str, Mapping[int, str]]:
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 : Optional[Any] ) -> float:
return 1e-4
| 360
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
A : int = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Union[str, Any] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 305
| 0
|
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# Return True if there is node that has not iterated.
SCREAMING_SNAKE_CASE_ = [False] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = []
queue.append(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = True
while queue:
SCREAMING_SNAKE_CASE_ = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = u
return visited[t]
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# This array is filled by BFS and to store path
SCREAMING_SNAKE_CASE_ = [-1] * (len(__UpperCamelCase ))
SCREAMING_SNAKE_CASE_ = 0
while bfs(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = float("Inf" )
SCREAMING_SNAKE_CASE_ = sink
while s != source:
# Find the minimum value in select path
SCREAMING_SNAKE_CASE_ = min(__UpperCamelCase , graph[parent[s]][s] )
SCREAMING_SNAKE_CASE_ = parent[s]
max_flow += path_flow
SCREAMING_SNAKE_CASE_ = sink
while v != source:
SCREAMING_SNAKE_CASE_ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
SCREAMING_SNAKE_CASE_ = parent[v]
return max_flow
A : Any = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
A : Optional[int] = 0, 5
print(ford_fulkerson(graph, source, sink))
| 361
|
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_text_model'''
def __init__( self : Optional[Any] , __magic_name__ : Union[str, Any]=30_522 , __magic_name__ : Tuple=768 , __magic_name__ : List[str]=12 , __magic_name__ : Optional[Any]=12 , __magic_name__ : str=3_072 , __magic_name__ : Dict="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : Optional[int]=0.1 , __magic_name__ : List[str]=512 , __magic_name__ : Any=2 , __magic_name__ : Optional[Any]=0.02 , __magic_name__ : int=1e-12 , __magic_name__ : str=0 , __magic_name__ : Optional[Any]="absolute" , __magic_name__ : Optional[Any]=True , **__magic_name__ : Tuple , ) -> Union[str, Any]:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = pad_token_id
@classmethod
def __A ( cls : Any , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_vision_model'''
def __init__( self : List[str] , __magic_name__ : int = 3 , __magic_name__ : int = 600 , __magic_name__ : float = 2.0 , __magic_name__ : float = 3.1 , __magic_name__ : int = 8 , __magic_name__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ : List[int] = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ : List[int] = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ : List[int] = [] , __magic_name__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ : float = 0.25 , __magic_name__ : str = "swish" , __magic_name__ : int = 2_560 , __magic_name__ : str = "mean" , __magic_name__ : float = 0.02 , __magic_name__ : float = 0.001 , __magic_name__ : float = 0.99 , __magic_name__ : float = 0.2 , **__magic_name__ : List[Any] , ) -> Tuple:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = width_coefficient
SCREAMING_SNAKE_CASE_ = depth_coefficient
SCREAMING_SNAKE_CASE_ = depth_divisor
SCREAMING_SNAKE_CASE_ = kernel_sizes
SCREAMING_SNAKE_CASE_ = in_channels
SCREAMING_SNAKE_CASE_ = out_channels
SCREAMING_SNAKE_CASE_ = depthwise_padding
SCREAMING_SNAKE_CASE_ = strides
SCREAMING_SNAKE_CASE_ = num_block_repeats
SCREAMING_SNAKE_CASE_ = expand_ratios
SCREAMING_SNAKE_CASE_ = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dim
SCREAMING_SNAKE_CASE_ = pooling_type
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = batch_norm_eps
SCREAMING_SNAKE_CASE_ = batch_norm_momentum
SCREAMING_SNAKE_CASE_ = drop_connect_rate
SCREAMING_SNAKE_CASE_ = sum(__magic_name__ ) * 4
@classmethod
def __A ( cls : List[str] , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Dict ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align'''
lowerCamelCase__ = True
def __init__( self : Optional[Any] , __magic_name__ : Dict=None , __magic_name__ : List[Any]=None , __magic_name__ : str=640 , __magic_name__ : Any=1.0 , __magic_name__ : Dict=0.02 , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__(**__magic_name__ )
if text_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
SCREAMING_SNAKE_CASE_ = AlignTextConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = AlignVisionConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = projection_dim
SCREAMING_SNAKE_CASE_ = temperature_init_value
SCREAMING_SNAKE_CASE_ = initializer_range
@classmethod
def __A ( cls : List[str] , __magic_name__ : AlignTextConfig , __magic_name__ : AlignVisionConfig , **__magic_name__ : Tuple ) -> Any:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__magic_name__ )
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.text_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 305
| 0
|
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = None
lowerCamelCase__ = BloomTokenizerFast
lowerCamelCase__ = BloomTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = False
lowerCamelCase__ = '''tokenizer_file'''
lowerCamelCase__ = {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}
def __A ( self : str ) -> Tuple:
super().setUp()
SCREAMING_SNAKE_CASE_ = BloomTokenizerFast.from_pretrained("bigscience/tokenizer" )
tokenizer.save_pretrained(self.tmpdirname )
def __A ( self : List[str] , **__magic_name__ : int ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **__magic_name__ )
def __A ( self : Any ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_ = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
SCREAMING_SNAKE_CASE_ = [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]
SCREAMING_SNAKE_CASE_ = tokenizer.batch_encode_plus(__magic_name__ )["input_ids"]
self.assertListEqual(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : Union[str, Any] , __magic_name__ : Tuple=6 ) -> Any:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE_ = self.rust_tokenizer_class.from_pretrained(__magic_name__ , **__magic_name__ )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
SCREAMING_SNAKE_CASE_ = "This is a simple input"
SCREAMING_SNAKE_CASE_ = ["This is a simple input 1", "This is a simple input 2"]
SCREAMING_SNAKE_CASE_ = ("This is a simple input", "This is a pair")
SCREAMING_SNAKE_CASE_ = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
try:
tokenizer_r.encode(__magic_name__ , max_length=__magic_name__ )
tokenizer_r.encode_plus(__magic_name__ , max_length=__magic_name__ )
tokenizer_r.batch_encode_plus(__magic_name__ , max_length=__magic_name__ )
tokenizer_r.encode(__magic_name__ , max_length=__magic_name__ )
tokenizer_r.batch_encode_plus(__magic_name__ , max_length=__magic_name__ )
except ValueError:
self.fail("Bloom Tokenizer should be able to deal with padding" )
SCREAMING_SNAKE_CASE_ = None # Hotfixing padding = None
self.assertRaises(__magic_name__ , tokenizer_r.encode , __magic_name__ , max_length=__magic_name__ , padding="max_length" )
# Simple input
self.assertRaises(__magic_name__ , tokenizer_r.encode_plus , __magic_name__ , max_length=__magic_name__ , padding="max_length" )
# Simple input
self.assertRaises(
__magic_name__ , tokenizer_r.batch_encode_plus , __magic_name__ , max_length=__magic_name__ , padding="max_length" , )
# Pair input
self.assertRaises(__magic_name__ , tokenizer_r.encode , __magic_name__ , max_length=__magic_name__ , padding="max_length" )
# Pair input
self.assertRaises(__magic_name__ , tokenizer_r.encode_plus , __magic_name__ , max_length=__magic_name__ , padding="max_length" )
# Pair input
self.assertRaises(
__magic_name__ , tokenizer_r.batch_encode_plus , __magic_name__ , max_length=__magic_name__ , padding="max_length" , )
def __A ( self : Optional[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_ = load_dataset("xnli" , "all_languages" , split="test" , streaming=__magic_name__ )
SCREAMING_SNAKE_CASE_ = next(iter(__magic_name__ ) )["premise"] # pick up one data
SCREAMING_SNAKE_CASE_ = list(sample_data.values() )
SCREAMING_SNAKE_CASE_ = list(map(tokenizer.encode , __magic_name__ ) )
SCREAMING_SNAKE_CASE_ = [tokenizer.decode(__magic_name__ , clean_up_tokenization_spaces=__magic_name__ ) for x in output_tokens]
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : List[str] ) -> Tuple:
# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
# any sequence length constraints. This test of the parent class will fail since it relies on the
# maximum sequence length of the positoonal embeddings.
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 362
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
SCREAMING_SNAKE_CASE_ = "x = y"
SCREAMING_SNAKE_CASE_ = {"y": 5}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 5, "y": 5} )
def __A ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "y = add_two(x)"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result is None
assert "tried to execute add_two" in out.out
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
def __A ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "x = 3\ny = 5"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
def __A ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "text = f'This is x: {x}.'"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(__magic_name__ , {"x": 3, "text": "This is x: 3."} )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "if x <= 3:\n y = 2\nelse:\n y = 5"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 2} )
SCREAMING_SNAKE_CASE_ = {"x": 8}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 8, "y": 5} )
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertListEqual(__magic_name__ , [3, 5] )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
def __A ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "y = x"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 3} )
def __A ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]\ntest_list[1]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = "x = 0\nfor i in range(3):\n x = i"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"range": range} , state=__magic_name__ )
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 2, "i": 2} )
| 305
| 0
|
def a__ ( __UpperCamelCase ):
assert column_title.isupper()
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) - 1
SCREAMING_SNAKE_CASE_ = 0
while index >= 0:
SCREAMING_SNAKE_CASE_ = (ord(column_title[index] ) - 6_4) * pow(2_6 , __UpperCamelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 363
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__UpperCamelCase )] )
SCREAMING_SNAKE_CASE_ = np.array(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __UpperCamelCase ) ) , x.transpose() ) , __UpperCamelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = (1, 2, 1)
SCREAMING_SNAKE_CASE_ = (1, 1, 0, 7)
SCREAMING_SNAKE_CASE_ = SARIMAX(
__UpperCamelCase , exog=__UpperCamelCase , order=__UpperCamelCase , seasonal_order=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = model.fit(disp=__UpperCamelCase , maxiter=6_0_0 , method="nm" )
SCREAMING_SNAKE_CASE_ = model_fit.predict(1 , len(__UpperCamelCase ) , exog=[test_match] )
return result[0]
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = SVR(kernel="rbf" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = regressor.predict(__UpperCamelCase )
return y_pred[0]
def a__ ( __UpperCamelCase ):
train_user.sort()
SCREAMING_SNAKE_CASE_ = np.percentile(__UpperCamelCase , 2_5 )
SCREAMING_SNAKE_CASE_ = np.percentile(__UpperCamelCase , 7_5 )
SCREAMING_SNAKE_CASE_ = qa - qa
SCREAMING_SNAKE_CASE_ = qa - (iqr * 0.1)
return low_lim
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
for i in list_vote:
if i > actual_result:
SCREAMING_SNAKE_CASE_ = not_safe + 1
else:
if abs(abs(__UpperCamelCase ) - abs(__UpperCamelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
A : Optional[Any] = pd.DataFrame(
data_input, columns=["total_user", "total_even", "days"]
)
A : Union[str, Any] = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : List[str] = normalize_df[:, 0].tolist()
A : int = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Tuple = x[: len(x) - 1]
A : str = x[len(x) - 1 :]
# for linear regression & sarimax
A : Tuple = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : List[Any] = total_date[len(total_date) - 1 :]
A : List[Any] = total_user[len(total_user) - 1 :]
A : Optional[Any] = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : Optional[int] = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : str = "" if data_safety_checker(res_vote, tst_user) else "not "
print("Today's data is {not_str}safe.")
| 305
| 0
|
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 : Optional[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 lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(SCREAMING_SNAKE_CASE__ )} )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
lowerCamelCase__ = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
lowerCamelCase__ = field(
default=1_2_8 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
lowerCamelCase__ = field(
default=6_4 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
lowerCamelCase__ = field(
default=3_0 , 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.'''
)
} , )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
lowerCamelCase__ = field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
lowerCamelCase__ = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
lowerCamelCase__ = field(
default=2_0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
lowerCamelCase__ = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
lowerCamelCase__ = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''train'''
lowerCamelCase__ = '''dev'''
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
def __init__( self : Tuple , __magic_name__ : SquadDataTrainingArguments , __magic_name__ : PreTrainedTokenizer , __magic_name__ : Optional[int] = None , __magic_name__ : Union[str, Split] = Split.train , __magic_name__ : Optional[bool] = False , __magic_name__ : Optional[str] = None , __magic_name__ : Optional[str] = "pt" , ) -> Tuple:
SCREAMING_SNAKE_CASE_ = args
SCREAMING_SNAKE_CASE_ = is_language_sensitive
SCREAMING_SNAKE_CASE_ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__magic_name__ , __magic_name__ ):
try:
SCREAMING_SNAKE_CASE_ = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
SCREAMING_SNAKE_CASE_ = mode
# Load data features from cache or dataset file
SCREAMING_SNAKE_CASE_ = "v2" if args.version_2_with_negative else "v1"
SCREAMING_SNAKE_CASE_ = 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.
SCREAMING_SNAKE_CASE_ = cached_features_file + ".lock"
with FileLock(__magic_name__ ):
if os.path.exists(__magic_name__ ) and not args.overwrite_cache:
SCREAMING_SNAKE_CASE_ = time.time()
SCREAMING_SNAKE_CASE_ = torch.load(__magic_name__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
SCREAMING_SNAKE_CASE_ = self.old_features["features"]
SCREAMING_SNAKE_CASE_ = self.old_features.get("dataset" , __magic_name__ )
SCREAMING_SNAKE_CASE_ = self.old_features.get("examples" , __magic_name__ )
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:
SCREAMING_SNAKE_CASE_ = self.processor.get_dev_examples(args.data_dir )
else:
SCREAMING_SNAKE_CASE_ = self.processor.get_train_examples(args.data_dir )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=__magic_name__ , 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=__magic_name__ , )
SCREAMING_SNAKE_CASE_ = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , __magic_name__ , )
# ^ 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 : Any ) -> Union[str, Any]:
return len(self.features )
def __getitem__( self : int , __magic_name__ : int ) -> Dict[str, torch.Tensor]:
# Convert to Tensors and build dataset
SCREAMING_SNAKE_CASE_ = self.features[i]
SCREAMING_SNAKE_CASE_ = torch.tensor(feature.input_ids , dtype=torch.long )
SCREAMING_SNAKE_CASE_ = torch.tensor(feature.attention_mask , dtype=torch.long )
SCREAMING_SNAKE_CASE_ = torch.tensor(feature.token_type_ids , dtype=torch.long )
SCREAMING_SNAKE_CASE_ = torch.tensor(feature.cls_index , dtype=torch.long )
SCREAMING_SNAKE_CASE_ = torch.tensor(feature.p_mask , dtype=torch.float )
SCREAMING_SNAKE_CASE_ = torch.tensor(feature.is_impossible , dtype=torch.float )
SCREAMING_SNAKE_CASE_ = {
"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:
SCREAMING_SNAKE_CASE_ = torch.tensor(feature.start_position , dtype=torch.long )
SCREAMING_SNAKE_CASE_ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 364
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
"SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwinForImageClassification",
"SwinForMaskedImageModeling",
"SwinModel",
"SwinPreTrainedModel",
"SwinBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
"TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSwinForImageClassification",
"TFSwinForMaskedImageModeling",
"TFSwinModel",
"TFSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViltImageProcessor'''
lowerCamelCase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[int] , __magic_name__ : str=None , __magic_name__ : List[str]=None , **__magic_name__ : 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`." )
super().__init__(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = self.image_processor
def __call__( self : List[str] , __magic_name__ : List[str] , __magic_name__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __magic_name__ : bool = True , __magic_name__ : Union[bool, str, PaddingStrategy] = False , __magic_name__ : Union[bool, str, TruncationStrategy] = None , __magic_name__ : Optional[int] = None , __magic_name__ : int = 0 , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = True , __magic_name__ : Optional[Union[str, TensorType]] = None , **__magic_name__ : str , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE_ = self.tokenizer(
text=__magic_name__ , add_special_tokens=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , max_length=__magic_name__ , stride=__magic_name__ , pad_to_multiple_of=__magic_name__ , return_token_type_ids=__magic_name__ , return_attention_mask=__magic_name__ , return_overflowing_tokens=__magic_name__ , return_special_tokens_mask=__magic_name__ , return_offsets_mapping=__magic_name__ , return_length=__magic_name__ , verbose=__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ , )
# add pixel_values + pixel_mask
SCREAMING_SNAKE_CASE_ = self.image_processor(__magic_name__ , return_tensors=__magic_name__ )
encoding.update(__magic_name__ )
return encoding
def __A ( self : Optional[int] , *__magic_name__ : List[Any] , **__magic_name__ : Optional[Any] ) -> Any:
return self.tokenizer.batch_decode(*__magic_name__ , **__magic_name__ )
def __A ( self : Dict , *__magic_name__ : List[Any] , **__magic_name__ : Union[str, Any] ) -> str:
return self.tokenizer.decode(*__magic_name__ , **__magic_name__ )
@property
def __A ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __A ( self : Dict ) -> List[Any]:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __magic_name__ , )
return self.image_processor_class
@property
def __A ( self : int ) -> List[Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __magic_name__ , )
return self.image_processor
| 365
|
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
A : Union[str, Any] = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@classmethod
def __A ( cls : Any ) -> Dict:
SCREAMING_SNAKE_CASE_ = TOKEN
HfFolder.save_token(__magic_name__ )
@classmethod
def __A ( cls : Optional[int] ) -> Tuple:
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__magic_name__ , repo_id="test-model-flax" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
__magic_name__ , repo_id="valid_org/test-model-flax-org" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
SCREAMING_SNAKE_CASE_ = False
return models_are_equal
@require_flax
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) , max_shard_size="10KB" )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def __A ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
| 305
| 0
|
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = inspect.getfile(accelerate.test_utils )
lowerCamelCase__ = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase__ = ['''accelerate''', '''launch''']
lowerCamelCase__ = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase__ = '''default_config.yaml'''
lowerCamelCase__ = config_folder / config_file
lowerCamelCase__ = config_folder / '''_default_config.yaml'''
lowerCamelCase__ = Path('''tests/test_configs''' )
@classmethod
def __A ( cls : Dict ) -> Any:
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def __A ( cls : Dict ) -> Dict:
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def __A ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def __A ( self : int ) -> List[Any]:
for config in sorted(self.test_config_path.glob("**/*.yaml" ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ["--config_file", str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def __A ( self : List[str] ) -> str:
execute_subprocess_async(["accelerate", "test"] , env=os.environ.copy() )
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = '''test-tpu'''
lowerCamelCase__ = '''us-central1-a'''
lowerCamelCase__ = '''ls'''
lowerCamelCase__ = ['''accelerate''', '''tpu-config''']
lowerCamelCase__ = '''cd /usr/share'''
lowerCamelCase__ = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase__ = '''Running gcloud compute tpus tpu-vm ssh'''
def __A ( self : List[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd
+ ["--command", self.command, "--tpu_zone", self.tpu_zone, "--tpu_name", self.tpu_name, "--debug"] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def __A ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command",
self.command,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def __A ( self : Optional[int] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--debug"] , return_stdout=__magic_name__ )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def __A ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--command", self.command, "--debug"] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def __A ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--command",
self.command,
"--command",
"echo \"Hello World\"",
"--debug",
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def __A ( self : Optional[int] ) -> Any:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd
+ ["--config_file", "tests/test_configs/latest.yaml", "--command_file", self.command_file, "--debug"] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def __A ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command_file",
self.command_file,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def __A ( self : int ) -> int:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--install_accelerate", "--debug"] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--install_accelerate",
"--accelerate_version",
"12.0.0",
"--debug",
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
| 366
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : Union[str, Any] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def a__ ( ):
SCREAMING_SNAKE_CASE_ = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
SCREAMING_SNAKE_CASE_ = get_sagemaker_input()
else:
SCREAMING_SNAKE_CASE_ = get_cluster_input()
return config
def a__ ( __UpperCamelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser("config" , description=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Accelerate config command" , description=__UpperCamelCase )
parser.add_argument(
"--config_file" , default=__UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_user_input()
if args.config_file is not None:
SCREAMING_SNAKE_CASE_ = args.config_file
else:
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__UpperCamelCase )
else:
config.to_yaml_file(__UpperCamelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = config_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
config_command(__UpperCamelCase )
if __name__ == "__main__":
main()
| 305
| 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 (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
A : Dict = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''pixel_values''']
def __init__( self : Union[str, Any] , __magic_name__ : bool = True , __magic_name__ : Optional[Dict[str, int]] = None , __magic_name__ : PILImageResampling = PILImageResampling.BILINEAR , __magic_name__ : bool = True , __magic_name__ : Dict[str, int] = None , __magic_name__ : bool = True , __magic_name__ : Union[int, float] = 1 / 255 , __magic_name__ : bool = True , __magic_name__ : Optional[Union[float, List[float]]] = None , __magic_name__ : Optional[Union[float, List[float]]] = None , **__magic_name__ : List[str] , ) -> None:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = size if size is not None else {"shortest_edge": 256}
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
SCREAMING_SNAKE_CASE_ = crop_size if crop_size is not None else {"height": 224, "width": 224}
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ )
SCREAMING_SNAKE_CASE_ = do_resize
SCREAMING_SNAKE_CASE_ = size
SCREAMING_SNAKE_CASE_ = resample
SCREAMING_SNAKE_CASE_ = do_center_crop
SCREAMING_SNAKE_CASE_ = crop_size
SCREAMING_SNAKE_CASE_ = do_rescale
SCREAMING_SNAKE_CASE_ = rescale_factor
SCREAMING_SNAKE_CASE_ = do_normalize
SCREAMING_SNAKE_CASE_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __A ( self : List[Any] , __magic_name__ : np.ndarray , __magic_name__ : Dict[str, int] , __magic_name__ : PILImageResampling = PILImageResampling.BICUBIC , __magic_name__ : Optional[Union[str, ChannelDimension]] = None , **__magic_name__ : str , ) -> np.ndarray:
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
SCREAMING_SNAKE_CASE_ = get_resize_output_image_size(__magic_name__ , size=size["shortest_edge"] , default_to_square=__magic_name__ )
return resize(__magic_name__ , size=__magic_name__ , resample=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __A ( self : List[Any] , __magic_name__ : np.ndarray , __magic_name__ : Dict[str, int] , __magic_name__ : Optional[Union[str, ChannelDimension]] = None , **__magic_name__ : Any , ) -> np.ndarray:
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ )
return center_crop(__magic_name__ , size=(size["height"], size["width"]) , data_format=__magic_name__ , **__magic_name__ )
def __A ( self : Union[str, Any] , __magic_name__ : np.ndarray , __magic_name__ : float , __magic_name__ : Optional[Union[str, ChannelDimension]] = None , **__magic_name__ : Union[str, Any] ) -> np.ndarray:
return rescale(__magic_name__ , scale=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __A ( self : Union[str, Any] , __magic_name__ : np.ndarray , __magic_name__ : Union[float, List[float]] , __magic_name__ : Union[float, List[float]] , __magic_name__ : Optional[Union[str, ChannelDimension]] = None , **__magic_name__ : str , ) -> np.ndarray:
return normalize(__magic_name__ , mean=__magic_name__ , std=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __A ( self : str , __magic_name__ : ImageInput , __magic_name__ : Optional[bool] = None , __magic_name__ : Dict[str, int] = None , __magic_name__ : PILImageResampling = None , __magic_name__ : bool = None , __magic_name__ : Dict[str, int] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[float] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[Union[float, List[float]]] = None , __magic_name__ : Optional[Union[float, List[float]]] = None , __magic_name__ : Optional[Union[str, TensorType]] = None , __magic_name__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **__magic_name__ : Dict , ) -> Any:
SCREAMING_SNAKE_CASE_ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE_ = size if size is not None else self.size
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
SCREAMING_SNAKE_CASE_ = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE_ = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE_ = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE_ = get_size_dict(__magic_name__ )
SCREAMING_SNAKE_CASE_ = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE_ = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE_ = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE_ = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE_ = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE_ = make_list_of_images(__magic_name__ )
if not valid_images(__magic_name__ ):
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." )
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.
SCREAMING_SNAKE_CASE_ = [to_numpy_array(__magic_name__ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE_ = [self.resize(image=__magic_name__ , size=__magic_name__ , resample=__magic_name__ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE_ = [self.center_crop(image=__magic_name__ , size=__magic_name__ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE_ = [self.rescale(image=__magic_name__ , scale=__magic_name__ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE_ = [self.normalize(image=__magic_name__ , mean=__magic_name__ , std=__magic_name__ ) for image in images]
SCREAMING_SNAKE_CASE_ = [to_channel_dimension_format(__magic_name__ , __magic_name__ ) for image in images]
SCREAMING_SNAKE_CASE_ = {"pixel_values": images}
return BatchFeature(data=__magic_name__ , tensor_type=__magic_name__ )
| 367
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(default='''summarization''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
lowerCamelCase__ = Features({'''text''': Value('''string''' )} )
lowerCamelCase__ = Features({'''summary''': Value('''string''' )} )
lowerCamelCase__ = "text"
lowerCamelCase__ = "summary"
@property
def __A ( self : Dict ) -> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 305
| 0
|
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
A : Optional[Any] = ["bert-base-uncased", "bert-base-cased"]
A : int = "hf-internal-testing/tiny-bert-tf-only"
if is_tf_available():
class lowerCamelCase (tf.keras.Model ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Union[str, Any] ) -> Optional[Any]:
super().__init__()
SCREAMING_SNAKE_CASE_ = tokenizer
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = TFAutoModel.from_config(__magic_name__ )
def __A ( self : Union[str, Any] , __magic_name__ : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.bert(**__magic_name__ )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : Any ) -> Dict:
super().setUp()
SCREAMING_SNAKE_CASE_ = [
BertTokenizer.from_pretrained(__magic_name__ ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
SCREAMING_SNAKE_CASE_ = [TFBertTokenizer.from_pretrained(__magic_name__ ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(__magic_name__ , use_fast_bert_tokenizer=__magic_name__ )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
SCREAMING_SNAKE_CASE_ = [
"This is a straightforward English test sentence.",
"This one has some weird characters\rto\nsee\r\nif those\u00E9break things.",
"Now we're going to add some Chinese: 一 二 三 一二三",
"And some much more rare Chinese: 齉 堃 齉堃",
"Je vais aussi écrire en français pour tester les accents",
"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
]
SCREAMING_SNAKE_CASE_ = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def __A ( self : str ) -> Optional[Any]:
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_tensors="tf" , padding="longest" )
SCREAMING_SNAKE_CASE_ = tf_tokenizer(__magic_name__ )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def __A ( self : Optional[int] ) -> Optional[Any]:
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE_ = tf_tokenizer(self.paired_sentences )
SCREAMING_SNAKE_CASE_ = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def __A ( self : Dict ) -> Optional[int]:
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE_ = tf.function(__magic_name__ )
for test_inputs in (self.test_sentences, self.paired_sentences):
SCREAMING_SNAKE_CASE_ = tf.constant(__magic_name__ )
SCREAMING_SNAKE_CASE_ = compiled_tokenizer(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tf_tokenizer(__magic_name__ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def __A ( self : Union[str, Any] ) -> Dict:
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE_ = ModelToSave(tokenizer=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tf.convert_to_tensor(self.test_sentences )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE_ = Path(__magic_name__ ) / "saved.model"
model.save(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tf.keras.models.load_model(__magic_name__ )
SCREAMING_SNAKE_CASE_ = loaded_model(__magic_name__ )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1e-5 )
| 368
|
from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None , __magic_name__ : List[str]=2_048 ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = config.__dict__
SCREAMING_SNAKE_CASE_ = modal_hidden_size
if num_labels:
SCREAMING_SNAKE_CASE_ = num_labels
| 305
| 0
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Optional[Any] = "▁"
A : str = {"vocab_file": "sentencepiece.bpe.model"}
A : str = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
),
}
}
A : Optional[Any] = {
"facebook/nllb-200-distilled-600M": 10_24,
}
# fmt: off
A : Any = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
lowerCamelCase__ = []
lowerCamelCase__ = []
def __init__( self : List[str] , __magic_name__ : Any , __magic_name__ : int="<s>" , __magic_name__ : Optional[int]="</s>" , __magic_name__ : List[str]="</s>" , __magic_name__ : Dict="<s>" , __magic_name__ : Dict="<unk>" , __magic_name__ : Dict="<pad>" , __magic_name__ : Optional[Any]="<mask>" , __magic_name__ : Dict=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Optional[Dict[str, Any]] = None , __magic_name__ : List[Any]=None , __magic_name__ : Union[str, Any]=False , **__magic_name__ : int , ) -> List[Any]:
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE_ = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else mask_token
SCREAMING_SNAKE_CASE_ = {} if sp_model_kwargs is None else sp_model_kwargs
SCREAMING_SNAKE_CASE_ = legacy_behaviour
super().__init__(
bos_token=__magic_name__ , eos_token=__magic_name__ , unk_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , tokenizer_file=__magic_name__ , src_lang=__magic_name__ , tgt_lang=__magic_name__ , additional_special_tokens=__magic_name__ , sp_model_kwargs=self.sp_model_kwargs , legacy_behaviour=__magic_name__ , **__magic_name__ , )
SCREAMING_SNAKE_CASE_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__magic_name__ ) )
SCREAMING_SNAKE_CASE_ = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
# Mimic fairseq token-to-id alignment for the first 4 token
SCREAMING_SNAKE_CASE_ = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = len(self.sp_model )
SCREAMING_SNAKE_CASE_ = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__magic_name__ )
}
SCREAMING_SNAKE_CASE_ = {v: k for k, v in self.lang_code_to_id.items()}
SCREAMING_SNAKE_CASE_ = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
SCREAMING_SNAKE_CASE_ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
SCREAMING_SNAKE_CASE_ = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
SCREAMING_SNAKE_CASE_ = src_lang if src_lang is not None else "eng_Latn"
SCREAMING_SNAKE_CASE_ = self.lang_code_to_id[self._src_lang]
SCREAMING_SNAKE_CASE_ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = self.__dict__.copy()
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Dict , __magic_name__ : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def __A ( self : Any ) -> List[Any]:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def __A ( self : List[str] ) -> str:
return self._src_lang
@src_lang.setter
def __A ( self : Dict , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __A ( self : Tuple , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None , __magic_name__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__magic_name__ , token_ids_a=__magic_name__ , already_has_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [1] * len(self.prefix_tokens )
SCREAMING_SNAKE_CASE_ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__magic_name__ )) + suffix_ones
return prefix_ones + ([0] * len(__magic_name__ )) + ([0] * len(__magic_name__ )) + suffix_ones
def __A ( self : int , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __A ( self : str , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE_ = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __A ( self : Tuple , __magic_name__ : Optional[int] , __magic_name__ : str , __magic_name__ : Optional[str] , __magic_name__ : Optional[str] , **__magic_name__ : Optional[int] ) -> Optional[Any]:
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
SCREAMING_SNAKE_CASE_ = src_lang
SCREAMING_SNAKE_CASE_ = self(__magic_name__ , add_special_tokens=__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.convert_tokens_to_ids(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tgt_lang_id
return inputs
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = {self.convert_ids_to_tokens(__magic_name__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __A ( self : Dict , __magic_name__ : str ) -> List[str]:
return self.sp_model.encode(__magic_name__ , out_type=__magic_name__ )
def __A ( self : Any , __magic_name__ : Dict ) -> Any:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE_ = self.sp_model.PieceToId(__magic_name__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __A ( self : Optional[Any] , __magic_name__ : Tuple ) -> Tuple:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __A ( self : Union[str, Any] , __magic_name__ : List[str] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "".join(__magic_name__ ).replace(__magic_name__ , " " ).strip()
return out_string
def __A ( self : Any , __magic_name__ : str , __magic_name__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__magic_name__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE_ = os.path.join(
__magic_name__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__magic_name__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __magic_name__ )
elif not os.path.isfile(self.vocab_file ):
with open(__magic_name__ , "wb" ) as fi:
SCREAMING_SNAKE_CASE_ = self.sp_model.serialized_model_proto()
fi.write(__magic_name__ )
return (out_vocab_file,)
def __A ( self : Tuple , __magic_name__ : List[str] , __magic_name__ : str = "eng_Latn" , __magic_name__ : Optional[List[str]] = None , __magic_name__ : str = "fra_Latn" , **__magic_name__ : Union[str, Any] , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE_ = src_lang
SCREAMING_SNAKE_CASE_ = tgt_lang
return super().prepare_seqaseq_batch(__magic_name__ , __magic_name__ , **__magic_name__ )
def __A ( self : Optional[Any] ) -> Optional[Any]:
return self.set_src_lang_special_tokens(self.src_lang )
def __A ( self : Union[str, Any] ) -> List[str]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __A ( self : List[str] , __magic_name__ : List[Any] ) -> None:
SCREAMING_SNAKE_CASE_ = self.lang_code_to_id[src_lang]
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE_ = [self.cur_lang_code]
SCREAMING_SNAKE_CASE_ = [self.eos_token_id]
def __A ( self : List[str] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = self.lang_code_to_id[lang]
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE_ = [self.cur_lang_code]
SCREAMING_SNAKE_CASE_ = [self.eos_token_id]
| 369
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViltImageProcessor'''
lowerCamelCase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[int] , __magic_name__ : str=None , __magic_name__ : List[str]=None , **__magic_name__ : 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`." )
super().__init__(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = self.image_processor
def __call__( self : List[str] , __magic_name__ : List[str] , __magic_name__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __magic_name__ : bool = True , __magic_name__ : Union[bool, str, PaddingStrategy] = False , __magic_name__ : Union[bool, str, TruncationStrategy] = None , __magic_name__ : Optional[int] = None , __magic_name__ : int = 0 , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = True , __magic_name__ : Optional[Union[str, TensorType]] = None , **__magic_name__ : str , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE_ = self.tokenizer(
text=__magic_name__ , add_special_tokens=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , max_length=__magic_name__ , stride=__magic_name__ , pad_to_multiple_of=__magic_name__ , return_token_type_ids=__magic_name__ , return_attention_mask=__magic_name__ , return_overflowing_tokens=__magic_name__ , return_special_tokens_mask=__magic_name__ , return_offsets_mapping=__magic_name__ , return_length=__magic_name__ , verbose=__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ , )
# add pixel_values + pixel_mask
SCREAMING_SNAKE_CASE_ = self.image_processor(__magic_name__ , return_tensors=__magic_name__ )
encoding.update(__magic_name__ )
return encoding
def __A ( self : Optional[int] , *__magic_name__ : List[Any] , **__magic_name__ : Optional[Any] ) -> Any:
return self.tokenizer.batch_decode(*__magic_name__ , **__magic_name__ )
def __A ( self : Dict , *__magic_name__ : List[Any] , **__magic_name__ : Union[str, Any] ) -> str:
return self.tokenizer.decode(*__magic_name__ , **__magic_name__ )
@property
def __A ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __A ( self : Dict ) -> List[Any]:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __magic_name__ , )
return self.image_processor_class
@property
def __A ( self : int ) -> List[Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __magic_name__ , )
return self.image_processor
| 305
| 0
|
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def a__ ( __UpperCamelCase ):
# vision encoder
if "img_encoder.pos_embed" in name:
SCREAMING_SNAKE_CASE_ = name.replace("img_encoder.pos_embed" , "vision_model.embeddings.position_embeddings" )
if "img_encoder.patch_embed.proj" in name:
SCREAMING_SNAKE_CASE_ = name.replace("img_encoder.patch_embed.proj" , "vision_model.embeddings.patch_embeddings.projection" )
if "img_encoder.patch_embed.norm" in name:
SCREAMING_SNAKE_CASE_ = name.replace("img_encoder.patch_embed.norm" , "vision_model.embeddings.layernorm" )
if "img_encoder.layers" in name:
SCREAMING_SNAKE_CASE_ = name.replace("img_encoder.layers" , "vision_model.encoder.stages" )
if "blocks" in name and "res" not in name:
SCREAMING_SNAKE_CASE_ = name.replace("blocks" , "layers" )
if "attn" in name and "pre_assign" not in name:
SCREAMING_SNAKE_CASE_ = name.replace("attn" , "self_attn" )
if "proj" in name and "self_attn" in name and "text" not in name:
SCREAMING_SNAKE_CASE_ = name.replace("proj" , "out_proj" )
if "pre_assign_attn.attn.proj" in name:
SCREAMING_SNAKE_CASE_ = name.replace("pre_assign_attn.attn.proj" , "pre_assign_attn.attn.out_proj" )
if "norm1" in name:
SCREAMING_SNAKE_CASE_ = name.replace("norm1" , "layer_norm1" )
if "norm2" in name and "pre_assign" not in name:
SCREAMING_SNAKE_CASE_ = name.replace("norm2" , "layer_norm2" )
if "img_encoder.norm" in name:
SCREAMING_SNAKE_CASE_ = name.replace("img_encoder.norm" , "vision_model.layernorm" )
# text encoder
if "text_encoder.token_embedding" in name:
SCREAMING_SNAKE_CASE_ = name.replace("text_encoder.token_embedding" , "text_model.embeddings.token_embedding" )
if "text_encoder.positional_embedding" in name:
SCREAMING_SNAKE_CASE_ = name.replace("text_encoder.positional_embedding" , "text_model.embeddings.position_embedding.weight" )
if "text_encoder.transformer.resblocks." in name:
SCREAMING_SNAKE_CASE_ = name.replace("text_encoder.transformer.resblocks." , "text_model.encoder.layers." )
if "ln_1" in name:
SCREAMING_SNAKE_CASE_ = name.replace("ln_1" , "layer_norm1" )
if "ln_2" in name:
SCREAMING_SNAKE_CASE_ = name.replace("ln_2" , "layer_norm2" )
if "c_fc" in name:
SCREAMING_SNAKE_CASE_ = name.replace("c_fc" , "fc1" )
if "c_proj" in name:
SCREAMING_SNAKE_CASE_ = name.replace("c_proj" , "fc2" )
if "text_encoder" in name:
SCREAMING_SNAKE_CASE_ = name.replace("text_encoder" , "text_model" )
if "ln_final" in name:
SCREAMING_SNAKE_CASE_ = name.replace("ln_final" , "final_layer_norm" )
# projection layers
if "img_projector.linear_hidden." in name:
SCREAMING_SNAKE_CASE_ = name.replace("img_projector.linear_hidden." , "visual_projection." )
if "img_projector.linear_out." in name:
SCREAMING_SNAKE_CASE_ = name.replace("img_projector.linear_out." , "visual_projection.3." )
if "text_projector.linear_hidden" in name:
SCREAMING_SNAKE_CASE_ = name.replace("text_projector.linear_hidden" , "text_projection" )
if "text_projector.linear_out" in name:
SCREAMING_SNAKE_CASE_ = name.replace("text_projector.linear_out" , "text_projection.3" )
return name
def a__ ( __UpperCamelCase , __UpperCamelCase ):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE_ = orig_state_dict.pop(__UpperCamelCase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
SCREAMING_SNAKE_CASE_ = key.split("." )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = int(key_split[2] ), int(key_split[4] )
SCREAMING_SNAKE_CASE_ = config.vision_config.hidden_size
if "weight" in key:
SCREAMING_SNAKE_CASE_ = val[:dim, :]
SCREAMING_SNAKE_CASE_ = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE_ = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE_ = val[:dim]
SCREAMING_SNAKE_CASE_ = val[dim : dim * 2]
SCREAMING_SNAKE_CASE_ = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
SCREAMING_SNAKE_CASE_ = key.split("." )
SCREAMING_SNAKE_CASE_ = int(key_split[3] )
SCREAMING_SNAKE_CASE_ = config.text_config.hidden_size
if "weight" in key:
SCREAMING_SNAKE_CASE_ = val[:dim, :]
SCREAMING_SNAKE_CASE_ = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE_ = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE_ = val[:dim]
SCREAMING_SNAKE_CASE_ = val[dim : dim * 2]
SCREAMING_SNAKE_CASE_ = val[-dim:]
else:
SCREAMING_SNAKE_CASE_ = rename_key(__UpperCamelCase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
SCREAMING_SNAKE_CASE_ = val.squeeze_()
else:
SCREAMING_SNAKE_CASE_ = val
return orig_state_dict
def a__ ( ):
SCREAMING_SNAKE_CASE_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE_ = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="groupvit-gcc-yfcc" , __UpperCamelCase=False ):
SCREAMING_SNAKE_CASE_ = GroupViTConfig()
SCREAMING_SNAKE_CASE_ = GroupViTModel(__UpperCamelCase ).eval()
SCREAMING_SNAKE_CASE_ = torch.load(__UpperCamelCase , map_location="cpu" )["model"]
SCREAMING_SNAKE_CASE_ = convert_state_dict(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = model.load_state_dict(__UpperCamelCase , strict=__UpperCamelCase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCamelCase ) == 0)
# verify result
SCREAMING_SNAKE_CASE_ = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32" )
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = processor(text=["a photo of a cat", "a photo of a dog"] , images=__UpperCamelCase , padding=__UpperCamelCase , return_tensors="pt" )
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__UpperCamelCase )
if model_name == "groupvit-gcc-yfcc":
SCREAMING_SNAKE_CASE_ = torch.tensor([[13.3523, 6.3629]] )
elif model_name == "groupvit-gcc-redcaps":
SCREAMING_SNAKE_CASE_ = torch.tensor([[16.1873, 8.6230]] )
else:
raise ValueError(F'''Model name {model_name} not supported.''' )
assert torch.allclose(outputs.logits_per_image , __UpperCamelCase , atol=1E-3 )
processor.save_pretrained(__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
print("Successfully saved processor and model to" , __UpperCamelCase )
if push_to_hub:
print("Pushing to the hub..." )
processor.push_to_hub(__UpperCamelCase , organization="nielsr" )
model.push_to_hub(__UpperCamelCase , organization="nielsr" )
if __name__ == "__main__":
A : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
)
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
parser.add_argument(
"--model_name",
default="groupvit-gccy-fcc",
type=str,
help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
)
A : Optional[Any] = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 370
|
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 ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : Optional[int] = {
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''table-transformer'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : List[Any] , __magic_name__ : Optional[Any]=True , __magic_name__ : Dict=None , __magic_name__ : Any=3 , __magic_name__ : List[str]=100 , __magic_name__ : Union[str, Any]=6 , __magic_name__ : Dict=2_048 , __magic_name__ : str=8 , __magic_name__ : int=6 , __magic_name__ : List[Any]=2_048 , __magic_name__ : Optional[int]=8 , __magic_name__ : Optional[int]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Optional[Any]=True , __magic_name__ : List[Any]="relu" , __magic_name__ : List[str]=256 , __magic_name__ : List[str]=0.1 , __magic_name__ : int=0.0 , __magic_name__ : Optional[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : str=1.0 , __magic_name__ : int=False , __magic_name__ : Dict="sine" , __magic_name__ : Union[str, Any]="resnet50" , __magic_name__ : Optional[Any]=True , __magic_name__ : str=False , __magic_name__ : List[str]=1 , __magic_name__ : int=5 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Tuple=1 , __magic_name__ : Optional[int]=1 , __magic_name__ : Optional[Any]=5 , __magic_name__ : Optional[int]=2 , __magic_name__ : Union[str, Any]=0.1 , **__magic_name__ : Tuple , ) -> str:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = backbone_config.get("model_type" )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE_ = config_class.from_dict(__magic_name__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None, None, None
SCREAMING_SNAKE_CASE_ = use_timm_backbone
SCREAMING_SNAKE_CASE_ = backbone_config
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = num_queries
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = init_xavier_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = auxiliary_loss
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = backbone
SCREAMING_SNAKE_CASE_ = use_pretrained_backbone
SCREAMING_SNAKE_CASE_ = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE_ = class_cost
SCREAMING_SNAKE_CASE_ = bbox_cost
SCREAMING_SNAKE_CASE_ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE_ = mask_loss_coefficient
SCREAMING_SNAKE_CASE_ = dice_loss_coefficient
SCREAMING_SNAKE_CASE_ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE_ = giou_loss_coefficient
SCREAMING_SNAKE_CASE_ = eos_coefficient
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def __A ( self : Union[str, Any] ) -> int:
return self.encoder_attention_heads
@property
def __A ( self : Any ) -> int:
return self.d_model
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def __A ( self : Any ) -> float:
return 1e-5
@property
def __A ( self : int ) -> int:
return 12
| 305
| 0
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : Union[str, Any] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def a__ ( ):
SCREAMING_SNAKE_CASE_ = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
SCREAMING_SNAKE_CASE_ = get_sagemaker_input()
else:
SCREAMING_SNAKE_CASE_ = get_cluster_input()
return config
def a__ ( __UpperCamelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser("config" , description=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Accelerate config command" , description=__UpperCamelCase )
parser.add_argument(
"--config_file" , default=__UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_user_input()
if args.config_file is not None:
SCREAMING_SNAKE_CASE_ = args.config_file
else:
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__UpperCamelCase )
else:
config.to_yaml_file(__UpperCamelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = config_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
config_command(__UpperCamelCase )
if __name__ == "__main__":
main()
| 371
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Dict = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = [
"SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Swinv2ForImageClassification",
"Swinv2ForMaskedImageModeling",
"Swinv2Model",
"Swinv2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
A : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 350
|
from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to be of square shaped array but got a "
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
SCREAMING_SNAKE_CASE_ = (table[i][j] - total) / upper[j][j]
SCREAMING_SNAKE_CASE_ = 1
for j in range(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
A : Dict = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , *__magic_name__ : List[Any] , **__magic_name__ : Any ) -> Optional[int]:
super().__init__(*__magic_name__ , **__magic_name__ )
self.check_model_type(__magic_name__ )
def __A ( self : Optional[int] , __magic_name__ : str=None , __magic_name__ : Optional[Any]=None , __magic_name__ : Optional[Any]=None , **__magic_name__ : List[Any] ) -> str:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = {}, {}
if padding is not None:
SCREAMING_SNAKE_CASE_ = padding
if truncation is not None:
SCREAMING_SNAKE_CASE_ = truncation
if top_k is not None:
SCREAMING_SNAKE_CASE_ = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : int , __magic_name__ : Union["Image.Image", str] , __magic_name__ : str = None , **__magic_name__ : List[Any] ) -> List[str]:
if isinstance(__magic_name__ , (Image.Image, str) ) and isinstance(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = {"image": image, "question": question}
else:
SCREAMING_SNAKE_CASE_ = image
SCREAMING_SNAKE_CASE_ = super().__call__(__magic_name__ , **__magic_name__ )
return results
def __A ( self : Any , __magic_name__ : Optional[Any] , __magic_name__ : Any=False , __magic_name__ : Union[str, Any]=False ) -> Any:
SCREAMING_SNAKE_CASE_ = load_image(inputs["image"] )
SCREAMING_SNAKE_CASE_ = self.tokenizer(
inputs["question"] , return_tensors=self.framework , padding=__magic_name__ , truncation=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.image_processor(images=__magic_name__ , return_tensors=self.framework )
model_inputs.update(__magic_name__ )
return model_inputs
def __A ( self : Optional[int] , __magic_name__ : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = self.model(**__magic_name__ )
return model_outputs
def __A ( self : List[str] , __magic_name__ : List[str] , __magic_name__ : Tuple=5 ) -> List[str]:
if top_k > self.model.config.num_labels:
SCREAMING_SNAKE_CASE_ = self.model.config.num_labels
if self.framework == "pt":
SCREAMING_SNAKE_CASE_ = model_outputs.logits.sigmoid()[0]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = probs.topk(__magic_name__ )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
SCREAMING_SNAKE_CASE_ = scores.tolist()
SCREAMING_SNAKE_CASE_ = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__magic_name__ , __magic_name__ )]
| 351
|
from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values" )
return 4 * pi * radius**2
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values" )
return 3 * pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values" )
SCREAMING_SNAKE_CASE_ = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values" )
return 2 * pi * radius * (height + radius)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values" )
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori" )
return 4 * pow(__UpperCamelCase , 2 ) * torus_radius * tube_radius
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values" )
return length * width
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values" )
return side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values" )
return (base * height) / 2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle" )
SCREAMING_SNAKE_CASE_ = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE_ = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values" )
return base * height
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values" )
return 1 / 2 * (basea + basea) * height
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values" )
return pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values" )
return pi * radius_x * radius_y
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values" )
return 1 / 2 * diagonal_a * diagonal_a
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides" )
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 305
| 0
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = StableDiffusionDiffEditPipeline
lowerCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
lowerCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
lowerCamelCase__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCamelCase__ = frozenset([] )
def __A ( self : Dict ) -> str:
torch.manual_seed(0 )
SCREAMING_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 , attention_head_dim=(2, 4) , use_linear_projection=__magic_name__ , )
SCREAMING_SNAKE_CASE_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=__magic_name__ , set_alpha_to_one=__magic_name__ , )
SCREAMING_SNAKE_CASE_ = DDIMInverseScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=__magic_name__ , set_alpha_to_zero=__magic_name__ , )
torch.manual_seed(0 )
SCREAMING_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 , sample_size=128 , )
torch.manual_seed(0 )
SCREAMING_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 , hidden_act="gelu" , projection_dim=512 , )
SCREAMING_SNAKE_CASE_ = CLIPTextModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
SCREAMING_SNAKE_CASE_ = {
"unet": unet,
"scheduler": scheduler,
"inverse_scheduler": inverse_scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def __A ( self : Union[str, Any] , __magic_name__ : Optional[Any] , __magic_name__ : Dict=0 ) -> Dict:
SCREAMING_SNAKE_CASE_ = floats_tensor((1, 16, 16) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
if str(__magic_name__ ).startswith("mps" ):
SCREAMING_SNAKE_CASE_ = torch.manual_seed(__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
SCREAMING_SNAKE_CASE_ = {
"prompt": "a dog and a newt",
"mask_image": mask,
"image_latents": latents,
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __A ( self : Optional[Any] , __magic_name__ : List[str] , __magic_name__ : Optional[Any]=0 ) -> List[str]:
SCREAMING_SNAKE_CASE_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE_ = Image.fromarray(np.uinta(__magic_name__ ) ).convert("RGB" )
if str(__magic_name__ ).startswith("mps" ):
SCREAMING_SNAKE_CASE_ = torch.manual_seed(__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
SCREAMING_SNAKE_CASE_ = {
"image": image,
"source_prompt": "a cat and a frog",
"target_prompt": "a dog and a newt",
"generator": generator,
"num_inference_steps": 2,
"num_maps_per_mask": 2,
"mask_encode_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __A ( self : Any , __magic_name__ : Optional[int] , __magic_name__ : Any=0 ) -> List[str]:
SCREAMING_SNAKE_CASE_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE_ = Image.fromarray(np.uinta(__magic_name__ ) ).convert("RGB" )
if str(__magic_name__ ).startswith("mps" ):
SCREAMING_SNAKE_CASE_ = torch.manual_seed(__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
SCREAMING_SNAKE_CASE_ = {
"image": image,
"prompt": "a cat and a frog",
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"decode_latents": True,
"output_type": "numpy",
}
return inputs
def __A ( self : int ) -> List[Any]:
if not hasattr(self.pipeline_class , "_optional_components" ):
return
SCREAMING_SNAKE_CASE_ = self.get_dummy_components()
SCREAMING_SNAKE_CASE_ = self.pipeline_class(**__magic_name__ )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(__magic_name__ , __magic_name__ , __magic_name__ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
SCREAMING_SNAKE_CASE_ = self.get_dummy_inputs(__magic_name__ )
SCREAMING_SNAKE_CASE_ = pipe(**__magic_name__ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.pipeline_class.from_pretrained(__magic_name__ )
pipe_loaded.to(__magic_name__ )
pipe_loaded.set_progress_bar_config(disable=__magic_name__ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__magic_name__ , __magic_name__ ) is None , F'''`{optional_component}` did not stay set to None after loading.''' , )
SCREAMING_SNAKE_CASE_ = self.get_dummy_inputs(__magic_name__ )
SCREAMING_SNAKE_CASE_ = pipe_loaded(**__magic_name__ )[0]
SCREAMING_SNAKE_CASE_ = np.abs(output - output_loaded ).max()
self.assertLess(__magic_name__ , 1e-4 )
def __A ( self : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = "cpu"
SCREAMING_SNAKE_CASE_ = self.get_dummy_components()
SCREAMING_SNAKE_CASE_ = self.pipeline_class(**__magic_name__ )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.get_dummy_mask_inputs(__magic_name__ )
SCREAMING_SNAKE_CASE_ = pipe.generate_mask(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
SCREAMING_SNAKE_CASE_ = np.array([0] * 9 )
SCREAMING_SNAKE_CASE_ = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__magic_name__ , 1e-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def __A ( self : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = "cpu"
SCREAMING_SNAKE_CASE_ = self.get_dummy_components()
SCREAMING_SNAKE_CASE_ = self.pipeline_class(**__magic_name__ )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.get_dummy_inversion_inputs(__magic_name__ )
SCREAMING_SNAKE_CASE_ = pipe.invert(**__magic_name__ ).images
SCREAMING_SNAKE_CASE_ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
SCREAMING_SNAKE_CASE_ = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
SCREAMING_SNAKE_CASE_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__magic_name__ , 1e-3 )
def __A ( self : Union[str, Any] ) -> Tuple:
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def __A ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ = "cpu"
SCREAMING_SNAKE_CASE_ = self.get_dummy_components()
SCREAMING_SNAKE_CASE_ = {"beta_start": 0.0_0085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
SCREAMING_SNAKE_CASE_ = DPMSolverMultistepScheduler(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = DPMSolverMultistepInverseScheduler(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.pipeline_class(**__magic_name__ )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.get_dummy_inversion_inputs(__magic_name__ )
SCREAMING_SNAKE_CASE_ = pipe.invert(**__magic_name__ ).images
SCREAMING_SNAKE_CASE_ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
SCREAMING_SNAKE_CASE_ = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
SCREAMING_SNAKE_CASE_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__magic_name__ , 1e-3 )
@require_torch_gpu
@slow
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def __A ( cls : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
SCREAMING_SNAKE_CASE_ = raw_image.convert("RGB" ).resize((768, 768) )
SCREAMING_SNAKE_CASE_ = raw_image
def __A ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE_ = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=__magic_name__ , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE_ = DDIMScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE_ = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "a bowl of fruit"
SCREAMING_SNAKE_CASE_ = "a bowl of pears"
SCREAMING_SNAKE_CASE_ = pipe.generate_mask(
image=self.raw_image , source_prompt=__magic_name__ , target_prompt=__magic_name__ , generator=__magic_name__ , )
SCREAMING_SNAKE_CASE_ = pipe.invert(
prompt=__magic_name__ , image=self.raw_image , inpaint_strength=0.7 , generator=__magic_name__ ).latents
SCREAMING_SNAKE_CASE_ = pipe(
prompt=__magic_name__ , mask_image=__magic_name__ , image_latents=__magic_name__ , generator=__magic_name__ , negative_prompt=__magic_name__ , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
SCREAMING_SNAKE_CASE_ = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
def __A ( self : Optional[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=__magic_name__ , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE_ = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "a bowl of fruit"
SCREAMING_SNAKE_CASE_ = "a bowl of pears"
SCREAMING_SNAKE_CASE_ = pipe.generate_mask(
image=self.raw_image , source_prompt=__magic_name__ , target_prompt=__magic_name__ , generator=__magic_name__ , )
SCREAMING_SNAKE_CASE_ = pipe.invert(
prompt=__magic_name__ , image=self.raw_image , inpaint_strength=0.7 , generator=__magic_name__ , num_inference_steps=25 , ).latents
SCREAMING_SNAKE_CASE_ = pipe(
prompt=__magic_name__ , mask_image=__magic_name__ , image_latents=__magic_name__ , generator=__magic_name__ , negative_prompt=__magic_name__ , inpaint_strength=0.7 , num_inference_steps=25 , output_type="numpy" , ).images[0]
SCREAMING_SNAKE_CASE_ = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 352
|
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
A : List[str] = logging.get_logger(__name__)
A : int = {
"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 lowerCamelCase (SCREAMING_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 : Dict , __magic_name__ : Dict=50_265 , __magic_name__ : str=512 , __magic_name__ : List[Any]=8 , __magic_name__ : Any=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Any=8 , __magic_name__ : Optional[int]=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Tuple=0.0 , __magic_name__ : Dict=0.0 , __magic_name__ : Optional[int]=True , __magic_name__ : Any=True , __magic_name__ : Dict="gelu" , __magic_name__ : Tuple=512 , __magic_name__ : List[str]=0.1 , __magic_name__ : List[Any]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : Union[str, Any]=1 , __magic_name__ : List[Any]=False , __magic_name__ : str=0 , __magic_name__ : Dict=1 , __magic_name__ : str=2 , __magic_name__ : Union[str, Any]=2 , **__magic_name__ : Optional[Any] , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , is_encoder_decoder=__magic_name__ , decoder_start_token_id=__magic_name__ , forced_eos_token_id=__magic_name__ , **__magic_name__ , )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def __A ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ = {0: "batch"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = 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 __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super().outputs
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __A ( self : int , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# Generate decoder inputs
SCREAMING_SNAKE_CASE_ = seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE_ = dict(**__magic_name__ , **__magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
SCREAMING_SNAKE_CASE_ = common_inputs["decoder_input_ids"].shape[1]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = decoder_seq_length + 3
SCREAMING_SNAKE_CASE_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(__magic_name__ , __magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ = min(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = max(__magic_name__ , __magic_name__ ) - min_num_layers
SCREAMING_SNAKE_CASE_ = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(__magic_name__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE_ = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(__magic_name__ , __magic_name__ ):
common_inputs["past_key_values"].append((torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) )
return common_inputs
def __A ( self : Union[str, Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE_ = seqlen + 2
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = common_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["attention_mask"], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(__magic_name__ )
]
return common_inputs
def __A ( self : Dict , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , 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
SCREAMING_SNAKE_CASE_ = tokenizer.num_special_tokens_to_add(__magic_name__ )
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__magic_name__ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE_ = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE_ = dict(tokenizer(__magic_name__ , return_tensors=__magic_name__ ) )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
elif self.task == "causal-lm":
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_causal_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : List[Any] , __magic_name__ : str , __magic_name__ : List[str] ) -> List[str]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super()._flatten_past_key_values_(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self )._flatten_past_key_values_(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
| 305
| 0
|
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
A : Dict = pytest.mark.integration
@pytest.mark.parametrize("path" , ["paws", "csv"] )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
inspect_dataset(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = path + ".py"
assert script_name in os.listdir(__UpperCamelCase )
assert "__pycache__" not in os.listdir(__UpperCamelCase )
@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.parametrize("path" , ["accuracy"] )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
inspect_metric(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = path + ".py"
assert script_name in os.listdir(__UpperCamelCase )
assert "__pycache__" not in os.listdir(__UpperCamelCase )
@pytest.mark.parametrize(
"path, config_name, expected_splits" , [
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
] , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_dataset_config_info(__UpperCamelCase , config_name=__UpperCamelCase )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception" , [
("paws", None, ValueError),
] , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
with pytest.raises(__UpperCamelCase ):
get_dataset_config_info(__UpperCamelCase , config_name=__UpperCamelCase )
@pytest.mark.parametrize(
"path, expected" , [
("squad", "plain_text"),
("acronym_identification", "default"),
("lhoestq/squad", "plain_text"),
("lhoestq/test", "default"),
("lhoestq/demo1", "lhoestq--demo1"),
("dalle-mini/wit", "dalle-mini--wit"),
] , )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_dataset_config_names(__UpperCamelCase )
assert expected in config_names
@pytest.mark.parametrize(
"path, expected_configs, expected_splits_in_first_config" , [
("squad", ["plain_text"], ["train", "validation"]),
("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
] , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_dataset_infos(__UpperCamelCase )
assert list(infos.keys() ) == expected_configs
SCREAMING_SNAKE_CASE_ = expected_configs[0]
assert expected_config in infos
SCREAMING_SNAKE_CASE_ = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"path, expected_config, expected_splits" , [
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
] , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_dataset_infos(__UpperCamelCase )
assert expected_config in infos
SCREAMING_SNAKE_CASE_ = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception" , [
("paws", None, ValueError),
] , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
with pytest.raises(__UpperCamelCase ):
get_dataset_split_names(__UpperCamelCase , config_name=__UpperCamelCase )
| 353
|
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , __magic_name__ : List[str] , __magic_name__ : int=100 , __magic_name__ : Optional[Any]=13 , __magic_name__ : Dict=30 , __magic_name__ : Tuple=2 , __magic_name__ : str=3 , __magic_name__ : str=True , __magic_name__ : Optional[int]=True , __magic_name__ : Union[str, Any]=32 , __magic_name__ : Optional[int]=4 , __magic_name__ : Dict=4 , __magic_name__ : Tuple=37 , __magic_name__ : Any="gelu" , __magic_name__ : int=0.1 , __magic_name__ : List[str]=0.1 , __magic_name__ : Optional[int]=10 , __magic_name__ : Tuple=0.02 , __magic_name__ : Optional[int]=3 , __magic_name__ : List[str]=None , __magic_name__ : Tuple=[0, 1, 2, 3] , ) -> List[str]:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = 100
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
SCREAMING_SNAKE_CASE_ = scope
SCREAMING_SNAKE_CASE_ = out_indices
SCREAMING_SNAKE_CASE_ = num_labels
# in BeiT, 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 __A ( self : Any ) -> int:
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __A ( self : Dict ) -> Optional[int]:
return BeitConfig(
vocab_size=self.vocab_size , 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=__magic_name__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def __A ( self : Optional[int] , __magic_name__ : List[str] , __magic_name__ : Optional[int] , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = BeitModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : str ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __A ( self : Dict , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : Tuple , __magic_name__ : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = self.type_sequence_label_size
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self : Tuple , __magic_name__ : Any , __magic_name__ : Optional[Any] , __magic_name__ : Tuple , __magic_name__ : int ) -> int:
SCREAMING_SNAKE_CASE_ = self.num_labels
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def __A ( self : str ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = config_and_inputs
SCREAMING_SNAKE_CASE_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = BeitModelTester(self )
SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def __A ( self : Dict ) -> List[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="BEiT does not use inputs_embeds" )
def __A ( self : List[str] ) -> Optional[Any]:
pass
@require_torch_multi_gpu
@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def __A ( self : str ) -> List[str]:
pass
def __A ( self : List[Any] ) -> List[str]:
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(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def __A ( self : Union[str, Any] ) -> int:
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(__magic_name__ )
SCREAMING_SNAKE_CASE_ = inspect.signature(model.forward )
# 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] , __magic_name__ )
def __A ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__magic_name__ )
def __A ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def __A ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__magic_name__ )
def __A ( self : int ) -> Optional[int]:
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : Any ) -> Tuple:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.gradient_checkpointing_enable()
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = _config_zero_init(__magic_name__ )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ = model_class(config=__magic_name__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def __A ( self : int ) -> Optional[int]:
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ = BeitModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self : List[Any] ) -> str:
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def __A ( self : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).pixel_values.to(__magic_name__ )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE_ = torch.ones((1, 196) , dtype=torch.bool ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(pixel_values=__magic_name__ , bool_masked_pos=__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , __magic_name__ , atol=1e-2 ) )
@slow
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 1_000) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([-1.2385, -1.0987, -1.0108] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 281
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to(
__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 21_841) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([1.6881, -0.2787, 0.5901] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 2_396
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Tuple ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = version.parse(PIL.__version__ ) < version.parse("9.0.0" )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=__magic_name__ , )
else:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=__magic_name__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __magic_name__ , atol=1e-4 ) )
@slow
def __A ( self : List[str] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ , target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE_ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
| 305
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : str = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",
"XLMRobertaXLOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = [
"XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMRobertaXLForCausalLM",
"XLMRobertaXLForMaskedLM",
"XLMRobertaXLForMultipleChoice",
"XLMRobertaXLForQuestionAnswering",
"XLMRobertaXLForSequenceClassification",
"XLMRobertaXLForTokenClassification",
"XLMRobertaXLModel",
"XLMRobertaXLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
A : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 354
|
from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = self._trie
for char in text:
if char not in trie:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = trie[char]
SCREAMING_SNAKE_CASE_ = True
def __A ( self : Union[str, Any] , __magic_name__ : str ) -> tuple | list:
SCREAMING_SNAKE_CASE_ = self._trie
for char in prefix:
if char in trie:
SCREAMING_SNAKE_CASE_ = trie[char]
else:
return []
return self._elements(__magic_name__ )
def __A ( self : int , __magic_name__ : dict ) -> tuple:
SCREAMING_SNAKE_CASE_ = []
for c, v in d.items():
SCREAMING_SNAKE_CASE_ = [" "] if c == END else [(c + s) for s in self._elements(__magic_name__ )]
result.extend(__magic_name__ )
return tuple(__magic_name__ )
A : Union[str, Any] = Trie()
A : Optional[int] = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
trie.insert_word(word)
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = trie.find_word(__UpperCamelCase )
return tuple(string + word for word in suffixes )
def a__ ( ):
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 305
| 0
|
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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = SwinvaConfig()
SCREAMING_SNAKE_CASE_ = swinva_name.split("_" )
SCREAMING_SNAKE_CASE_ = name_split[1]
if "to" in name_split[3]:
SCREAMING_SNAKE_CASE_ = int(name_split[3][-3:] )
else:
SCREAMING_SNAKE_CASE_ = int(name_split[3] )
if "to" in name_split[2]:
SCREAMING_SNAKE_CASE_ = int(name_split[2][-2:] )
else:
SCREAMING_SNAKE_CASE_ = int(name_split[2][6:] )
if model_size == "tiny":
SCREAMING_SNAKE_CASE_ = 9_6
SCREAMING_SNAKE_CASE_ = (2, 2, 6, 2)
SCREAMING_SNAKE_CASE_ = (3, 6, 1_2, 2_4)
elif model_size == "small":
SCREAMING_SNAKE_CASE_ = 9_6
SCREAMING_SNAKE_CASE_ = (2, 2, 1_8, 2)
SCREAMING_SNAKE_CASE_ = (3, 6, 1_2, 2_4)
elif model_size == "base":
SCREAMING_SNAKE_CASE_ = 1_2_8
SCREAMING_SNAKE_CASE_ = (2, 2, 1_8, 2)
SCREAMING_SNAKE_CASE_ = (4, 8, 1_6, 3_2)
else:
SCREAMING_SNAKE_CASE_ = 1_9_2
SCREAMING_SNAKE_CASE_ = (2, 2, 1_8, 2)
SCREAMING_SNAKE_CASE_ = (6, 1_2, 2_4, 4_8)
if "to" in swinva_name:
SCREAMING_SNAKE_CASE_ = (1_2, 1_2, 1_2, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
SCREAMING_SNAKE_CASE_ = 2_1_8_4_1
SCREAMING_SNAKE_CASE_ = "huggingface/label-files"
SCREAMING_SNAKE_CASE_ = "imagenet-22k-id2label.json"
SCREAMING_SNAKE_CASE_ = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = idalabel
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
else:
SCREAMING_SNAKE_CASE_ = 1_0_0_0
SCREAMING_SNAKE_CASE_ = "huggingface/label-files"
SCREAMING_SNAKE_CASE_ = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE_ = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = idalabel
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = img_size
SCREAMING_SNAKE_CASE_ = num_classes
SCREAMING_SNAKE_CASE_ = embed_dim
SCREAMING_SNAKE_CASE_ = depths
SCREAMING_SNAKE_CASE_ = num_heads
SCREAMING_SNAKE_CASE_ = window_size
return config
def a__ ( __UpperCamelCase ):
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE_ = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE_ = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
SCREAMING_SNAKE_CASE_ = "encoder." + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE_ = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
SCREAMING_SNAKE_CASE_ = name.replace("attn" , "attention.self" )
if "norm1" in name:
SCREAMING_SNAKE_CASE_ = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
SCREAMING_SNAKE_CASE_ = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE_ = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE_ = name.replace("mlp.fc2" , "output.dense" )
if "q_bias" in name:
SCREAMING_SNAKE_CASE_ = name.replace("q_bias" , "query.bias" )
if "k_bias" in name:
SCREAMING_SNAKE_CASE_ = name.replace("k_bias" , "key.bias" )
if "v_bias" in name:
SCREAMING_SNAKE_CASE_ = name.replace("v_bias" , "value.bias" )
if "cpb_mlp" in name:
SCREAMING_SNAKE_CASE_ = name.replace("cpb_mlp" , "continuous_position_bias_mlp" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE_ = "layernorm.weight"
if name == "norm.bias":
SCREAMING_SNAKE_CASE_ = "layernorm.bias"
if "head" in name:
SCREAMING_SNAKE_CASE_ = name.replace("head" , "classifier" )
else:
SCREAMING_SNAKE_CASE_ = "swinv2." + name
return name
def a__ ( __UpperCamelCase , __UpperCamelCase ):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE_ = orig_state_dict.pop(__UpperCamelCase )
if "mask" in key:
continue
elif "qkv" in key:
SCREAMING_SNAKE_CASE_ = key.split("." )
SCREAMING_SNAKE_CASE_ = int(key_split[1] )
SCREAMING_SNAKE_CASE_ = int(key_split[3] )
SCREAMING_SNAKE_CASE_ = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE_ = val[:dim, :]
SCREAMING_SNAKE_CASE_ = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE_ = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE_ = val[:dim]
SCREAMING_SNAKE_CASE_ = val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE_ = val[-dim:]
else:
SCREAMING_SNAKE_CASE_ = val
return orig_state_dict
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = timm.create_model(__UpperCamelCase , pretrained=__UpperCamelCase )
timm_model.eval()
SCREAMING_SNAKE_CASE_ = get_swinva_config(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = SwinvaForImageClassification(__UpperCamelCase )
model.eval()
SCREAMING_SNAKE_CASE_ = convert_state_dict(timm_model.state_dict() , __UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) )
SCREAMING_SNAKE_CASE_ = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
SCREAMING_SNAKE_CASE_ = image_processor(images=__UpperCamelCase , return_tensors="pt" )
SCREAMING_SNAKE_CASE_ = timm_model(inputs["pixel_values"] )
SCREAMING_SNAKE_CASE_ = model(**__UpperCamelCase ).logits
assert torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-3 )
print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__UpperCamelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__UpperCamelCase )
model.push_to_hub(
repo_path_or_name=Path(__UpperCamelCase , __UpperCamelCase ) , organization="nandwalritik" , commit_message="Add model" , )
if __name__ == "__main__":
A : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swinv2_name",
default="swinv2_tiny_patch4_window8_256",
type=str,
help="Name of the Swinv2 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."
)
A : Any = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 355
|
from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name # process name
SCREAMING_SNAKE_CASE_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
SCREAMING_SNAKE_CASE_ = arrival_time
SCREAMING_SNAKE_CASE_ = burst_time # remaining burst time
SCREAMING_SNAKE_CASE_ = 0 # total time of the process wait in ready queue
SCREAMING_SNAKE_CASE_ = 0 # time from arrival time to completion time
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Tuple , __magic_name__ : int , __magic_name__ : list[int] , __magic_name__ : deque[Process] , __magic_name__ : int , ) -> None:
# total number of mlfq's queues
SCREAMING_SNAKE_CASE_ = number_of_queues
# time slice of queues that round robin algorithm applied
SCREAMING_SNAKE_CASE_ = time_slices
# unfinished process is in this ready_queue
SCREAMING_SNAKE_CASE_ = queue
# current time
SCREAMING_SNAKE_CASE_ = current_time
# finished process is in this sequence queue
SCREAMING_SNAKE_CASE_ = deque()
def __A ( self : Dict ) -> list[str]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def __A ( self : Tuple , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def __A ( self : str , __magic_name__ : deque[Process] ) -> list[int]:
return [q.burst_time for q in queue]
def __A ( self : Optional[Any] , __magic_name__ : Process ) -> int:
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def __A ( self : Optional[Any] , __magic_name__ : deque[Process] ) -> deque[Process]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of finished process
while len(__magic_name__ ) != 0:
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(__magic_name__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
SCREAMING_SNAKE_CASE_ = 0
# set the process's turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# set the completion time
SCREAMING_SNAKE_CASE_ = self.current_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def __A ( self : Any , __magic_name__ : deque[Process] , __magic_name__ : int ) -> tuple[deque[Process], deque[Process]]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(__magic_name__ ) ):
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(__magic_name__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
SCREAMING_SNAKE_CASE_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(__magic_name__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
SCREAMING_SNAKE_CASE_ = 0
# set the finish time
SCREAMING_SNAKE_CASE_ = self.current_time
# update the process' turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def __A ( self : Any ) -> deque[Process]:
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
A : Dict = Process("P1", 0, 53)
A : str = Process("P2", 0, 17)
A : List[Any] = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Dict = 3
A : Any = [17, 25]
A : Dict = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
A : Union[str, Any] = Process("P1", 0, 53)
A : Any = Process("P2", 0, 17)
A : Dict = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Optional[int] = 3
A : int = [17, 25]
A : Union[str, Any] = deque([Pa, Pa, Pa, Pa])
A : Tuple = MLFQ(number_of_queues, time_slices, queue, 0)
A : Tuple = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| 305
| 0
|
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''vision-encoder-decoder'''
lowerCamelCase__ = True
def __init__( self : int , **__magic_name__ : List[str] ) -> List[str]:
super().__init__(**__magic_name__ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F'''A configuraton of type {self.model_type} cannot be instantiated because '''
F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}''' )
SCREAMING_SNAKE_CASE_ = kwargs.pop("encoder" )
SCREAMING_SNAKE_CASE_ = encoder_config.pop("model_type" )
SCREAMING_SNAKE_CASE_ = kwargs.pop("decoder" )
SCREAMING_SNAKE_CASE_ = decoder_config.pop("model_type" )
SCREAMING_SNAKE_CASE_ = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = AutoConfig.for_model(__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = True
@classmethod
def __A ( cls : List[Any] , __magic_name__ : PretrainedConfig , __magic_name__ : PretrainedConfig , **__magic_name__ : Optional[Any] ) -> PretrainedConfig:
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__magic_name__ )
def __A ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.encoder.to_dict()
SCREAMING_SNAKE_CASE_ = self.decoder.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __A ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def __A ( self : Union[str, Any] ) -> float:
return 1e-4
@property
def __A ( self : str ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}} )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def __A ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
SCREAMING_SNAKE_CASE_ = OrderedDict()
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "past_decoder_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "past_decoder_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def __A ( self : Dict , __magic_name__ : "PreTrainedTokenizerBase" , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional["TensorType"] = None , ) -> Mapping[str, Any]:
import torch
SCREAMING_SNAKE_CASE_ = OrderedDict()
SCREAMING_SNAKE_CASE_ = super().generate_dummy_inputs(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = dummy_input["input_ids"].shape
SCREAMING_SNAKE_CASE_ = (batch, encoder_sequence, self._config.encoder_hidden_size)
SCREAMING_SNAKE_CASE_ = dummy_input.pop("input_ids" )
SCREAMING_SNAKE_CASE_ = dummy_input.pop("attention_mask" )
SCREAMING_SNAKE_CASE_ = torch.zeros(__magic_name__ )
return common_inputs
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def __A ( self : Union[str, Any] ) -> None:
pass
def __A ( self : Optional[int] , __magic_name__ : PretrainedConfig ) -> OnnxConfig:
return VisionEncoderDecoderEncoderOnnxConfig(__magic_name__ )
def __A ( self : Union[str, Any] , __magic_name__ : PretrainedConfig , __magic_name__ : PretrainedConfig , __magic_name__ : str = "default" ) -> OnnxConfig:
SCREAMING_SNAKE_CASE_ = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__magic_name__ , __magic_name__ )
| 356
|
import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 305
| 0
|
from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None , __magic_name__ : List[str]=2_048 ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = config.__dict__
SCREAMING_SNAKE_CASE_ = modal_hidden_size
if num_labels:
SCREAMING_SNAKE_CASE_ = num_labels
| 357
|
from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "08x" )[-8:]
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
return little_endian_hex
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = b""
for char in message:
bit_string += format(__UpperCamelCase , "08b" ).encode("utf-8" )
SCREAMING_SNAKE_CASE_ = format(len(__UpperCamelCase ) , "064b" ).encode("utf-8" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__UpperCamelCase ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) % 5_1_2 != 0:
raise ValueError("Input must have length that's a multiple of 512" )
for pos in range(0 , len(__UpperCamelCase ) , 5_1_2 ):
SCREAMING_SNAKE_CASE_ = bit_string[pos : pos + 5_1_2]
SCREAMING_SNAKE_CASE_ = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "032b" )
SCREAMING_SNAKE_CASE_ = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__UpperCamelCase , 2 )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return (a + b) % 2**3_2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
if shift < 0:
raise ValueError("Shift must be non-negative" )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = preprocess(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
SCREAMING_SNAKE_CASE_ = 0X67452301
SCREAMING_SNAKE_CASE_ = 0Xefcdab89
SCREAMING_SNAKE_CASE_ = 0X98badcfe
SCREAMING_SNAKE_CASE_ = 0X10325476
SCREAMING_SNAKE_CASE_ = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = aa
SCREAMING_SNAKE_CASE_ = ba
SCREAMING_SNAKE_CASE_ = ca
SCREAMING_SNAKE_CASE_ = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = d ^ (b & (c ^ d))
SCREAMING_SNAKE_CASE_ = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = c ^ (d & (b ^ c))
SCREAMING_SNAKE_CASE_ = (5 * i + 1) % 1_6
elif i <= 4_7:
SCREAMING_SNAKE_CASE_ = b ^ c ^ d
SCREAMING_SNAKE_CASE_ = (3 * i + 5) % 1_6
else:
SCREAMING_SNAKE_CASE_ = c ^ (b | not_aa(__UpperCamelCase ))
SCREAMING_SNAKE_CASE_ = (7 * i) % 1_6
SCREAMING_SNAKE_CASE_ = (f + a + added_consts[i] + block_words[g]) % 2**3_2
SCREAMING_SNAKE_CASE_ = d
SCREAMING_SNAKE_CASE_ = c
SCREAMING_SNAKE_CASE_ = b
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , left_rotate_aa(__UpperCamelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
A : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
order.append(__UpperCamelCase )
return order
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return component
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) * [False]
SCREAMING_SNAKE_CASE_ = {vert: [] for vert in range(len(__UpperCamelCase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = []
for i, was_visited in enumerate(__UpperCamelCase ):
if not was_visited:
order += topology_sort(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) * [False]
for i in range(len(__UpperCamelCase ) ):
SCREAMING_SNAKE_CASE_ = order[len(__UpperCamelCase ) - i - 1]
if not visited[vert]:
SCREAMING_SNAKE_CASE_ = find_components(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
components_list.append(__UpperCamelCase )
return components_list
| 358
|
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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ) -> Any:
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = BlipImageProcessor()
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )
SCREAMING_SNAKE_CASE_ = BlipaProcessor(__magic_name__ , __magic_name__ )
processor.save_pretrained(self.tmpdirname )
def __A ( self : str , **__magic_name__ : int ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).tokenizer
def __A ( self : Dict , **__magic_name__ : List[Any] ) -> int:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).image_processor
def __A ( self : int ) -> Any:
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ = [Image.fromarray(np.moveaxis(__magic_name__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE_ = self.get_image_processor(do_normalize=__magic_name__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE_ = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__magic_name__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __magic_name__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __magic_name__ )
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = image_processor(__magic_name__ , return_tensors="np" )
SCREAMING_SNAKE_CASE_ = processor(images=__magic_name__ , 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 : str ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_token_type_ids=__magic_name__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__magic_name__ ):
processor()
def __A ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ = processor.batch_decode(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 305
| 0
|
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 a__ ( __UpperCamelCase , __UpperCamelCase ):
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
SCREAMING_SNAKE_CASE_ = flax_key_tuple[:-1] + ("weight",)
SCREAMING_SNAKE_CASE_ = torch.permute(__UpperCamelCase , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase ):
# linear layer
SCREAMING_SNAKE_CASE_ = flax_key_tuple[:-1] + ("weight",)
SCREAMING_SNAKE_CASE_ = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
SCREAMING_SNAKE_CASE_ = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if "metadata" in layer:
SCREAMING_SNAKE_CASE_ = layer.split("metadata" )
SCREAMING_SNAKE_CASE_ = "".join(split_layer[0] )[:-1]
SCREAMING_SNAKE_CASE_ = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
SCREAMING_SNAKE_CASE_ = layer.split("kvstore" )
SCREAMING_SNAKE_CASE_ = "".join(split_layer[0] )[:-1]
SCREAMING_SNAKE_CASE_ = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
SCREAMING_SNAKE_CASE_ = layer.split("/" )
SCREAMING_SNAKE_CASE_ = "/".join(split_layer[:-1] )
SCREAMING_SNAKE_CASE_ = (split_layer[-1],)
if "kvstore/path" in layer:
SCREAMING_SNAKE_CASE_ = F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
elif "kvstore/driver" in layer:
SCREAMING_SNAKE_CASE_ = "file"
else:
SCREAMING_SNAKE_CASE_ = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = rename_keys(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = {}
for k, v in current_block.items():
SCREAMING_SNAKE_CASE_ = v
SCREAMING_SNAKE_CASE_ = new_current_block
torch.save(__UpperCamelCase , __UpperCamelCase )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = WEIGHTS_NAME ):
SCREAMING_SNAKE_CASE_ = convert_file_size_to_int(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
SCREAMING_SNAKE_CASE_ = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
SCREAMING_SNAKE_CASE_ = flatten_dict(__UpperCamelCase , sep="/" )
SCREAMING_SNAKE_CASE_ = {}
for layer in checkpoint_info.keys():
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = get_key_and_tensorstore_dict(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if curr_real_layer_name in all_layers:
SCREAMING_SNAKE_CASE_ = content
else:
SCREAMING_SNAKE_CASE_ = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
SCREAMING_SNAKE_CASE_ = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
SCREAMING_SNAKE_CASE_ = torch.tensor(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = rename_base_flax_keys(tuple(key.split("/" ) ) , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = "/".join(__UpperCamelCase )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
SCREAMING_SNAKE_CASE_ = os.path.join(
__UpperCamelCase , weights_name.replace(".bin" , F'''-{len(__UpperCamelCase )+1:05d}-of-???.bin''' ) )
rename_and_save_block(__UpperCamelCase , __UpperCamelCase )
sharded_state_dicts.append(current_block.keys() )
del current_block
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = raw_weights.to(getattr(__UpperCamelCase , __UpperCamelCase ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , weights_name.replace(".bin" , F'''-{len(__UpperCamelCase )+1:05d}-of-???.bin''' ) )
rename_and_save_block(__UpperCamelCase , __UpperCamelCase )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(__UpperCamelCase ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = {}
for idx, shard in enumerate(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = weights_name.replace(
".bin" , F'''-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin''' ) # len(sharded_state_dicts):05d}
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , weights_name.replace(".bin" , F'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(__UpperCamelCase , os.path.join(__UpperCamelCase , __UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = shard
for key in shard:
SCREAMING_SNAKE_CASE_ = shard_file
# Add the metadata
SCREAMING_SNAKE_CASE_ = {"total_size": total_size}
SCREAMING_SNAKE_CASE_ = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(__UpperCamelCase , __UpperCamelCase ) , "w" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ = json.dumps(__UpperCamelCase , indent=2 , sort_keys=__UpperCamelCase ) + "\n"
f.write(__UpperCamelCase )
return metadata, index
if __name__ == "__main__":
A : Optional[Any] = 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.",
)
A : Optional[Any] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def a__ ( ):
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
SCREAMING_SNAKE_CASE_ = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
SCREAMING_SNAKE_CASE_ = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
SCREAMING_SNAKE_CASE_ = TaTokenizer.from_pretrained("t5-small" )
SCREAMING_SNAKE_CASE_ = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
SCREAMING_SNAKE_CASE_ = tokenizer(__UpperCamelCase , return_tensors="pt" ).input_ids
SCREAMING_SNAKE_CASE_ = model.generate(__UpperCamelCase , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 359
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ["DPTFeatureExtractor"]
A : str = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[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
A : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
from typing import Dict, Optional
import numpy as np
import datasets
A : Any = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"
A : List[Any] = "\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), 'per_category_accuracy': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n"
A : List[str] = "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False , ):
if label_map is not None:
for old_id, new_id in label_map.items():
SCREAMING_SNAKE_CASE_ = new_id
# turn into Numpy arrays
SCREAMING_SNAKE_CASE_ = np.array(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.array(__UpperCamelCase )
if reduce_labels:
SCREAMING_SNAKE_CASE_ = 2_5_5
SCREAMING_SNAKE_CASE_ = label - 1
SCREAMING_SNAKE_CASE_ = 2_5_5
SCREAMING_SNAKE_CASE_ = label != ignore_index
SCREAMING_SNAKE_CASE_ = np.not_equal(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = pred_label[mask]
SCREAMING_SNAKE_CASE_ = np.array(__UpperCamelCase )[mask]
SCREAMING_SNAKE_CASE_ = pred_label[pred_label == label]
SCREAMING_SNAKE_CASE_ = np.histogram(__UpperCamelCase , bins=__UpperCamelCase , range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE_ = np.histogram(__UpperCamelCase , bins=__UpperCamelCase , range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE_ = np.histogram(__UpperCamelCase , bins=__UpperCamelCase , range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE_ = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False , ):
SCREAMING_SNAKE_CASE_ = np.zeros((num_labels,) , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = np.zeros((num_labels,) , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = np.zeros((num_labels,) , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = intersect_and_union(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = total_intersect_and_union(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# compute metrics
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = total_area_intersect.sum() / total_area_label.sum()
SCREAMING_SNAKE_CASE_ = total_area_intersect / total_area_union
SCREAMING_SNAKE_CASE_ = total_area_intersect / total_area_label
SCREAMING_SNAKE_CASE_ = np.nanmean(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.nanmean(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = all_acc
SCREAMING_SNAKE_CASE_ = iou
SCREAMING_SNAKE_CASE_ = acc
if nan_to_num is not None:
SCREAMING_SNAKE_CASE_ = {metric: np.nan_to_num(__UpperCamelCase , nan=__UpperCamelCase ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase (datasets.Metric ):
"""simple docstring"""
def __A ( self : Any ) -> Tuple:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) , reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] , )
def __A ( self : Any , __magic_name__ : int , __magic_name__ : Any , __magic_name__ : int , __magic_name__ : bool , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[Dict[int, int]] = None , __magic_name__ : bool = False , ) -> int:
SCREAMING_SNAKE_CASE_ = mean_iou(
results=__magic_name__ , gt_seg_maps=__magic_name__ , num_labels=__magic_name__ , ignore_index=__magic_name__ , nan_to_num=__magic_name__ , label_map=__magic_name__ , reduce_labels=__magic_name__ , )
return iou_result
| 360
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
A : int = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Union[str, Any] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 305
| 0
|
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def __A ( self : Optional[int] , __magic_name__ : Optional[int] , __magic_name__ : Tuple , __magic_name__ : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = hf_hub_download(
repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
SCREAMING_SNAKE_CASE_ = VideoClassificationPipeline(model=__magic_name__ , image_processor=__magic_name__ , top_k=2 )
SCREAMING_SNAKE_CASE_ = [
example_video_filepath,
"https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
]
return video_classifier, examples
def __A ( self : Tuple , __magic_name__ : List[str] , __magic_name__ : Dict ) -> Tuple:
"""simple docstring"""
for example in examples:
SCREAMING_SNAKE_CASE_ = video_classifier(__magic_name__ )
self.assertEqual(
__magic_name__ , [
{"score": ANY(__magic_name__ ), "label": ANY(__magic_name__ )},
{"score": ANY(__magic_name__ ), "label": ANY(__magic_name__ )},
] , )
@require_torch
def __A ( self : Optional[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
SCREAMING_SNAKE_CASE_ = VideoMAEFeatureExtractor(
size={"shortest_edge": 10} , crop_size={"height": 10, "width": 10} )
SCREAMING_SNAKE_CASE_ = pipeline(
"video-classification" , model=__magic_name__ , feature_extractor=__magic_name__ , frame_sampling_rate=4 )
SCREAMING_SNAKE_CASE_ = hf_hub_download(repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
SCREAMING_SNAKE_CASE_ = video_classifier(__magic_name__ , top_k=2 )
self.assertEqual(
nested_simplify(__magic_name__ , decimals=4 ) , [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}] , )
SCREAMING_SNAKE_CASE_ = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(__magic_name__ , decimals=4 ) , [
[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
] , )
@require_tf
def __A ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
pass
| 361
|
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_text_model'''
def __init__( self : Optional[Any] , __magic_name__ : Union[str, Any]=30_522 , __magic_name__ : Tuple=768 , __magic_name__ : List[str]=12 , __magic_name__ : Optional[Any]=12 , __magic_name__ : str=3_072 , __magic_name__ : Dict="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : Optional[int]=0.1 , __magic_name__ : List[str]=512 , __magic_name__ : Any=2 , __magic_name__ : Optional[Any]=0.02 , __magic_name__ : int=1e-12 , __magic_name__ : str=0 , __magic_name__ : Optional[Any]="absolute" , __magic_name__ : Optional[Any]=True , **__magic_name__ : Tuple , ) -> Union[str, Any]:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = pad_token_id
@classmethod
def __A ( cls : Any , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_vision_model'''
def __init__( self : List[str] , __magic_name__ : int = 3 , __magic_name__ : int = 600 , __magic_name__ : float = 2.0 , __magic_name__ : float = 3.1 , __magic_name__ : int = 8 , __magic_name__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ : List[int] = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ : List[int] = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ : List[int] = [] , __magic_name__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ : float = 0.25 , __magic_name__ : str = "swish" , __magic_name__ : int = 2_560 , __magic_name__ : str = "mean" , __magic_name__ : float = 0.02 , __magic_name__ : float = 0.001 , __magic_name__ : float = 0.99 , __magic_name__ : float = 0.2 , **__magic_name__ : List[Any] , ) -> Tuple:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = width_coefficient
SCREAMING_SNAKE_CASE_ = depth_coefficient
SCREAMING_SNAKE_CASE_ = depth_divisor
SCREAMING_SNAKE_CASE_ = kernel_sizes
SCREAMING_SNAKE_CASE_ = in_channels
SCREAMING_SNAKE_CASE_ = out_channels
SCREAMING_SNAKE_CASE_ = depthwise_padding
SCREAMING_SNAKE_CASE_ = strides
SCREAMING_SNAKE_CASE_ = num_block_repeats
SCREAMING_SNAKE_CASE_ = expand_ratios
SCREAMING_SNAKE_CASE_ = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dim
SCREAMING_SNAKE_CASE_ = pooling_type
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = batch_norm_eps
SCREAMING_SNAKE_CASE_ = batch_norm_momentum
SCREAMING_SNAKE_CASE_ = drop_connect_rate
SCREAMING_SNAKE_CASE_ = sum(__magic_name__ ) * 4
@classmethod
def __A ( cls : List[str] , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Dict ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align'''
lowerCamelCase__ = True
def __init__( self : Optional[Any] , __magic_name__ : Dict=None , __magic_name__ : List[Any]=None , __magic_name__ : str=640 , __magic_name__ : Any=1.0 , __magic_name__ : Dict=0.02 , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__(**__magic_name__ )
if text_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
SCREAMING_SNAKE_CASE_ = AlignTextConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = AlignVisionConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = projection_dim
SCREAMING_SNAKE_CASE_ = temperature_init_value
SCREAMING_SNAKE_CASE_ = initializer_range
@classmethod
def __A ( cls : List[str] , __magic_name__ : AlignTextConfig , __magic_name__ : AlignVisionConfig , **__magic_name__ : Tuple ) -> Any:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__magic_name__ )
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.text_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 305
| 0
|
"""simple docstring"""
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = set({"(", "[", "{"} )
SCREAMING_SNAKE_CASE_ = set({")", "]", "}"} )
SCREAMING_SNAKE_CASE_ = {"{": "}", "[": "]", "(": ")"}
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 a__ ( ):
SCREAMING_SNAKE_CASE_ = input("Enter sequence of brackets: " )
if is_balanced(__UpperCamelCase ):
print(__UpperCamelCase , "is balanced" )
else:
print(__UpperCamelCase , "is not balanced" )
if __name__ == "__main__":
main()
| 362
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
SCREAMING_SNAKE_CASE_ = "x = y"
SCREAMING_SNAKE_CASE_ = {"y": 5}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 5, "y": 5} )
def __A ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "y = add_two(x)"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result is None
assert "tried to execute add_two" in out.out
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = "x = 3"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3} )
def __A ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "x = 3\ny = 5"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 5} )
def __A ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE_ = "text = f'This is x: {x}.'"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(__magic_name__ , {"x": 3, "text": "This is x: 3."} )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "if x <= 3:\n y = 2\nelse:\n y = 5"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 2} )
SCREAMING_SNAKE_CASE_ = {"x": 8}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 8, "y": 5} )
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
self.assertListEqual(__magic_name__ , [3, 5] )
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
def __A ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "y = x"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {} , state=__magic_name__ )
assert result == 3
self.assertDictEqual(__magic_name__ , {"x": 3, "y": 3} )
def __A ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = "test_list = [x, add_two(x)]\ntest_list[1]"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_list": [3, 5]} )
SCREAMING_SNAKE_CASE_ = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
SCREAMING_SNAKE_CASE_ = {"x": 3}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"add_two": add_two} , state=__magic_name__ )
assert result == 5
self.assertDictEqual(__magic_name__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = "x = 0\nfor i in range(3):\n x = i"
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = evaluate(__magic_name__ , {"range": range} , state=__magic_name__ )
assert result == 2
self.assertDictEqual(__magic_name__ , {"x": 2, "i": 2} )
| 305
| 0
|
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Union[str, Any] = "▁"
A : List[Any] = {
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
}
A : Dict = {
"vocab_file": {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
),
},
"spm_file": {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
)
},
}
A : Optional[Any] = {
"facebook/s2t-small-librispeech-asr": 10_24,
}
A : Optional[Any] = ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]
A : Dict = {"mustc": MUSTC_LANGS}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = MAX_MODEL_INPUT_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
lowerCamelCase__ = []
def __init__( self : List[str] , __magic_name__ : int , __magic_name__ : List[str] , __magic_name__ : List[Any]="<s>" , __magic_name__ : List[str]="</s>" , __magic_name__ : Any="<pad>" , __magic_name__ : Union[str, Any]="<unk>" , __magic_name__ : Tuple=False , __magic_name__ : int=False , __magic_name__ : List[Any]=None , __magic_name__ : Any=None , __magic_name__ : Optional[Dict[str, Any]] = None , **__magic_name__ : Any , ) -> None:
SCREAMING_SNAKE_CASE_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__magic_name__ , eos_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , do_upper_case=__magic_name__ , do_lower_case=__magic_name__ , tgt_lang=__magic_name__ , lang_codes=__magic_name__ , sp_model_kwargs=self.sp_model_kwargs , **__magic_name__ , )
SCREAMING_SNAKE_CASE_ = do_upper_case
SCREAMING_SNAKE_CASE_ = do_lower_case
SCREAMING_SNAKE_CASE_ = load_json(__magic_name__ )
SCREAMING_SNAKE_CASE_ = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE_ = spm_file
SCREAMING_SNAKE_CASE_ = load_spm(__magic_name__ , self.sp_model_kwargs )
if lang_codes is not None:
SCREAMING_SNAKE_CASE_ = lang_codes
SCREAMING_SNAKE_CASE_ = LANGUAGES[lang_codes]
SCREAMING_SNAKE_CASE_ = [F'''<lang:{lang}>''' for lang in self.langs]
SCREAMING_SNAKE_CASE_ = {lang: self.sp_model.PieceToId(F'''<lang:{lang}>''' ) for lang in self.langs}
SCREAMING_SNAKE_CASE_ = self.lang_tokens
SCREAMING_SNAKE_CASE_ = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
SCREAMING_SNAKE_CASE_ = {}
@property
def __A ( self : Tuple ) -> int:
return len(self.encoder )
@property
def __A ( self : Any ) -> str:
return self._tgt_lang
@tgt_lang.setter
def __A ( self : Union[str, Any] , __magic_name__ : Optional[Any] ) -> None:
SCREAMING_SNAKE_CASE_ = new_tgt_lang
self.set_tgt_lang_special_tokens(__magic_name__ )
def __A ( self : Optional[int] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = self.lang_code_to_id[tgt_lang]
SCREAMING_SNAKE_CASE_ = [lang_code_id]
def __A ( self : Union[str, Any] , __magic_name__ : str ) -> List[str]:
return self.sp_model.encode(__magic_name__ , out_type=__magic_name__ )
def __A ( self : List[str] , __magic_name__ : str ) -> List[str]:
return self.encoder.get(__magic_name__ , self.encoder[self.unk_token] )
def __A ( self : int , __magic_name__ : int ) -> str:
return self.decoder.get(__magic_name__ , self.unk_token )
def __A ( self : List[Any] , __magic_name__ : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
SCREAMING_SNAKE_CASE_ = self.sp_model.decode(__magic_name__ )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
SCREAMING_SNAKE_CASE_ = []
else:
current_sub_tokens.append(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.sp_model.decode(__magic_name__ )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def __A ( self : Dict , __magic_name__ : Optional[int] , __magic_name__ : Optional[int]=None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def __A ( self : List[str] , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None , __magic_name__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__magic_name__ , token_ids_a=__magic_name__ , already_has_special_tokens=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [1] * len(self.prefix_tokens )
SCREAMING_SNAKE_CASE_ = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(__magic_name__ )) + suffix_ones
return prefix_ones + ([0] * len(__magic_name__ )) + ([0] * len(__magic_name__ )) + suffix_ones
def __A ( self : Optional[Any] ) -> Dict:
SCREAMING_SNAKE_CASE_ = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE_ = self.__dict__.copy()
SCREAMING_SNAKE_CASE_ = None
return state
def __setstate__( self : str , __magic_name__ : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = load_spm(self.spm_file , self.sp_model_kwargs )
def __A ( self : Tuple , __magic_name__ : str , __magic_name__ : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE_ = Path(__magic_name__ )
assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
SCREAMING_SNAKE_CASE_ = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
)
SCREAMING_SNAKE_CASE_ = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
)
save_json(self.encoder , __magic_name__ )
if os.path.abspath(self.spm_file ) != os.path.abspath(__magic_name__ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , __magic_name__ )
elif not os.path.isfile(self.spm_file ):
with open(__magic_name__ , "wb" ) as fi:
SCREAMING_SNAKE_CASE_ = self.sp_model.serialized_model_proto()
fi.write(__magic_name__ )
return (str(__magic_name__ ), str(__magic_name__ ))
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sentencepiece.SentencePieceProcessor(**__UpperCamelCase )
spm.Load(str(__UpperCamelCase ) )
return spm
def a__ ( __UpperCamelCase ):
with open(__UpperCamelCase , "r" ) as f:
return json.load(__UpperCamelCase )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
with open(__UpperCamelCase , "w" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase , indent=2 )
| 363
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__UpperCamelCase )] )
SCREAMING_SNAKE_CASE_ = np.array(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __UpperCamelCase ) ) , x.transpose() ) , __UpperCamelCase )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = (1, 2, 1)
SCREAMING_SNAKE_CASE_ = (1, 1, 0, 7)
SCREAMING_SNAKE_CASE_ = SARIMAX(
__UpperCamelCase , exog=__UpperCamelCase , order=__UpperCamelCase , seasonal_order=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = model.fit(disp=__UpperCamelCase , maxiter=6_0_0 , method="nm" )
SCREAMING_SNAKE_CASE_ = model_fit.predict(1 , len(__UpperCamelCase ) , exog=[test_match] )
return result[0]
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = SVR(kernel="rbf" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = regressor.predict(__UpperCamelCase )
return y_pred[0]
def a__ ( __UpperCamelCase ):
train_user.sort()
SCREAMING_SNAKE_CASE_ = np.percentile(__UpperCamelCase , 2_5 )
SCREAMING_SNAKE_CASE_ = np.percentile(__UpperCamelCase , 7_5 )
SCREAMING_SNAKE_CASE_ = qa - qa
SCREAMING_SNAKE_CASE_ = qa - (iqr * 0.1)
return low_lim
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
for i in list_vote:
if i > actual_result:
SCREAMING_SNAKE_CASE_ = not_safe + 1
else:
if abs(abs(__UpperCamelCase ) - abs(__UpperCamelCase ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
A : Optional[Any] = pd.DataFrame(
data_input, columns=["total_user", "total_even", "days"]
)
A : Union[str, Any] = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : List[str] = normalize_df[:, 0].tolist()
A : int = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Tuple = x[: len(x) - 1]
A : str = x[len(x) - 1 :]
# for linear regression & sarimax
A : Tuple = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : List[Any] = total_date[len(total_date) - 1 :]
A : List[Any] = total_user[len(total_user) - 1 :]
A : Optional[Any] = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : Optional[int] = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : str = "" if data_safety_checker(res_vote, tst_user) else "not "
print("Today's data is {not_str}safe.")
| 305
| 0
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
"compression_format, is_archive" , [
("7z", True),
("bz2", False),
("gzip", False),
("lz4", False),
("tar", True),
("xz", False),
("zip", True),
("zstd", False),
] , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
SCREAMING_SNAKE_CASE_ = {
"7z": (seven_zip_file, SevenZipExtractor),
"bz2": (bza_file, BzipaExtractor),
"gzip": (gz_file, GzipExtractor),
"lz4": (lza_file, LzaExtractor),
"tar": (tar_file, TarExtractor),
"xz": (xz_file, XzExtractor),
"zip": (zip_file, ZipExtractor),
"zstd": (zstd_file, ZstdExtractor),
}
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = input_paths_and_base_extractors[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__UpperCamelCase )
assert base_extractor.is_extractable(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = tmp_path / ("extracted" if is_archive else "extracted.txt")
base_extractor.extract(__UpperCamelCase , __UpperCamelCase )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE_ = file_path.read_text(encoding="utf-8" )
else:
SCREAMING_SNAKE_CASE_ = output_path.read_text(encoding="utf-8" )
SCREAMING_SNAKE_CASE_ = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
"compression_format, is_archive" , [
("7z", True),
("bz2", False),
("gzip", False),
("lz4", False),
("tar", True),
("xz", False),
("zip", True),
("zstd", False),
] , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
SCREAMING_SNAKE_CASE_ = {
"7z": seven_zip_file,
"bz2": bza_file,
"gzip": gz_file,
"lz4": lza_file,
"tar": tar_file,
"xz": xz_file,
"zip": zip_file,
"zstd": zstd_file,
}
SCREAMING_SNAKE_CASE_ = input_paths[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE_ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = Extractor.infer_extractor_format(__UpperCamelCase )
assert extractor_format is not None
SCREAMING_SNAKE_CASE_ = tmp_path / ("extracted" if is_archive else "extracted.txt")
Extractor.extract(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE_ = file_path.read_text(encoding="utf-8" )
else:
SCREAMING_SNAKE_CASE_ = output_path.read_text(encoding="utf-8" )
SCREAMING_SNAKE_CASE_ = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def a__ ( __UpperCamelCase , __UpperCamelCase ):
import tarfile
SCREAMING_SNAKE_CASE_ = tmp_path / "data_dot_dot"
directory.mkdir()
SCREAMING_SNAKE_CASE_ = directory / "tar_file_with_dot_dot.tar"
with tarfile.TarFile(__UpperCamelCase , "w" ) as f:
f.add(__UpperCamelCase , arcname=os.path.join(".." , text_file.name ) )
return path
@pytest.fixture
def a__ ( __UpperCamelCase ):
import tarfile
SCREAMING_SNAKE_CASE_ = tmp_path / "data_sym_link"
directory.mkdir()
SCREAMING_SNAKE_CASE_ = directory / "tar_file_with_sym_link.tar"
os.symlink(".." , directory / "subdir" , target_is_directory=__UpperCamelCase )
with tarfile.TarFile(__UpperCamelCase , "w" ) as f:
f.add(str(directory / "subdir" ) , arcname="subdir" ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
"insecure_tar_file, error_log" , [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")] , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = {
"tar_file_with_dot_dot": tar_file_with_dot_dot,
"tar_file_with_sym_link": tar_file_with_sym_link,
}
SCREAMING_SNAKE_CASE_ = insecure_tar_files[insecure_tar_file]
SCREAMING_SNAKE_CASE_ = tmp_path / "extracted"
TarExtractor.extract(__UpperCamelCase , __UpperCamelCase )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def a__ ( __UpperCamelCase ):
# We should have less false positives than zipfile.is_zipfile
# We do that by checking only the magic number
SCREAMING_SNAKE_CASE_ = tmpdir / "not_a_zip_file"
# From: https://github.com/python/cpython/pull/5053
SCREAMING_SNAKE_CASE_ = (
b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
b"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
b"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
b"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
)
with not_a_zip_file.open("wb" ) as f:
f.write(__UpperCamelCase )
assert zipfile.is_zipfile(str(__UpperCamelCase ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(__UpperCamelCase ) # but we're right
| 364
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
"SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwinForImageClassification",
"SwinForMaskedImageModeling",
"SwinModel",
"SwinPreTrainedModel",
"SwinBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
"TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSwinForImageClassification",
"TFSwinForMaskedImageModeling",
"TFSwinModel",
"TFSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = tmp_path / "file.csv"
SCREAMING_SNAKE_CASE_ = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20\n " )
with open(__UpperCamelCase , "w" ) as f:
f.write(__UpperCamelCase )
return str(__UpperCamelCase )
@pytest.fixture
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = tmp_path / "malformed_file.csv"
SCREAMING_SNAKE_CASE_ = textwrap.dedent(
"\\n header1,header2\n 1,2\n 10,20,\n " )
with open(__UpperCamelCase , "w" ) as f:
f.write(__UpperCamelCase )
return str(__UpperCamelCase )
@pytest.fixture
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = tmp_path / "csv_with_image.csv"
SCREAMING_SNAKE_CASE_ = textwrap.dedent(
F'''\
image
{image_file}
''' )
with open(__UpperCamelCase , "w" ) as f:
f.write(__UpperCamelCase )
return str(__UpperCamelCase )
@pytest.fixture
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = tmp_path / "csv_with_label.csv"
SCREAMING_SNAKE_CASE_ = textwrap.dedent(
"\\n label\n good\n bad\n good\n " )
with open(__UpperCamelCase , "w" ) as f:
f.write(__UpperCamelCase )
return str(__UpperCamelCase )
@pytest.fixture
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = tmp_path / "csv_with_int_list.csv"
SCREAMING_SNAKE_CASE_ = textwrap.dedent(
"\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n " )
with open(__UpperCamelCase , "w" ) as f:
f.write(__UpperCamelCase )
return str(__UpperCamelCase )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = Csv()
SCREAMING_SNAKE_CASE_ = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__UpperCamelCase , match="Error tokenizing data" ):
for _ in generator:
pass
assert any(
record.levelname == "ERROR"
and "Failed to read file" in record.message
and os.path.basename(__UpperCamelCase ) in record.message
for record in caplog.records )
@require_pil
def a__ ( __UpperCamelCase ):
with open(__UpperCamelCase , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ = f.read().splitlines()[1]
SCREAMING_SNAKE_CASE_ = Csv(encoding="utf-8" , features=Features({"image": Image()} ) )
SCREAMING_SNAKE_CASE_ = csv._generate_tables([[csv_file_with_image]] )
SCREAMING_SNAKE_CASE_ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("image" ).type == Image()()
SCREAMING_SNAKE_CASE_ = pa_table.to_pydict()["image"]
assert generated_content == [{"path": image_file, "bytes": None}]
def a__ ( __UpperCamelCase ):
with open(__UpperCamelCase , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ = f.read().splitlines()[1:]
SCREAMING_SNAKE_CASE_ = Csv(encoding="utf-8" , features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
SCREAMING_SNAKE_CASE_ = csv._generate_tables([[csv_file_with_label]] )
SCREAMING_SNAKE_CASE_ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
SCREAMING_SNAKE_CASE_ = pa_table.to_pydict()["label"]
assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(__UpperCamelCase ) for label in labels]
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = Csv(encoding="utf-8" , sep="," , converters={"int_list": lambda __UpperCamelCase : [int(__UpperCamelCase ) for i in x.split()]} )
SCREAMING_SNAKE_CASE_ = csv._generate_tables([[csv_file_with_int_list]] )
SCREAMING_SNAKE_CASE_ = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
SCREAMING_SNAKE_CASE_ = pa_table.to_pydict()["int_list"]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 365
|
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
A : Union[str, Any] = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@classmethod
def __A ( cls : Any ) -> Dict:
SCREAMING_SNAKE_CASE_ = TOKEN
HfFolder.save_token(__magic_name__ )
@classmethod
def __A ( cls : Optional[int] ) -> Tuple:
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def __A ( self : str ) -> str:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(__magic_name__ , repo_id="test-model-flax" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(F'''{USER}/test-model-flax''' )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def __A ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
__magic_name__ , repo_id="valid_org/test-model-flax-org" , push_to_hub=__magic_name__ , use_auth_token=self._token )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE_ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE_ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(__magic_name__ , 1e-3 , msg=F'''{key} not identical''' )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
SCREAMING_SNAKE_CASE_ = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
SCREAMING_SNAKE_CASE_ = False
return models_are_equal
@require_flax
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE_ = FlaxBertModel(__magic_name__ )
SCREAMING_SNAKE_CASE_ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(__magic_name__ , __magic_name__ ) , max_shard_size="10KB" )
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertTrue(check_models_equal(__magic_name__ , __magic_name__ ) )
def __A ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def __A ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE_ = "bert"
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = FlaxBertModel.from_pretrained(__magic_name__ , subfolder=__magic_name__ )
self.assertIsNotNone(__magic_name__ )
| 305
| 0
|
import cmath
import math
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = math.radians(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = math.radians(__UpperCamelCase )
# Convert voltage and current to rectangular form
SCREAMING_SNAKE_CASE_ = cmath.rect(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = cmath.rect(__UpperCamelCase , __UpperCamelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : Union[str, Any] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def a__ ( ):
SCREAMING_SNAKE_CASE_ = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
SCREAMING_SNAKE_CASE_ = get_sagemaker_input()
else:
SCREAMING_SNAKE_CASE_ = get_cluster_input()
return config
def a__ ( __UpperCamelCase=None ):
if subparsers is not None:
SCREAMING_SNAKE_CASE_ = subparsers.add_parser("config" , description=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Accelerate config command" , description=__UpperCamelCase )
parser.add_argument(
"--config_file" , default=__UpperCamelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__UpperCamelCase )
return parser
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = get_user_input()
if args.config_file is not None:
SCREAMING_SNAKE_CASE_ = args.config_file
else:
if not os.path.isdir(__UpperCamelCase ):
os.makedirs(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__UpperCamelCase )
else:
config.to_yaml_file(__UpperCamelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = config_command_parser()
SCREAMING_SNAKE_CASE_ = parser.parse_args()
config_command(__UpperCamelCase )
if __name__ == "__main__":
main()
| 305
| 0
|
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : Optional[Any] = logging.get_logger(__name__)
A : int = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''deta'''
lowerCamelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : Optional[Any] , __magic_name__ : List[Any]=None , __magic_name__ : str=900 , __magic_name__ : Union[str, Any]=2_048 , __magic_name__ : List[str]=6 , __magic_name__ : Union[str, Any]=2_048 , __magic_name__ : int=8 , __magic_name__ : List[str]=6 , __magic_name__ : Tuple=1_024 , __magic_name__ : Tuple=8 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Optional[int]=True , __magic_name__ : str="relu" , __magic_name__ : int=256 , __magic_name__ : Tuple=0.1 , __magic_name__ : Optional[int]=0.0 , __magic_name__ : Optional[Any]=0.0 , __magic_name__ : List[str]=0.02 , __magic_name__ : Tuple=1.0 , __magic_name__ : List[str]=True , __magic_name__ : Optional[Any]=False , __magic_name__ : Dict="sine" , __magic_name__ : Union[str, Any]=5 , __magic_name__ : Tuple=4 , __magic_name__ : Union[str, Any]=4 , __magic_name__ : List[str]=True , __magic_name__ : List[str]=300 , __magic_name__ : Optional[Any]=True , __magic_name__ : Optional[Any]=True , __magic_name__ : str=1 , __magic_name__ : Union[str, Any]=5 , __magic_name__ : List[str]=2 , __magic_name__ : Tuple=1 , __magic_name__ : List[Any]=1 , __magic_name__ : str=5 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Optional[int]=0.1 , __magic_name__ : Any=0.25 , **__magic_name__ : Tuple , ) -> Optional[int]:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
else:
if isinstance(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = backbone_config.pop("model_type" )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE_ = config_class.from_dict(__magic_name__ )
SCREAMING_SNAKE_CASE_ = backbone_config
SCREAMING_SNAKE_CASE_ = num_queries
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = init_xavier_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = auxiliary_loss
SCREAMING_SNAKE_CASE_ = position_embedding_type
# deformable attributes
SCREAMING_SNAKE_CASE_ = num_feature_levels
SCREAMING_SNAKE_CASE_ = encoder_n_points
SCREAMING_SNAKE_CASE_ = decoder_n_points
SCREAMING_SNAKE_CASE_ = two_stage
SCREAMING_SNAKE_CASE_ = two_stage_num_proposals
SCREAMING_SNAKE_CASE_ = with_box_refine
SCREAMING_SNAKE_CASE_ = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError("If two_stage is True, with_box_refine must be True." )
# Hungarian matcher
SCREAMING_SNAKE_CASE_ = class_cost
SCREAMING_SNAKE_CASE_ = bbox_cost
SCREAMING_SNAKE_CASE_ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE_ = mask_loss_coefficient
SCREAMING_SNAKE_CASE_ = dice_loss_coefficient
SCREAMING_SNAKE_CASE_ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE_ = giou_loss_coefficient
SCREAMING_SNAKE_CASE_ = eos_coefficient
SCREAMING_SNAKE_CASE_ = focal_alpha
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def __A ( self : str ) -> int:
return self.encoder_attention_heads
@property
def __A ( self : Any ) -> int:
return self.d_model
def __A ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 367
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(default='''summarization''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
lowerCamelCase__ = Features({'''text''': Value('''string''' )} )
lowerCamelCase__ = Features({'''summary''': Value('''string''' )} )
lowerCamelCase__ = "text"
lowerCamelCase__ = "summary"
@property
def __A ( self : Dict ) -> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 305
| 0
|
from __future__ import annotations
import math
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if len(__UpperCamelCase ) != 2 or len(a[0] ) != 2 or len(__UpperCamelCase ) != 2 or len(b[0] ) != 2:
raise Exception("Matrices are not 2x2" )
SCREAMING_SNAKE_CASE_ = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__UpperCamelCase ) )
]
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__UpperCamelCase ) )
]
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception("Odd matrices are not supported!" )
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = matrix_length // 2
SCREAMING_SNAKE_CASE_ = [[a[i][j] for j in range(__UpperCamelCase , __UpperCamelCase )] for i in range(__UpperCamelCase )]
SCREAMING_SNAKE_CASE_ = [
[a[i][j] for j in range(__UpperCamelCase , __UpperCamelCase )] for i in range(__UpperCamelCase , __UpperCamelCase )
]
SCREAMING_SNAKE_CASE_ = [[a[i][j] for j in range(__UpperCamelCase )] for i in range(__UpperCamelCase )]
SCREAMING_SNAKE_CASE_ = [[a[i][j] for j in range(__UpperCamelCase )] for i in range(__UpperCamelCase , __UpperCamelCase )]
return top_left, top_right, bot_left, bot_right
def a__ ( __UpperCamelCase ):
return len(__UpperCamelCase ), len(matrix[0] )
def a__ ( __UpperCamelCase ):
print("\n".join(str(__UpperCamelCase ) for line in matrix ) )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if matrix_dimensions(__UpperCamelCase ) == (2, 2):
return default_matrix_multiplication(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = split_matrix(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = split_matrix(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = actual_strassen(__UpperCamelCase , matrix_subtraction(__UpperCamelCase , __UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = actual_strassen(matrix_addition(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = actual_strassen(matrix_addition(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = actual_strassen(__UpperCamelCase , matrix_subtraction(__UpperCamelCase , __UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = actual_strassen(matrix_addition(__UpperCamelCase , __UpperCamelCase ) , matrix_addition(__UpperCamelCase , __UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = actual_strassen(matrix_subtraction(__UpperCamelCase , __UpperCamelCase ) , matrix_addition(__UpperCamelCase , __UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = actual_strassen(matrix_subtraction(__UpperCamelCase , __UpperCamelCase ) , matrix_addition(__UpperCamelCase , __UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = matrix_addition(matrix_subtraction(matrix_addition(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase ) , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = matrix_addition(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = matrix_addition(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = matrix_subtraction(matrix_subtraction(matrix_addition(__UpperCamelCase , __UpperCamelCase ) , __UpperCamelCase ) , __UpperCamelCase )
# construct the new matrix from our 4 quadrants
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__UpperCamelCase ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(__UpperCamelCase ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if matrix_dimensions(__UpperCamelCase )[1] != matrix_dimensions(__UpperCamelCase )[0]:
SCREAMING_SNAKE_CASE_ = (
"Unable to multiply these matrices, please check the dimensions.\n"
F'''Matrix A: {matrixa}\n'''
F'''Matrix B: {matrixa}'''
)
raise Exception(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = matrix_dimensions(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = matrix_dimensions(__UpperCamelCase )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
SCREAMING_SNAKE_CASE_ = max(*__UpperCamelCase , *__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = int(math.pow(2 , math.ceil(math.loga(__UpperCamelCase ) ) ) )
SCREAMING_SNAKE_CASE_ = matrixa
SCREAMING_SNAKE_CASE_ = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , __UpperCamelCase ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __UpperCamelCase ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __UpperCamelCase ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
SCREAMING_SNAKE_CASE_ = actual_strassen(__UpperCamelCase , __UpperCamelCase )
# Removing the additional zeros
for i in range(0 , __UpperCamelCase ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __UpperCamelCase ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
A : Any = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
A : Any = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrixa, matrixa))
| 368
|
from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None , __magic_name__ : List[str]=2_048 ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = config.__dict__
SCREAMING_SNAKE_CASE_ = modal_hidden_size
if num_labels:
SCREAMING_SNAKE_CASE_ = num_labels
| 305
| 0
|
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
A : Union[str, Any] = sys.version_info >= (3, 10)
def a__ ( __UpperCamelCase=None , __UpperCamelCase=None ):
return field(default_factory=lambda: default , metadata=__UpperCamelCase )
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = 4_2
lowerCamelCase__ = field(default='''toto''' , metadata={'''help''': '''help message'''} )
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = False
lowerCamelCase__ = True
lowerCamelCase__ = None
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''titi'''
lowerCamelCase__ = '''toto'''
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''titi'''
lowerCamelCase__ = '''toto'''
lowerCamelCase__ = 4_2
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = '''toto'''
def __A ( self : str ) -> int:
SCREAMING_SNAKE_CASE_ = BasicEnum(self.foo )
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = '''toto'''
def __A ( self : Optional[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = MixedTypeEnum(self.foo )
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = None
lowerCamelCase__ = field(default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''help message'''} )
lowerCamelCase__ = None
lowerCamelCase__ = list_field(default=[] )
lowerCamelCase__ = list_field(default=[] )
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = list_field(default=[] )
lowerCamelCase__ = list_field(default=[1, 2, 3] )
lowerCamelCase__ = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
lowerCamelCase__ = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = field()
lowerCamelCase__ = field()
lowerCamelCase__ = field()
def __A ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE_ = BasicEnum(self.required_enum )
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = 4_2
lowerCamelCase__ = field()
lowerCamelCase__ = None
lowerCamelCase__ = field(default='''toto''' , metadata={'''help''': '''help message'''} )
lowerCamelCase__ = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
if is_python_no_less_than_3_10:
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = False
lowerCamelCase__ = True
lowerCamelCase__ = None
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = None
lowerCamelCase__ = field(default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''help message'''} )
lowerCamelCase__ = None
lowerCamelCase__ = list_field(default=[] )
lowerCamelCase__ = list_field(default=[] )
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] , __magic_name__ : argparse.ArgumentParser , __magic_name__ : argparse.ArgumentParser ) -> int:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
SCREAMING_SNAKE_CASE_ = {k: v for k, v in vars(__magic_name__ ).items() if k != "container"}
SCREAMING_SNAKE_CASE_ = {k: v for k, v in vars(__magic_name__ ).items() if k != "container"}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("choices" , __magic_name__ ) and yy.get("choices" , __magic_name__ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["type"](__magic_name__ ) , yy["type"](__magic_name__ ) )
del xx["type"], yy["type"]
self.assertEqual(__magic_name__ , __magic_name__ )
def __A ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=__magic_name__ , required=__magic_name__ )
expected.add_argument("--bar" , type=__magic_name__ , required=__magic_name__ )
expected.add_argument("--baz" , type=__magic_name__ , required=__magic_name__ )
expected.add_argument("--flag" , type=__magic_name__ , default=__magic_name__ , const=__magic_name__ , nargs="?" )
self.argparsersEqual(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
((SCREAMING_SNAKE_CASE_ ) , ) = parser.parse_args_into_dataclasses(__magic_name__ , look_for_args_file=__magic_name__ )
self.assertFalse(example.flag )
def __A ( self : List[Any] ) -> Any:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument("--foo" , default=42 , type=__magic_name__ )
expected.add_argument("--baz" , default="toto" , type=__magic_name__ , help="help message" )
self.argparsersEqual(__magic_name__ , __magic_name__ )
def __A ( self : Optional[int] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=__magic_name__ , default=__magic_name__ , const=__magic_name__ , nargs="?" )
expected.add_argument("--baz" , type=__magic_name__ , default=__magic_name__ , const=__magic_name__ , nargs="?" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("--no_baz" , action="store_false" , default=__magic_name__ , dest="baz" )
expected.add_argument("--opt" , type=__magic_name__ , default=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__magic_name__ )
for dataclass_type in dataclass_types:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
self.argparsersEqual(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = parser.parse_args([] )
self.assertEqual(__magic_name__ , Namespace(foo=__magic_name__ , baz=__magic_name__ , opt=__magic_name__ ) )
SCREAMING_SNAKE_CASE_ = parser.parse_args(["--foo", "--no_baz"] )
self.assertEqual(__magic_name__ , Namespace(foo=__magic_name__ , baz=__magic_name__ , opt=__magic_name__ ) )
SCREAMING_SNAKE_CASE_ = parser.parse_args(["--foo", "--baz"] )
self.assertEqual(__magic_name__ , Namespace(foo=__magic_name__ , baz=__magic_name__ , opt=__magic_name__ ) )
SCREAMING_SNAKE_CASE_ = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"] )
self.assertEqual(__magic_name__ , Namespace(foo=__magic_name__ , baz=__magic_name__ , opt=__magic_name__ ) )
SCREAMING_SNAKE_CASE_ = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"] )
self.assertEqual(__magic_name__ , Namespace(foo=__magic_name__ , baz=__magic_name__ , opt=__magic_name__ ) )
def __A ( self : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=["titi", "toto", 42] , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
SCREAMING_SNAKE_CASE_ = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
SCREAMING_SNAKE_CASE_ = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
SCREAMING_SNAKE_CASE_ = parser.parse_args_into_dataclasses(["--foo", "titi"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
SCREAMING_SNAKE_CASE_ = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
SCREAMING_SNAKE_CASE_ = parser.parse_args_into_dataclasses(["--foo", "42"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def __A ( self : Optional[Any] ) -> List[Any]:
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = '''toto'''
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=("titi", "toto", 42) , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
SCREAMING_SNAKE_CASE_ = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
SCREAMING_SNAKE_CASE_ = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
def __A ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument("--foo_int" , nargs="+" , default=[] , type=__magic_name__ )
expected.add_argument("--bar_int" , nargs="+" , default=[1, 2, 3] , type=__magic_name__ )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=__magic_name__ )
expected.add_argument("--foo_float" , nargs="+" , default=[0.1, 0.2, 0.3] , type=__magic_name__ )
self.argparsersEqual(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = parser.parse_args([] )
self.assertEqual(
__magic_name__ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["Hallo", "Bonjour", "Hello"] , foo_float=[0.1, 0.2, 0.3] ) , )
SCREAMING_SNAKE_CASE_ = parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split() )
self.assertEqual(__magic_name__ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["a", "b", "c"] , foo_float=[0.1, 0.7] ) )
def __A ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument("--foo" , default=__magic_name__ , type=__magic_name__ )
expected.add_argument("--bar" , default=__magic_name__ , type=__magic_name__ , help="help message" )
expected.add_argument("--baz" , default=__magic_name__ , type=__magic_name__ )
expected.add_argument("--ces" , nargs="+" , default=[] , type=__magic_name__ )
expected.add_argument("--des" , nargs="+" , default=[] , type=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__magic_name__ )
for dataclass_type in dataclass_types:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
self.argparsersEqual(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = parser.parse_args([] )
self.assertEqual(__magic_name__ , Namespace(foo=__magic_name__ , bar=__magic_name__ , baz=__magic_name__ , ces=[] , des=[] ) )
SCREAMING_SNAKE_CASE_ = parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split() )
self.assertEqual(__magic_name__ , Namespace(foo=12 , bar=3.14 , baz="42" , ces=["a", "b", "c"] , des=[1, 2, 3] ) )
def __A ( self : int ) -> List[str]:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument("--required_list" , nargs="+" , type=__magic_name__ , required=__magic_name__ )
expected.add_argument("--required_str" , type=__magic_name__ , required=__magic_name__ )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=__magic_name__ , )
self.argparsersEqual(__magic_name__ , __magic_name__ )
def __A ( self : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=__magic_name__ , required=__magic_name__ )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=__magic_name__ , )
expected.add_argument("--opt" , type=__magic_name__ , default=__magic_name__ )
expected.add_argument("--baz" , default="toto" , type=__magic_name__ , help="help message" )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=__magic_name__ )
self.argparsersEqual(__magic_name__ , __magic_name__ )
def __A ( self : List[Any] ) -> Any:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
SCREAMING_SNAKE_CASE_ = parser.parse_dict(__magic_name__ )[0]
SCREAMING_SNAKE_CASE_ = BasicExample(**__magic_name__ )
self.assertEqual(__magic_name__ , __magic_name__ )
def __A ( self : Tuple ) -> Dict:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
"extra": 42,
}
self.assertRaises(__magic_name__ , parser.parse_dict , __magic_name__ , allow_extra_keys=__magic_name__ )
def __A ( self : Optional[int] ) -> int:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE_ = os.path.join(__magic_name__ , "temp_json" )
os.mkdir(__magic_name__ )
with open(temp_local_path + ".json" , "w+" ) as f:
json.dump(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = parser.parse_yaml_file(Path(temp_local_path + ".json" ) )[0]
SCREAMING_SNAKE_CASE_ = BasicExample(**__magic_name__ )
self.assertEqual(__magic_name__ , __magic_name__ )
def __A ( self : List[Any] ) -> Any:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
SCREAMING_SNAKE_CASE_ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE_ = os.path.join(__magic_name__ , "temp_yaml" )
os.mkdir(__magic_name__ )
with open(temp_local_path + ".yaml" , "w+" ) as f:
yaml.dump(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = parser.parse_yaml_file(Path(temp_local_path + ".yaml" ) )[0]
SCREAMING_SNAKE_CASE_ = BasicExample(**__magic_name__ )
self.assertEqual(__magic_name__ , __magic_name__ )
def __A ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE_ = HfArgumentParser(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
| 369
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViltImageProcessor'''
lowerCamelCase__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[int] , __magic_name__ : str=None , __magic_name__ : List[str]=None , **__magic_name__ : 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`." )
super().__init__(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = self.image_processor
def __call__( self : List[str] , __magic_name__ : List[str] , __magic_name__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __magic_name__ : bool = True , __magic_name__ : Union[bool, str, PaddingStrategy] = False , __magic_name__ : Union[bool, str, TruncationStrategy] = None , __magic_name__ : Optional[int] = None , __magic_name__ : int = 0 , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : Optional[bool] = None , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = False , __magic_name__ : bool = True , __magic_name__ : Optional[Union[str, TensorType]] = None , **__magic_name__ : str , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE_ = self.tokenizer(
text=__magic_name__ , add_special_tokens=__magic_name__ , padding=__magic_name__ , truncation=__magic_name__ , max_length=__magic_name__ , stride=__magic_name__ , pad_to_multiple_of=__magic_name__ , return_token_type_ids=__magic_name__ , return_attention_mask=__magic_name__ , return_overflowing_tokens=__magic_name__ , return_special_tokens_mask=__magic_name__ , return_offsets_mapping=__magic_name__ , return_length=__magic_name__ , verbose=__magic_name__ , return_tensors=__magic_name__ , **__magic_name__ , )
# add pixel_values + pixel_mask
SCREAMING_SNAKE_CASE_ = self.image_processor(__magic_name__ , return_tensors=__magic_name__ )
encoding.update(__magic_name__ )
return encoding
def __A ( self : Optional[int] , *__magic_name__ : List[Any] , **__magic_name__ : Optional[Any] ) -> Any:
return self.tokenizer.batch_decode(*__magic_name__ , **__magic_name__ )
def __A ( self : Dict , *__magic_name__ : List[Any] , **__magic_name__ : Union[str, Any] ) -> str:
return self.tokenizer.decode(*__magic_name__ , **__magic_name__ )
@property
def __A ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __A ( self : Dict ) -> List[Any]:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __magic_name__ , )
return self.image_processor_class
@property
def __A ( self : int ) -> List[Any]:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __magic_name__ , )
return self.image_processor
| 305
| 0
|
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("Input value must be an 'int' type" )
SCREAMING_SNAKE_CASE_ = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370
|
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 ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : Optional[int] = {
"microsoft/table-transformer-detection": (
"https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
),
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''table-transformer'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self : List[Any] , __magic_name__ : Optional[Any]=True , __magic_name__ : Dict=None , __magic_name__ : Any=3 , __magic_name__ : List[str]=100 , __magic_name__ : Union[str, Any]=6 , __magic_name__ : Dict=2_048 , __magic_name__ : str=8 , __magic_name__ : int=6 , __magic_name__ : List[Any]=2_048 , __magic_name__ : Optional[int]=8 , __magic_name__ : Optional[int]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Optional[Any]=True , __magic_name__ : List[Any]="relu" , __magic_name__ : List[str]=256 , __magic_name__ : List[str]=0.1 , __magic_name__ : int=0.0 , __magic_name__ : Optional[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : str=1.0 , __magic_name__ : int=False , __magic_name__ : Dict="sine" , __magic_name__ : Union[str, Any]="resnet50" , __magic_name__ : Optional[Any]=True , __magic_name__ : str=False , __magic_name__ : List[str]=1 , __magic_name__ : int=5 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Tuple=1 , __magic_name__ : Optional[int]=1 , __magic_name__ : Optional[Any]=5 , __magic_name__ : Optional[int]=2 , __magic_name__ : Union[str, Any]=0.1 , **__magic_name__ : Tuple , ) -> str:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__magic_name__ , __magic_name__ ):
SCREAMING_SNAKE_CASE_ = backbone_config.get("model_type" )
SCREAMING_SNAKE_CASE_ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE_ = config_class.from_dict(__magic_name__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None, None, None
SCREAMING_SNAKE_CASE_ = use_timm_backbone
SCREAMING_SNAKE_CASE_ = backbone_config
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = num_queries
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = init_xavier_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = auxiliary_loss
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = backbone
SCREAMING_SNAKE_CASE_ = use_pretrained_backbone
SCREAMING_SNAKE_CASE_ = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE_ = class_cost
SCREAMING_SNAKE_CASE_ = bbox_cost
SCREAMING_SNAKE_CASE_ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE_ = mask_loss_coefficient
SCREAMING_SNAKE_CASE_ = dice_loss_coefficient
SCREAMING_SNAKE_CASE_ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE_ = giou_loss_coefficient
SCREAMING_SNAKE_CASE_ = eos_coefficient
super().__init__(is_encoder_decoder=__magic_name__ , **__magic_name__ )
@property
def __A ( self : Union[str, Any] ) -> int:
return self.encoder_attention_heads
@property
def __A ( self : Any ) -> int:
return self.d_model
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = version.parse('''1.11''' )
@property
def __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def __A ( self : Any ) -> float:
return 1e-5
@property
def __A ( self : int ) -> int:
return 12
| 305
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[Any] = logging.get_logger(__name__)
A : int = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''trocr'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__( self : List[str] , __magic_name__ : Tuple=50_265 , __magic_name__ : List[Any]=1_024 , __magic_name__ : Tuple=12 , __magic_name__ : Tuple=16 , __magic_name__ : List[Any]=4_096 , __magic_name__ : Optional[Any]="gelu" , __magic_name__ : str=512 , __magic_name__ : Optional[int]=0.1 , __magic_name__ : List[Any]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Any=2 , __magic_name__ : str=0.02 , __magic_name__ : str=0.0 , __magic_name__ : Optional[Any]=True , __magic_name__ : Tuple=False , __magic_name__ : Tuple=True , __magic_name__ : Tuple=True , __magic_name__ : Dict=1 , __magic_name__ : Optional[Any]=0 , __magic_name__ : int=2 , **__magic_name__ : List[str] , ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = scale_embedding
SCREAMING_SNAKE_CASE_ = use_learned_position_embeddings
SCREAMING_SNAKE_CASE_ = layernorm_embedding
super().__init__(
pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , decoder_start_token_id=__magic_name__ , **__magic_name__ , )
| 371
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Dict = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
# See all REALM models at https://huggingface.co/models?filter=realm
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''realm'''
def __init__( self : Dict , __magic_name__ : str=30_522 , __magic_name__ : List[str]=768 , __magic_name__ : str=128 , __magic_name__ : List[Any]=12 , __magic_name__ : List[str]=12 , __magic_name__ : List[str]=8 , __magic_name__ : Any=3_072 , __magic_name__ : str="gelu_new" , __magic_name__ : Optional[Any]=0.1 , __magic_name__ : Union[str, Any]=0.1 , __magic_name__ : Optional[int]=512 , __magic_name__ : Tuple=2 , __magic_name__ : int=0.02 , __magic_name__ : Union[str, Any]=1e-12 , __magic_name__ : Union[str, Any]=256 , __magic_name__ : Optional[Any]=10 , __magic_name__ : Optional[int]=1e-3 , __magic_name__ : Dict=5 , __magic_name__ : Dict=320 , __magic_name__ : List[Any]=13_353_718 , __magic_name__ : Optional[Any]=5_000 , __magic_name__ : List[Any]=1 , __magic_name__ : Optional[int]=0 , __magic_name__ : Optional[Any]=2 , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
# Common config
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = retriever_proj_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = num_candidates
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_ = initializer_range
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = layer_norm_eps
# Reader config
SCREAMING_SNAKE_CASE_ = span_hidden_size
SCREAMING_SNAKE_CASE_ = max_span_width
SCREAMING_SNAKE_CASE_ = reader_layer_norm_eps
SCREAMING_SNAKE_CASE_ = reader_beam_size
SCREAMING_SNAKE_CASE_ = reader_seq_len
# Retrieval config
SCREAMING_SNAKE_CASE_ = num_block_records
SCREAMING_SNAKE_CASE_ = searcher_beam_size
| 350
|
from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to be of square shaped array but got a "
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
SCREAMING_SNAKE_CASE_ = (table[i][j] - total) / upper[j][j]
SCREAMING_SNAKE_CASE_ = 1
for j in range(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@slow
def __A ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE_ = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
SCREAMING_SNAKE_CASE_ = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
model.to(__magic_name__ )
from datasets import load_dataset
SCREAMING_SNAKE_CASE_ = load_dataset("nielsr/rvlcdip-demo" )
SCREAMING_SNAKE_CASE_ = dataset["train"][0]["image"].convert("RGB" )
SCREAMING_SNAKE_CASE_ = image_processor(__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 16) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor(
[-0.4158, -0.4092, -0.4347] , device=__magic_name__ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
| 351
|
from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values" )
return 4 * pi * radius**2
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values" )
return 3 * pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values" )
SCREAMING_SNAKE_CASE_ = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values" )
return 2 * pi * radius * (height + radius)
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values" )
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori" )
return 4 * pow(__UpperCamelCase , 2 ) * torus_radius * tube_radius
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values" )
return length * width
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values" )
return side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values" )
return (base * height) / 2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle" )
SCREAMING_SNAKE_CASE_ = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE_ = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values" )
return base * height
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values" )
return 1 / 2 * (basea + basea) * height
def a__ ( __UpperCamelCase ):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values" )
return pi * radius**2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values" )
return pi * radius_x * radius_y
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values" )
return 1 / 2 * diagonal_a * diagonal_a
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides" )
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("[DEMO] Areas of various geometric shapes: \n")
print(f"Rectangle: {area_rectangle(10, 20) = }")
print(f"Square: {area_square(10) = }")
print(f"Triangle: {area_triangle(10, 10) = }")
print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(f"Parallelogram: {area_parallelogram(10, 20) = }")
print(f"Rhombus: {area_rhombus(10, 20) = }")
print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
print(f"Circle: {area_circle(20) = }")
print(f"Ellipse: {area_ellipse(10, 20) = }")
print("\nSurface Areas of various geometric shapes: \n")
print(f"Cube: {surface_area_cube(20) = }")
print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(f"Sphere: {surface_area_sphere(20) = }")
print(f"Hemisphere: {surface_area_hemisphere(20) = }")
print(f"Cone: {surface_area_cone(10, 20) = }")
print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
print(f"Torus: {surface_area_torus(20, 10) = }")
print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(f"Square: {area_reg_polygon(4, 10) = }")
print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 305
| 0
|
from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to be of square shaped array but got a "
F'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
SCREAMING_SNAKE_CASE_ = np.zeros((rows, columns) )
for i in range(__UpperCamelCase ):
for j in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
if upper[j][j] == 0:
raise ArithmeticError("No LU decomposition exists" )
SCREAMING_SNAKE_CASE_ = (table[i][j] - total) / upper[j][j]
SCREAMING_SNAKE_CASE_ = 1
for j in range(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 352
|
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
A : List[str] = logging.get_logger(__name__)
A : int = {
"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 lowerCamelCase (SCREAMING_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 : Dict , __magic_name__ : Dict=50_265 , __magic_name__ : str=512 , __magic_name__ : List[Any]=8 , __magic_name__ : Any=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Any=8 , __magic_name__ : Optional[int]=2_048 , __magic_name__ : Dict=16 , __magic_name__ : Tuple=0.0 , __magic_name__ : Dict=0.0 , __magic_name__ : Optional[int]=True , __magic_name__ : Any=True , __magic_name__ : Dict="gelu" , __magic_name__ : Tuple=512 , __magic_name__ : List[str]=0.1 , __magic_name__ : List[Any]=0.0 , __magic_name__ : List[Any]=0.0 , __magic_name__ : Tuple=0.02 , __magic_name__ : Union[str, Any]=1 , __magic_name__ : List[Any]=False , __magic_name__ : str=0 , __magic_name__ : Dict=1 , __magic_name__ : str=2 , __magic_name__ : Union[str, Any]=2 , **__magic_name__ : Optional[Any] , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , is_encoder_decoder=__magic_name__ , decoder_start_token_id=__magic_name__ , forced_eos_token_id=__magic_name__ , **__magic_name__ , )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
@property
def __A ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ = {0: "batch"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
else:
SCREAMING_SNAKE_CASE_ = 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 __A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super().outputs
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
for i in range(__magic_name__ ):
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
SCREAMING_SNAKE_CASE_ = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __A ( self : int , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# Generate decoder inputs
SCREAMING_SNAKE_CASE_ = seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE_ = dict(**__magic_name__ , **__magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
SCREAMING_SNAKE_CASE_ = common_inputs["decoder_input_ids"].shape[1]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = decoder_seq_length + 3
SCREAMING_SNAKE_CASE_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(__magic_name__ , __magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ = min(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = max(__magic_name__ , __magic_name__ ) - min_num_layers
SCREAMING_SNAKE_CASE_ = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(__magic_name__ ):
common_inputs["past_key_values"].append(
(
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
torch.zeros(__magic_name__ ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE_ = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(__magic_name__ , __magic_name__ ):
common_inputs["past_key_values"].append((torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) )
return common_inputs
def __A ( self : Union[str, Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE_ = seqlen + 2
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_layers
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.num_attention_heads
SCREAMING_SNAKE_CASE_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE_ = common_inputs["attention_mask"].dtype
SCREAMING_SNAKE_CASE_ = torch.cat(
[common_inputs["attention_mask"], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
SCREAMING_SNAKE_CASE_ = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(__magic_name__ )
]
return common_inputs
def __A ( self : Dict , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , 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
SCREAMING_SNAKE_CASE_ = tokenizer.num_special_tokens_to_add(__magic_name__ )
SCREAMING_SNAKE_CASE_ = compute_effective_axis_dimension(
__magic_name__ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__magic_name__ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE_ = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE_ = dict(tokenizer(__magic_name__ , return_tensors=__magic_name__ ) )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
elif self.task == "causal-lm":
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_causal_lm(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
return common_inputs
def __A ( self : Optional[Any] , __magic_name__ : str , __magic_name__ : List[Any] , __magic_name__ : str , __magic_name__ : List[str] ) -> List[str]:
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE_ = super()._flatten_past_key_values_(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
else:
SCREAMING_SNAKE_CASE_ = super(__magic_name__ , self )._flatten_past_key_values_(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
| 305
| 0
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
A : int = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Union[str, Any] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 353
|
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , __magic_name__ : List[str] , __magic_name__ : int=100 , __magic_name__ : Optional[Any]=13 , __magic_name__ : Dict=30 , __magic_name__ : Tuple=2 , __magic_name__ : str=3 , __magic_name__ : str=True , __magic_name__ : Optional[int]=True , __magic_name__ : Union[str, Any]=32 , __magic_name__ : Optional[int]=4 , __magic_name__ : Dict=4 , __magic_name__ : Tuple=37 , __magic_name__ : Any="gelu" , __magic_name__ : int=0.1 , __magic_name__ : List[str]=0.1 , __magic_name__ : Optional[int]=10 , __magic_name__ : Tuple=0.02 , __magic_name__ : Optional[int]=3 , __magic_name__ : List[str]=None , __magic_name__ : Tuple=[0, 1, 2, 3] , ) -> List[str]:
SCREAMING_SNAKE_CASE_ = parent
SCREAMING_SNAKE_CASE_ = 100
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
SCREAMING_SNAKE_CASE_ = scope
SCREAMING_SNAKE_CASE_ = out_indices
SCREAMING_SNAKE_CASE_ = num_labels
# in BeiT, 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 __A ( self : Any ) -> int:
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE_ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __A ( self : Dict ) -> Optional[int]:
return BeitConfig(
vocab_size=self.vocab_size , 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=__magic_name__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def __A ( self : Optional[int] , __magic_name__ : List[str] , __magic_name__ : Optional[int] , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = BeitModel(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : str ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling(config=__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __A ( self : Dict , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : Tuple , __magic_name__ : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = self.type_sequence_label_size
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = BeitForImageClassification(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __A ( self : Tuple , __magic_name__ : Any , __magic_name__ : Optional[Any] , __magic_name__ : Tuple , __magic_name__ : int ) -> int:
SCREAMING_SNAKE_CASE_ = self.num_labels
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation(__magic_name__ )
model.to(__magic_name__ )
model.eval()
SCREAMING_SNAKE_CASE_ = model(__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE_ = model(__magic_name__ , labels=__magic_name__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def __A ( self : str ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = config_and_inputs
SCREAMING_SNAKE_CASE_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def __A ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE_ = BeitModelTester(self )
SCREAMING_SNAKE_CASE_ = ConfigTester(self , config_class=__magic_name__ , has_text_modality=__magic_name__ , hidden_size=37 )
def __A ( self : Dict ) -> List[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="BEiT does not use inputs_embeds" )
def __A ( self : List[str] ) -> Optional[Any]:
pass
@require_torch_multi_gpu
@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def __A ( self : str ) -> List[str]:
pass
def __A ( self : List[Any] ) -> List[str]:
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(__magic_name__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__magic_name__ , nn.Linear ) )
def __A ( self : Union[str, Any] ) -> int:
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(__magic_name__ )
SCREAMING_SNAKE_CASE_ = inspect.signature(model.forward )
# 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] , __magic_name__ )
def __A ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__magic_name__ )
def __A ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
def __A ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__magic_name__ )
def __A ( self : int ) -> Optional[int]:
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : Any ) -> Tuple:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(__magic_name__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE_ = model_class(__magic_name__ )
model.gradient_checkpointing_enable()
model.to(__magic_name__ )
model.train()
SCREAMING_SNAKE_CASE_ = self._prepare_for_class(__magic_name__ , __magic_name__ , return_labels=__magic_name__ )
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ ).loss
loss.backward()
def __A ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ = _config_zero_init(__magic_name__ )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ = model_class(config=__magic_name__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def __A ( self : int ) -> Optional[int]:
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ = BeitModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def a__ ( ):
SCREAMING_SNAKE_CASE_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@cached_property
def __A ( self : List[Any] ) -> str:
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def __A ( self : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).pixel_values.to(__magic_name__ )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE_ = torch.ones((1, 196) , dtype=torch.bool ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(pixel_values=__magic_name__ , bool_masked_pos=__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , __magic_name__ , atol=1e-2 ) )
@slow
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 1_000) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([-1.2385, -1.0987, -1.0108] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 281
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE_ = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to(
__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.default_image_processor
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 21_841) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.tensor([1.6881, -0.2787, 0.5901] ).to(__magic_name__ )
self.assertTrue(torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 ) )
SCREAMING_SNAKE_CASE_ = 2_396
self.assertEqual(logits.argmax(-1 ).item() , __magic_name__ )
@slow
def __A ( self : Tuple ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE_ = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = version.parse(PIL.__version__ ) < version.parse("9.0.0" )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=__magic_name__ , )
else:
SCREAMING_SNAKE_CASE_ = torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=__magic_name__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __magic_name__ , atol=1e-4 ) )
@slow
def __A ( self : List[str] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
SCREAMING_SNAKE_CASE_ = model.to(__magic_name__ )
SCREAMING_SNAKE_CASE_ = BeitImageProcessor(do_resize=__magic_name__ , size=640 , do_center_crop=__magic_name__ )
SCREAMING_SNAKE_CASE_ = load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
SCREAMING_SNAKE_CASE_ = Image.open(ds[0]["file"] )
SCREAMING_SNAKE_CASE_ = image_processor(images=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = model(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ , target_sizes=[(500, 300)] )
SCREAMING_SNAKE_CASE_ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
SCREAMING_SNAKE_CASE_ = image_processor.post_process_semantic_segmentation(outputs=__magic_name__ )
SCREAMING_SNAKE_CASE_ = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , __magic_name__ )
| 305
| 0
|
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
A : Dict = (
"https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"
)
A : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
def a__ ( ):
SCREAMING_SNAKE_CASE_ = "https://pypi.org/pypi/diffusers/json"
SCREAMING_SNAKE_CASE_ = json.loads(request.urlopen(__UpperCamelCase ).read() )["releases"].keys()
return sorted(__UpperCamelCase , key=lambda __UpperCamelCase : version.Version(__UpperCamelCase ) )
def a__ ( ):
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__UpperCamelCase )
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = Path(__UpperCamelCase ) / "__init__.py"
if not init_path.exists():
init_path.touch()
def a__ ( __UpperCamelCase ):
init_hf_modules()
SCREAMING_SNAKE_CASE_ = Path(__UpperCamelCase ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = dynamic_module_path / "__init__.py"
if not init_path.exists():
init_path.touch()
def a__ ( __UpperCamelCase ):
with open(__UpperCamelCase , "r" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ = f.read()
# Imports of the form `import .xxx`
SCREAMING_SNAKE_CASE_ = re.findall("^\s*import\s+\.(\S+)\s*$" , __UpperCamelCase , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import" , __UpperCamelCase , flags=re.MULTILINE )
# Unique-ify
return list(set(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = [module_file]
SCREAMING_SNAKE_CASE_ = []
# Let's recurse through all relative imports
while not no_change:
SCREAMING_SNAKE_CASE_ = []
for f in files_to_check:
new_imports.extend(get_relative_imports(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = Path(__UpperCamelCase ).parent
SCREAMING_SNAKE_CASE_ = [str(module_path / m ) for m in new_imports]
SCREAMING_SNAKE_CASE_ = [f for f in new_import_files if f not in all_relative_imports]
SCREAMING_SNAKE_CASE_ = [F'''{f}.py''' for f in new_import_files]
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) == 0
all_relative_imports.extend(__UpperCamelCase )
return all_relative_imports
def a__ ( __UpperCamelCase ):
with open(__UpperCamelCase , "r" , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE_ = f.read()
# Imports of the form `import xxx`
SCREAMING_SNAKE_CASE_ = re.findall("^\s*import\s+(\S+)\s*$" , __UpperCamelCase , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall("^\s*from\s+(\S+)\s+import" , __UpperCamelCase , flags=re.MULTILINE )
# Only keep the top-level module
SCREAMING_SNAKE_CASE_ = [imp.split("." )[0] for imp in imports if not imp.startswith("." )]
# Unique-ify and test we got them all
SCREAMING_SNAKE_CASE_ = list(set(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = []
for imp in imports:
try:
importlib.import_module(__UpperCamelCase )
except ImportError:
missing_packages.append(__UpperCamelCase )
if len(__UpperCamelCase ) > 0:
raise ImportError(
"This modeling file requires the following packages that were not found in your environment: "
F'''{", ".join(__UpperCamelCase )}. Run `pip install {" ".join(__UpperCamelCase )}`''' )
return get_relative_imports(__UpperCamelCase )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = module_path.replace(os.path.sep , "." )
SCREAMING_SNAKE_CASE_ = importlib.import_module(__UpperCamelCase )
if class_name is None:
return find_pipeline_class(__UpperCamelCase )
return getattr(__UpperCamelCase , __UpperCamelCase )
def a__ ( __UpperCamelCase ):
from ..pipelines import DiffusionPipeline
SCREAMING_SNAKE_CASE_ = dict(inspect.getmembers(__UpperCamelCase , inspect.isclass ) )
SCREAMING_SNAKE_CASE_ = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __UpperCamelCase )
and cls.__module__.split("." )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
F''' {loaded_module}.''' )
SCREAMING_SNAKE_CASE_ = cls
return pipeline_class
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , ):
SCREAMING_SNAKE_CASE_ = str(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , __UpperCamelCase )
if os.path.isfile(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = module_file_or_url
SCREAMING_SNAKE_CASE_ = "local"
elif pretrained_model_name_or_path.count("/" ) == 0:
SCREAMING_SNAKE_CASE_ = get_diffusers_versions()
# cut ".dev0"
SCREAMING_SNAKE_CASE_ = "v" + ".".join(__version__.split("." )[:3] )
# retrieve github version that matches
if revision is None:
SCREAMING_SNAKE_CASE_ = latest_version if latest_version[1:] in available_versions else "main"
logger.info(F'''Defaulting to latest_version: {revision}.''' )
elif revision in available_versions:
SCREAMING_SNAKE_CASE_ = F'''v{revision}'''
elif revision == "main":
SCREAMING_SNAKE_CASE_ = revision
else:
raise ValueError(
F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
F''' {", ".join(available_versions + ["main"] )}.''' )
# community pipeline on GitHub
SCREAMING_SNAKE_CASE_ = COMMUNITY_PIPELINES_URL.format(revision=__UpperCamelCase , pipeline=__UpperCamelCase )
try:
SCREAMING_SNAKE_CASE_ = cached_download(
__UpperCamelCase , cache_dir=__UpperCamelCase , force_download=__UpperCamelCase , proxies=__UpperCamelCase , resume_download=__UpperCamelCase , local_files_only=__UpperCamelCase , use_auth_token=__UpperCamelCase , )
SCREAMING_SNAKE_CASE_ = "git"
SCREAMING_SNAKE_CASE_ = pretrained_model_name_or_path + ".py"
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
else:
try:
# Load from URL or cache if already cached
SCREAMING_SNAKE_CASE_ = hf_hub_download(
__UpperCamelCase , __UpperCamelCase , cache_dir=__UpperCamelCase , force_download=__UpperCamelCase , proxies=__UpperCamelCase , resume_download=__UpperCamelCase , local_files_only=__UpperCamelCase , use_auth_token=__UpperCamelCase , )
SCREAMING_SNAKE_CASE_ = os.path.join("local" , "--".join(pretrained_model_name_or_path.split("/" ) ) )
except EnvironmentError:
logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
raise
# Check we have all the requirements in our environment
SCREAMING_SNAKE_CASE_ = check_imports(__UpperCamelCase )
# Now we move the module inside our cached dynamic modules.
SCREAMING_SNAKE_CASE_ = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = Path(__UpperCamelCase ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__UpperCamelCase , submodule_path / module_file )
for module_needed in modules_needed:
SCREAMING_SNAKE_CASE_ = F'''{module_needed}.py'''
shutil.copy(os.path.join(__UpperCamelCase , __UpperCamelCase ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = use_auth_token
elif use_auth_token is True:
SCREAMING_SNAKE_CASE_ = HfFolder.get_token()
else:
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = model_info(__UpperCamelCase , revision=__UpperCamelCase , token=__UpperCamelCase ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
SCREAMING_SNAKE_CASE_ = submodule_path / commit_hash
SCREAMING_SNAKE_CASE_ = full_submodule + os.path.sep + commit_hash
create_dynamic_module(__UpperCamelCase )
if not (submodule_path / module_file).exists():
shutil.copy(__UpperCamelCase , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__UpperCamelCase , F'''{module_needed}.py''' , cache_dir=__UpperCamelCase , force_download=__UpperCamelCase , resume_download=__UpperCamelCase , proxies=__UpperCamelCase , use_auth_token=__UpperCamelCase , revision=__UpperCamelCase , local_files_only=__UpperCamelCase , )
return os.path.join(__UpperCamelCase , __UpperCamelCase )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , **__UpperCamelCase , ):
SCREAMING_SNAKE_CASE_ = get_cached_module_file(
__UpperCamelCase , __UpperCamelCase , cache_dir=__UpperCamelCase , force_download=__UpperCamelCase , resume_download=__UpperCamelCase , proxies=__UpperCamelCase , use_auth_token=__UpperCamelCase , revision=__UpperCamelCase , local_files_only=__UpperCamelCase , )
return get_class_in_module(__UpperCamelCase , final_module.replace(".py" , "" ) )
| 354
|
from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_name__ : str ) -> None:
SCREAMING_SNAKE_CASE_ = self._trie
for char in text:
if char not in trie:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = trie[char]
SCREAMING_SNAKE_CASE_ = True
def __A ( self : Union[str, Any] , __magic_name__ : str ) -> tuple | list:
SCREAMING_SNAKE_CASE_ = self._trie
for char in prefix:
if char in trie:
SCREAMING_SNAKE_CASE_ = trie[char]
else:
return []
return self._elements(__magic_name__ )
def __A ( self : int , __magic_name__ : dict ) -> tuple:
SCREAMING_SNAKE_CASE_ = []
for c, v in d.items():
SCREAMING_SNAKE_CASE_ = [" "] if c == END else [(c + s) for s in self._elements(__magic_name__ )]
result.extend(__magic_name__ )
return tuple(__magic_name__ )
A : Union[str, Any] = Trie()
A : Optional[int] = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
trie.insert_word(word)
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = trie.find_word(__UpperCamelCase )
return tuple(string + word for word in suffixes )
def a__ ( ):
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 305
| 0
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer
A : Optional[int] = logging.get_logger(__name__)
A : str = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
A : Union[str, Any] = {
"vocab_file": {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
"junnyu/roformer_chinese_char_small": (
"https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
),
"junnyu/roformer_chinese_char_base": (
"https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
),
"junnyu/roformer_small_discriminator": (
"https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
),
"junnyu/roformer_small_generator": (
"https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
),
}
}
A : Tuple = {
"junnyu/roformer_chinese_small": 15_36,
"junnyu/roformer_chinese_base": 15_36,
"junnyu/roformer_chinese_char_small": 5_12,
"junnyu/roformer_chinese_char_base": 5_12,
"junnyu/roformer_small_discriminator": 1_28,
"junnyu/roformer_small_generator": 1_28,
}
A : Union[str, Any] = {
"junnyu/roformer_chinese_small": {"do_lower_case": True},
"junnyu/roformer_chinese_base": {"do_lower_case": True},
"junnyu/roformer_chinese_char_small": {"do_lower_case": True},
"junnyu/roformer_chinese_char_base": {"do_lower_case": True},
"junnyu/roformer_small_discriminator": {"do_lower_case": True},
"junnyu/roformer_small_generator": {"do_lower_case": True},
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase__ = RoFormerTokenizer
def __init__( self : Union[str, Any] , __magic_name__ : Any=None , __magic_name__ : Tuple=None , __magic_name__ : List[Any]=True , __magic_name__ : Optional[Any]="[UNK]" , __magic_name__ : Dict="[SEP]" , __magic_name__ : Optional[Any]="[PAD]" , __magic_name__ : Dict="[CLS]" , __magic_name__ : Optional[int]="[MASK]" , __magic_name__ : Tuple=True , __magic_name__ : List[Any]=None , **__magic_name__ : Optional[Any] , ) -> List[Any]:
super().__init__(
__magic_name__ , tokenizer_file=__magic_name__ , do_lower_case=__magic_name__ , unk_token=__magic_name__ , sep_token=__magic_name__ , pad_token=__magic_name__ , cls_token=__magic_name__ , mask_token=__magic_name__ , tokenize_chinese_chars=__magic_name__ , strip_accents=__magic_name__ , **__magic_name__ , )
SCREAMING_SNAKE_CASE_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
pre_tok_state.get("lowercase" , __magic_name__ ) != do_lower_case
or pre_tok_state.get("strip_accents" , __magic_name__ ) != strip_accents
):
SCREAMING_SNAKE_CASE_ = getattr(__magic_name__ , pre_tok_state.pop("type" ) )
SCREAMING_SNAKE_CASE_ = do_lower_case
SCREAMING_SNAKE_CASE_ = strip_accents
SCREAMING_SNAKE_CASE_ = pre_tok_class(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = do_lower_case
def __getstate__( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE_ = self.__dict__.copy()
SCREAMING_SNAKE_CASE_ = BertPreTokenizer()
return state
def __setstate__( self : Optional[int] , __magic_name__ : int ) -> int:
SCREAMING_SNAKE_CASE_ = d
SCREAMING_SNAKE_CASE_ = self.__dict__["_tokenizer"].get_vocab()
SCREAMING_SNAKE_CASE_ = PreTokenizer.custom(JiebaPreTokenizer(__magic_name__ ) )
def __A ( self : str , __magic_name__ : str , __magic_name__ : Dict=None ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __A ( self : Any , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE_ = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self : Any , __magic_name__ : str , __magic_name__ : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE_ = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
def __A ( self : str , __magic_name__ : Any , __magic_name__ : Tuple=None , __magic_name__ : List[Any]=None , __magic_name__ : List[Any]=False , **__magic_name__ : Optional[int] , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = BertPreTokenizer()
return super().save_pretrained(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , **__magic_name__ )
| 355
|
from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name # process name
SCREAMING_SNAKE_CASE_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
SCREAMING_SNAKE_CASE_ = arrival_time
SCREAMING_SNAKE_CASE_ = burst_time # remaining burst time
SCREAMING_SNAKE_CASE_ = 0 # total time of the process wait in ready queue
SCREAMING_SNAKE_CASE_ = 0 # time from arrival time to completion time
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Tuple , __magic_name__ : int , __magic_name__ : list[int] , __magic_name__ : deque[Process] , __magic_name__ : int , ) -> None:
# total number of mlfq's queues
SCREAMING_SNAKE_CASE_ = number_of_queues
# time slice of queues that round robin algorithm applied
SCREAMING_SNAKE_CASE_ = time_slices
# unfinished process is in this ready_queue
SCREAMING_SNAKE_CASE_ = queue
# current time
SCREAMING_SNAKE_CASE_ = current_time
# finished process is in this sequence queue
SCREAMING_SNAKE_CASE_ = deque()
def __A ( self : Dict ) -> list[str]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def __A ( self : Tuple , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def __A ( self : str , __magic_name__ : deque[Process] ) -> list[int]:
return [q.burst_time for q in queue]
def __A ( self : Optional[Any] , __magic_name__ : Process ) -> int:
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def __A ( self : Optional[Any] , __magic_name__ : deque[Process] ) -> deque[Process]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of finished process
while len(__magic_name__ ) != 0:
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(__magic_name__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
SCREAMING_SNAKE_CASE_ = 0
# set the process's turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# set the completion time
SCREAMING_SNAKE_CASE_ = self.current_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def __A ( self : Any , __magic_name__ : deque[Process] , __magic_name__ : int ) -> tuple[deque[Process], deque[Process]]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(__magic_name__ ) ):
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(__magic_name__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
SCREAMING_SNAKE_CASE_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(__magic_name__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
SCREAMING_SNAKE_CASE_ = 0
# set the finish time
SCREAMING_SNAKE_CASE_ = self.current_time
# update the process' turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def __A ( self : Any ) -> deque[Process]:
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
A : Dict = Process("P1", 0, 53)
A : str = Process("P2", 0, 17)
A : List[Any] = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Dict = 3
A : Any = [17, 25]
A : Dict = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
A : Union[str, Any] = Process("P1", 0, 53)
A : Any = Process("P2", 0, 17)
A : Dict = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Optional[int] = 3
A : int = [17, 25]
A : Union[str, Any] = deque([Pa, Pa, Pa, Pa])
A : Tuple = MLFQ(number_of_queues, time_slices, queue, 0)
A : Tuple = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| 305
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Tuple = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = ["BloomTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
"BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
"BloomForCausalLM",
"BloomModel",
"BloomPreTrainedModel",
"BloomForSequenceClassification",
"BloomForTokenClassification",
"BloomForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
A : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 356
|
import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 305
| 0
|
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_text_model'''
def __init__( self : Optional[Any] , __magic_name__ : Union[str, Any]=30_522 , __magic_name__ : Tuple=768 , __magic_name__ : List[str]=12 , __magic_name__ : Optional[Any]=12 , __magic_name__ : str=3_072 , __magic_name__ : Dict="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : Optional[int]=0.1 , __magic_name__ : List[str]=512 , __magic_name__ : Any=2 , __magic_name__ : Optional[Any]=0.02 , __magic_name__ : int=1e-12 , __magic_name__ : str=0 , __magic_name__ : Optional[Any]="absolute" , __magic_name__ : Optional[Any]=True , **__magic_name__ : Tuple , ) -> Union[str, Any]:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = pad_token_id
@classmethod
def __A ( cls : Any , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_vision_model'''
def __init__( self : List[str] , __magic_name__ : int = 3 , __magic_name__ : int = 600 , __magic_name__ : float = 2.0 , __magic_name__ : float = 3.1 , __magic_name__ : int = 8 , __magic_name__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ : List[int] = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ : List[int] = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ : List[int] = [] , __magic_name__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ : float = 0.25 , __magic_name__ : str = "swish" , __magic_name__ : int = 2_560 , __magic_name__ : str = "mean" , __magic_name__ : float = 0.02 , __magic_name__ : float = 0.001 , __magic_name__ : float = 0.99 , __magic_name__ : float = 0.2 , **__magic_name__ : List[Any] , ) -> Tuple:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = width_coefficient
SCREAMING_SNAKE_CASE_ = depth_coefficient
SCREAMING_SNAKE_CASE_ = depth_divisor
SCREAMING_SNAKE_CASE_ = kernel_sizes
SCREAMING_SNAKE_CASE_ = in_channels
SCREAMING_SNAKE_CASE_ = out_channels
SCREAMING_SNAKE_CASE_ = depthwise_padding
SCREAMING_SNAKE_CASE_ = strides
SCREAMING_SNAKE_CASE_ = num_block_repeats
SCREAMING_SNAKE_CASE_ = expand_ratios
SCREAMING_SNAKE_CASE_ = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dim
SCREAMING_SNAKE_CASE_ = pooling_type
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = batch_norm_eps
SCREAMING_SNAKE_CASE_ = batch_norm_momentum
SCREAMING_SNAKE_CASE_ = drop_connect_rate
SCREAMING_SNAKE_CASE_ = sum(__magic_name__ ) * 4
@classmethod
def __A ( cls : List[str] , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Dict ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align'''
lowerCamelCase__ = True
def __init__( self : Optional[Any] , __magic_name__ : Dict=None , __magic_name__ : List[Any]=None , __magic_name__ : str=640 , __magic_name__ : Any=1.0 , __magic_name__ : Dict=0.02 , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__(**__magic_name__ )
if text_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
SCREAMING_SNAKE_CASE_ = AlignTextConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = AlignVisionConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = projection_dim
SCREAMING_SNAKE_CASE_ = temperature_init_value
SCREAMING_SNAKE_CASE_ = initializer_range
@classmethod
def __A ( cls : List[str] , __magic_name__ : AlignTextConfig , __magic_name__ : AlignVisionConfig , **__magic_name__ : Tuple ) -> Any:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__magic_name__ )
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.text_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 357
|
from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "08x" )[-8:]
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8" )
return little_endian_hex
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = b""
for char in message:
bit_string += format(__UpperCamelCase , "08b" ).encode("utf-8" )
SCREAMING_SNAKE_CASE_ = format(len(__UpperCamelCase ) , "064b" ).encode("utf-8" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__UpperCamelCase ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) % 5_1_2 != 0:
raise ValueError("Input must have length that's a multiple of 512" )
for pos in range(0 , len(__UpperCamelCase ) , 5_1_2 ):
SCREAMING_SNAKE_CASE_ = bit_string[pos : pos + 5_1_2]
SCREAMING_SNAKE_CASE_ = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def a__ ( __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
SCREAMING_SNAKE_CASE_ = format(__UpperCamelCase , "032b" )
SCREAMING_SNAKE_CASE_ = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__UpperCamelCase , 2 )
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return (a + b) % 2**3_2
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if i < 0:
raise ValueError("Input must be non-negative" )
if shift < 0:
raise ValueError("Shift must be non-negative" )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = preprocess(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
SCREAMING_SNAKE_CASE_ = 0X67452301
SCREAMING_SNAKE_CASE_ = 0Xefcdab89
SCREAMING_SNAKE_CASE_ = 0X98badcfe
SCREAMING_SNAKE_CASE_ = 0X10325476
SCREAMING_SNAKE_CASE_ = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = aa
SCREAMING_SNAKE_CASE_ = ba
SCREAMING_SNAKE_CASE_ = ca
SCREAMING_SNAKE_CASE_ = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = d ^ (b & (c ^ d))
SCREAMING_SNAKE_CASE_ = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
SCREAMING_SNAKE_CASE_ = c ^ (d & (b ^ c))
SCREAMING_SNAKE_CASE_ = (5 * i + 1) % 1_6
elif i <= 4_7:
SCREAMING_SNAKE_CASE_ = b ^ c ^ d
SCREAMING_SNAKE_CASE_ = (3 * i + 5) % 1_6
else:
SCREAMING_SNAKE_CASE_ = c ^ (b | not_aa(__UpperCamelCase ))
SCREAMING_SNAKE_CASE_ = (7 * i) % 1_6
SCREAMING_SNAKE_CASE_ = (f + a + added_consts[i] + block_words[g]) % 2**3_2
SCREAMING_SNAKE_CASE_ = d
SCREAMING_SNAKE_CASE_ = c
SCREAMING_SNAKE_CASE_ = b
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , left_rotate_aa(__UpperCamelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = sum_aa(__UpperCamelCase , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase ) + reformat_hex(__UpperCamelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
| 0
|
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
A : List[str] = logging.getLogger(__name__)
A : Dict = "pytorch_model.bin"
@dataclasses.dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = dataclasses.field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
lowerCamelCase__ = dataclasses.field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''} , )
@dataclasses.dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''} )
lowerCamelCase__ = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''} )
lowerCamelCase__ = dataclasses.field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
lowerCamelCase__ = dataclasses.field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''The name of the task to train on.'''} , )
lowerCamelCase__ = dataclasses.field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''The list of labels for the task.'''} )
@dataclasses.dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = dataclasses.field(
metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''} )
lowerCamelCase__ = dataclasses.field(
default='''accuracy''' , metadata={'''help''': '''The evaluation metric used for the task.'''} )
lowerCamelCase__ = dataclasses.field(
default='''no''' , metadata={
'''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
} , )
lowerCamelCase__ = dataclasses.field(
default=1_0 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
lowerCamelCase__ = dataclasses.field(
default=0.0 , metadata={
'''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
} , )
lowerCamelCase__ = dataclasses.field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} , )
lowerCamelCase__ = dataclasses.field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} , )
lowerCamelCase__ = dataclasses.field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} , )
lowerCamelCase__ = dataclasses.field(
default=0.0 , metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} , )
lowerCamelCase__ = dataclasses.field(
default=1_0_0 , metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} , )
lowerCamelCase__ = dataclasses.field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Random seed for initialization.'''} , )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
SCREAMING_SNAKE_CASE_ = dataset.filter(lambda __UpperCamelCase : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
SCREAMING_SNAKE_CASE_ = int(eval_result * len(__UpperCamelCase ) )
print(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = dataset.sort("probability" , reverse=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = dataset.select(range(__UpperCamelCase ) )
SCREAMING_SNAKE_CASE_ = dataset.remove_columns(["label", "probability"] )
SCREAMING_SNAKE_CASE_ = dataset.rename_column("prediction" , "label" )
SCREAMING_SNAKE_CASE_ = dataset.map(lambda __UpperCamelCase : {"label": idalabel[example["label"]]} )
SCREAMING_SNAKE_CASE_ = dataset.shuffle(seed=args.seed )
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , F'''train_pseudo.{args.data_file_extension}''' )
if args.data_file_extension == "csv":
dataset.to_csv(__UpperCamelCase , index=__UpperCamelCase )
else:
dataset.to_json(__UpperCamelCase )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
SCREAMING_SNAKE_CASE_ = STModelArguments(model_name_or_path=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = STDataArguments(train_file=__UpperCamelCase , infer_file=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = STTrainingArguments(output_dir=__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(__UpperCamelCase ).items():
setattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
for key, value in kwargs.items():
if hasattr(__UpperCamelCase , __UpperCamelCase ):
setattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Sanity checks
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
SCREAMING_SNAKE_CASE_ = args.train_file
SCREAMING_SNAKE_CASE_ = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
SCREAMING_SNAKE_CASE_ = args.eval_file
for key in data_files:
SCREAMING_SNAKE_CASE_ = data_files[key].split("." )[-1]
assert extension in ["csv", "json"], F'''`{key}_file` should be a csv or a json file.'''
if args.data_file_extension is None:
SCREAMING_SNAKE_CASE_ = extension
else:
assert extension == args.data_file_extension, F'''`{key}_file` should be a {args.data_file_extension} file`.'''
assert (
args.eval_metric in datasets.list_metrics()
), F'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("Creating the initial data directory for self-training..." )
SCREAMING_SNAKE_CASE_ = F'''{args.output_dir}/self-train_iter-{{}}'''.format
SCREAMING_SNAKE_CASE_ = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=__UpperCamelCase )
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = False
# Show the progress bar
SCREAMING_SNAKE_CASE_ = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
SCREAMING_SNAKE_CASE_ = data_dir_format(__UpperCamelCase )
assert os.path.exists(__UpperCamelCase )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "stage-1" )
SCREAMING_SNAKE_CASE_ = {
"accelerator": accelerator,
"model_name_or_path": args.model_name_or_path,
"cache_dir": args.cache_dir,
"do_train": True,
"train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
"do_eval": True if args.eval_file is not None else False,
"eval_file": data_files["eval"],
"do_predict": True,
"infer_file": data_files["infer"],
"task_name": args.task_name,
"label_list": args.label_list,
"output_dir": current_output_dir,
"eval_metric": args.eval_metric,
"evaluation_strategy": args.evaluation_strategy,
"early_stopping_patience": args.early_stopping_patience,
"early_stopping_threshold": args.early_stopping_threshold,
"seed": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(__UpperCamelCase , __UpperCamelCase ):
arguments_dict.update({key: value} )
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "best-checkpoint" , __UpperCamelCase )
if os.path.exists(__UpperCamelCase ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1." , __UpperCamelCase , __UpperCamelCase , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 1 *****" , __UpperCamelCase )
finetune(**__UpperCamelCase )
accelerator.wait_for_everyone()
assert os.path.exists(__UpperCamelCase )
logger.info("Self-training job completed: iteration: %d, stage: 1." , __UpperCamelCase )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "best-checkpoint" )
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "stage-2" )
# Update arguments_dict
SCREAMING_SNAKE_CASE_ = model_path
SCREAMING_SNAKE_CASE_ = data_files["train"]
SCREAMING_SNAKE_CASE_ = current_output_dir
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "best-checkpoint" , __UpperCamelCase )
if os.path.exists(__UpperCamelCase ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2." , __UpperCamelCase , __UpperCamelCase , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 2 *****" , __UpperCamelCase )
finetune(**__UpperCamelCase )
accelerator.wait_for_everyone()
assert os.path.exists(__UpperCamelCase )
logger.info("Self-training job completed: iteration: %d, stage: 2." , __UpperCamelCase )
SCREAMING_SNAKE_CASE_ = iteration
SCREAMING_SNAKE_CASE_ = data_dir_format(iteration + 1 )
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(os.path.join(__UpperCamelCase , "best-checkpoint" ) )
SCREAMING_SNAKE_CASE_ = config.idalabel
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "eval_results_best-checkpoint.json" )
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "test_results_best-checkpoint.json" )
assert os.path.exists(__UpperCamelCase )
with open(__UpperCamelCase , "r" ) as f:
SCREAMING_SNAKE_CASE_ = float(json.load(__UpperCamelCase )[args.eval_metric] )
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , "infer_output_best-checkpoint.csv" )
assert os.path.exists(__UpperCamelCase )
# Loading the dataset from local csv or json files.
SCREAMING_SNAKE_CASE_ = load_dataset(args.data_file_extension , data_files={"data": data_files["infer"]} )["data"]
SCREAMING_SNAKE_CASE_ = load_dataset("csv" , data_files={"data": infer_output_file} )["data"]
if accelerator.is_main_process:
os.makedirs(__UpperCamelCase , exist_ok=__UpperCamelCase )
shutil.copy(__UpperCamelCase , os.path.join(__UpperCamelCase , F'''eval_results_iter-{iteration}.json''' ) )
if os.path.exists(__UpperCamelCase ):
shutil.copy(__UpperCamelCase , os.path.join(__UpperCamelCase , F'''test_results_iter-{iteration}.json''' ) )
create_pseudo_labeled_data(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE_ = os.path.join(__UpperCamelCase , F'''train_pseudo.{args.data_file_extension}''' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
SCREAMING_SNAKE_CASE_ = eval_result
if best_iteration is None:
SCREAMING_SNAKE_CASE_ = new_iteration
SCREAMING_SNAKE_CASE_ = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
SCREAMING_SNAKE_CASE_ = new_iteration
SCREAMING_SNAKE_CASE_ = new_eval_result
SCREAMING_SNAKE_CASE_ = 0
else:
if new_eval_result == best_eval_result:
SCREAMING_SNAKE_CASE_ = new_iteration
SCREAMING_SNAKE_CASE_ = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
SCREAMING_SNAKE_CASE_ = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("Best iteration: %d" , __UpperCamelCase )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , __UpperCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__UpperCamelCase , F'''eval_results_iter-{iteration}.json''' ) , os.path.join(__UpperCamelCase , "eval_results_best-iteration.json" ) , )
else:
# Assume that the last iteration is the best
logger.info("Best iteration: %d" , args.max_selftrain_iterations - 1 )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , __UpperCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__UpperCamelCase , F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json''' ) , os.path.join(__UpperCamelCase , "eval_results_best-iteration.json" ) , )
| 358
|
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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : int ) -> Any:
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = BlipImageProcessor()
SCREAMING_SNAKE_CASE_ = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )
SCREAMING_SNAKE_CASE_ = BlipaProcessor(__magic_name__ , __magic_name__ )
processor.save_pretrained(self.tmpdirname )
def __A ( self : str , **__magic_name__ : int ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).tokenizer
def __A ( self : Dict , **__magic_name__ : List[Any] ) -> int:
return AutoProcessor.from_pretrained(self.tmpdirname , **__magic_name__ ).image_processor
def __A ( self : int ) -> Any:
shutil.rmtree(self.tmpdirname )
def __A ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ = [Image.fromarray(np.moveaxis(__magic_name__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE_ = self.get_image_processor(do_normalize=__magic_name__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE_ = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__magic_name__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __magic_name__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __magic_name__ )
def __A ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = image_processor(__magic_name__ , return_tensors="np" )
SCREAMING_SNAKE_CASE_ = processor(images=__magic_name__ , 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 : str ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer(__magic_name__ , return_token_type_ids=__magic_name__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(__magic_name__ ):
processor()
def __A ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ = processor.batch_decode(__magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
def __A ( self : List[str] ) -> int:
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BlipaProcessor(tokenizer=__magic_name__ , image_processor=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "lower newer"
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=__magic_name__ , images=__magic_name__ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 305
| 0
|
import requests
from bsa import BeautifulSoup
def a__ ( __UpperCamelCase = "AAPL" ):
SCREAMING_SNAKE_CASE_ = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
SCREAMING_SNAKE_CASE_ = BeautifulSoup(requests.get(__UpperCamelCase ).text , "html.parser" )
SCREAMING_SNAKE_CASE_ = "My(6px) Pos(r) smartphone_Mt(6px)"
return soup.find("div" , class_=class_ ).find("span" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f"Current {symbol:<4} stock price is {stock_price(symbol):>8}")
| 359
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ["DPTFeatureExtractor"]
A : str = ["DPTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[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
A : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 305
| 0
|
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = generate_pascal_triangle(__UpperCamelCase )
for row_idx in range(__UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=" " )
# Print row values
for col_idx in range(row_idx + 1 ):
if col_idx != row_idx:
print(triangle[row_idx][col_idx] , end=" " )
else:
print(triangle[row_idx][col_idx] , end="" )
print()
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("The input value of 'num_rows' should be 'int'" )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
"The input value of 'num_rows' should be greater than or equal to 0" )
SCREAMING_SNAKE_CASE_ = []
for current_row_idx in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = populate_current_row(__UpperCamelCase , __UpperCamelCase )
triangle.append(__UpperCamelCase )
return triangle
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [-1] * (current_row_idx + 1)
# first and last elements of current row are equal to 1
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 1, 1
for current_col_idx in range(1 , __UpperCamelCase ):
calculate_current_element(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
return current_row
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
SCREAMING_SNAKE_CASE_ = triangle[current_row_idx - 1][current_col_idx - 1]
SCREAMING_SNAKE_CASE_ = triangle[current_row_idx - 1][current_col_idx]
SCREAMING_SNAKE_CASE_ = above_to_left_elt + above_to_right_elt
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("The input value of 'num_rows' should be 'int'" )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
"The input value of 'num_rows' should be greater than or equal to 0" )
SCREAMING_SNAKE_CASE_ = [[1]]
for row_index in range(1 , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] + result[-1] + [0]
SCREAMING_SNAKE_CASE_ = row_index + 1
# Calculate the number of distinct elements in a row
SCREAMING_SNAKE_CASE_ = sum(divmod(__UpperCamelCase , 2 ) )
SCREAMING_SNAKE_CASE_ = [
temp_row[i - 1] + temp_row[i] for i in range(1 , distinct_elements + 1 )
]
SCREAMING_SNAKE_CASE_ = row_first_half[: (row_index + 1) // 2]
row_second_half.reverse()
SCREAMING_SNAKE_CASE_ = row_first_half + row_second_half
result.append(__UpperCamelCase )
return result
def a__ ( ):
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(__UpperCamelCase , __UpperCamelCase ) -> None:
SCREAMING_SNAKE_CASE_ = F'''{func.__name__}({value})'''
SCREAMING_SNAKE_CASE_ = timeit(F'''__main__.{call}''' , setup="import __main__" )
# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
print(F'''{call:38} -- {timing:.4f} seconds''' )
for value in range(1_5 ): # (1, 7, 14):
for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
benchmark_a_function(__UpperCamelCase , __UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 360
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : str = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
A : int = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Union[str, Any] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open("anagrams.txt", "w") as file:
file.write("all_anagrams = \n ")
file.write(pprint.pformat(all_anagrams))
| 305
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
"SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"SwinForImageClassification",
"SwinForMaskedImageModeling",
"SwinModel",
"SwinPreTrainedModel",
"SwinBackbone",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
"TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFSwinForImageClassification",
"TFSwinForMaskedImageModeling",
"TFSwinModel",
"TFSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 361
|
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_text_model'''
def __init__( self : Optional[Any] , __magic_name__ : Union[str, Any]=30_522 , __magic_name__ : Tuple=768 , __magic_name__ : List[str]=12 , __magic_name__ : Optional[Any]=12 , __magic_name__ : str=3_072 , __magic_name__ : Dict="gelu" , __magic_name__ : Any=0.1 , __magic_name__ : Optional[int]=0.1 , __magic_name__ : List[str]=512 , __magic_name__ : Any=2 , __magic_name__ : Optional[Any]=0.02 , __magic_name__ : int=1e-12 , __magic_name__ : str=0 , __magic_name__ : Optional[Any]="absolute" , __magic_name__ : Optional[Any]=True , **__magic_name__ : Tuple , ) -> Union[str, Any]:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = use_cache
SCREAMING_SNAKE_CASE_ = pad_token_id
@classmethod
def __A ( cls : Any , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align_vision_model'''
def __init__( self : List[str] , __magic_name__ : int = 3 , __magic_name__ : int = 600 , __magic_name__ : float = 2.0 , __magic_name__ : float = 3.1 , __magic_name__ : int = 8 , __magic_name__ : List[int] = [3, 3, 5, 3, 5, 5, 3] , __magic_name__ : List[int] = [32, 16, 24, 40, 80, 112, 192] , __magic_name__ : List[int] = [16, 24, 40, 80, 112, 192, 320] , __magic_name__ : List[int] = [] , __magic_name__ : List[int] = [1, 2, 2, 2, 1, 2, 1] , __magic_name__ : List[int] = [1, 2, 2, 3, 3, 4, 1] , __magic_name__ : List[int] = [1, 6, 6, 6, 6, 6, 6] , __magic_name__ : float = 0.25 , __magic_name__ : str = "swish" , __magic_name__ : int = 2_560 , __magic_name__ : str = "mean" , __magic_name__ : float = 0.02 , __magic_name__ : float = 0.001 , __magic_name__ : float = 0.99 , __magic_name__ : float = 0.2 , **__magic_name__ : List[Any] , ) -> Tuple:
super().__init__(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = num_channels
SCREAMING_SNAKE_CASE_ = image_size
SCREAMING_SNAKE_CASE_ = width_coefficient
SCREAMING_SNAKE_CASE_ = depth_coefficient
SCREAMING_SNAKE_CASE_ = depth_divisor
SCREAMING_SNAKE_CASE_ = kernel_sizes
SCREAMING_SNAKE_CASE_ = in_channels
SCREAMING_SNAKE_CASE_ = out_channels
SCREAMING_SNAKE_CASE_ = depthwise_padding
SCREAMING_SNAKE_CASE_ = strides
SCREAMING_SNAKE_CASE_ = num_block_repeats
SCREAMING_SNAKE_CASE_ = expand_ratios
SCREAMING_SNAKE_CASE_ = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dim
SCREAMING_SNAKE_CASE_ = pooling_type
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = batch_norm_eps
SCREAMING_SNAKE_CASE_ = batch_norm_momentum
SCREAMING_SNAKE_CASE_ = drop_connect_rate
SCREAMING_SNAKE_CASE_ = sum(__magic_name__ ) * 4
@classmethod
def __A ( cls : List[str] , __magic_name__ : Union[str, os.PathLike] , **__magic_name__ : Dict ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__magic_name__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = cls.get_config_dict(__magic_name__ , **__magic_name__ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
SCREAMING_SNAKE_CASE_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__magic_name__ , **__magic_name__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''align'''
lowerCamelCase__ = True
def __init__( self : Optional[Any] , __magic_name__ : Dict=None , __magic_name__ : List[Any]=None , __magic_name__ : str=640 , __magic_name__ : Any=1.0 , __magic_name__ : Dict=0.02 , **__magic_name__ : Union[str, Any] , ) -> int:
super().__init__(**__magic_name__ )
if text_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
SCREAMING_SNAKE_CASE_ = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
SCREAMING_SNAKE_CASE_ = AlignTextConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = AlignVisionConfig(**__magic_name__ )
SCREAMING_SNAKE_CASE_ = projection_dim
SCREAMING_SNAKE_CASE_ = temperature_init_value
SCREAMING_SNAKE_CASE_ = initializer_range
@classmethod
def __A ( cls : List[str] , __magic_name__ : AlignTextConfig , __magic_name__ : AlignVisionConfig , **__magic_name__ : Tuple ) -> Any:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__magic_name__ )
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE_ = self.text_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE_ = self.__class__.model_type
return output
| 305
| 0
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.