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"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ): # picklable for multiprocessing
return x.sum()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ): # picklable for multiprocessing
return i + 1
@dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int
SCREAMING_SNAKE_CASE_ : str
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = {}
_lowercase : Any = []
_lowercase : Dict = 1
_lowercase : Optional[int] = [1, 2]
_lowercase : int = {"""a""": 1, """b""": 2}
_lowercase : Tuple = {"""a""": [1, 2], """b""": [3, 4]}
_lowercase : str = {"""a""": {"""1""": 1}, """b""": 2}
_lowercase : List[Any] = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
_lowercase : Any = {}
_lowercase : Dict = []
_lowercase : List[str] = 2
_lowercase : Union[str, Any] = [2, 3]
_lowercase : int = {"""a""": 2, """b""": 3}
_lowercase : Any = {"""a""": [2, 3], """b""": [4, 5]}
_lowercase : Union[str, Any] = {"""a""": {"""1""": 2}, """b""": 3}
_lowercase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ) ,UpperCAmelCase_ )
_lowercase : List[str] = 2
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
_lowercase : Tuple = {"""a""": np.eye(2 ), """b""": np.zeros(3 ), """c""": np.ones(2 )}
_lowercase : List[Any] = {"""a""": 2, """b""": 0, """c""": 2}
_lowercase : Optional[int] = {
"""a""": np.eye(2 ).astype(UpperCAmelCase_ ),
"""b""": np.zeros(3 ).astype(UpperCAmelCase_ ),
"""c""": np.ones(2 ).astype(UpperCAmelCase_ ),
}
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,map_numpy=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,map_numpy=UpperCAmelCase_ ).items()} ,{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,)
self.assertEqual(map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,map_numpy=UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCAmelCase_ ,UpperCAmelCase_ ,map_numpy=UpperCAmelCase_ ,num_proc=UpperCAmelCase_ ).items()} ,{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,)
with self.assertRaises(UpperCAmelCase_ ): # can't pickle a local lambda
map_nested(lambda UpperCAmelCase_ : x + 1 ,UpperCAmelCase_ ,num_proc=UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Dict = {"""a""": 1, """b""": 2}
_lowercase : Any = {"""a""": 3, """b""": 4}
_lowercase : Optional[Any] = {"""a""": 5, """b""": 6}
_lowercase : Any = sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ) ) ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = "bar"
_lowercase : Union[str, Any] = Foo()
self.assertEqual(foo.my_attr ,"""bar""" )
with temporary_assignment(UpperCAmelCase_ ,"""my_attr""" ,"""BAR""" ):
self.assertEqual(foo.my_attr ,"""BAR""" )
self.assertEqual(foo.my_attr ,"""bar""" )
@pytest.mark.parametrize(
"""iterable_length, num_proc, expected_num_proc""" , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
with patch("""datasets.utils.py_utils._single_map_nested""" ) as mock_single_map_nested, patch(
"""datasets.parallel.parallel.Pool""" ) as mock_multiprocessing_pool:
_lowercase : List[Any] = {F"""{i}""": i for i in range(__UpperCAmelCase )}
_lowercase : Optional[Any] = map_nested(lambda __UpperCAmelCase : x + 10 , __UpperCAmelCase , num_proc=__UpperCAmelCase , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class UpperCamelCase ( snake_case ):
"""simple docstring"""
@require_tf
def lowerCamelCase__ ( self ):
import tensorflow as tf
from tensorflow.keras import layers
_lowercase : Optional[Any] = layers.Dense(2 )
def gen_random_output():
_lowercase : str = tf.random.uniform((1, 3) )
return model(UpperCAmelCase_ ).numpy()
with temp_seed(42 ,set_tensorflow=UpperCAmelCase_ ):
_lowercase : List[str] = gen_random_output()
with temp_seed(42 ,set_tensorflow=UpperCAmelCase_ ):
_lowercase : Any = gen_random_output()
_lowercase : str = gen_random_output()
np.testing.assert_equal(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertGreater(np.abs(outa - outa ).sum() ,0 )
@require_torch
def lowerCamelCase__ ( self ):
import torch
def gen_random_output():
_lowercase : Any = torch.nn.Linear(3 ,2 )
_lowercase : str = torch.rand(1 ,3 )
return model(UpperCAmelCase_ ).detach().numpy()
with temp_seed(42 ,set_pytorch=UpperCAmelCase_ ):
_lowercase : Optional[int] = gen_random_output()
with temp_seed(42 ,set_pytorch=UpperCAmelCase_ ):
_lowercase : List[Any] = gen_random_output()
_lowercase : List[str] = gen_random_output()
np.testing.assert_equal(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertGreater(np.abs(outa - outa ).sum() ,0 )
def lowerCamelCase__ ( self ):
def gen_random_output():
return np.random.rand(1 ,3 )
with temp_seed(42 ):
_lowercase : Dict = gen_random_output()
with temp_seed(42 ):
_lowercase : Optional[Any] = gen_random_output()
_lowercase : Any = gen_random_output()
np.testing.assert_equal(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertGreater(np.abs(outa - outa ).sum() ,0 )
@pytest.mark.parametrize("""input_data""" , [{}] )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Dict = NestedDataStructure(__UpperCAmelCase ).data
assert output_data == input_data
@pytest.mark.parametrize(
"""data, expected_output""" , [
({}, []),
([], []),
("""foo""", ["""foo"""]),
(["""foo""", """bar"""], ["""foo""", """bar"""]),
([["""foo""", """bar"""]], ["""foo""", """bar"""]),
([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
({"""a""": 1, """b""": 2}, [1, 2]),
({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
] , )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Dict = NestedDataStructure(__UpperCAmelCase ).flatten()
assert output == expected_output
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : int = A(x=1 , y="""foobar""" )
_lowercase : Optional[int] = {"""x""": 1, """y""": """foobar"""}
assert asdict(__UpperCAmelCase ) == expected_output
_lowercase : Optional[Any] = {"""a""": {"""b""": A(x=10 , y="""foo""" )}, """c""": [A(x=20 , y="""bar""" )]}
_lowercase : Optional[Any] = {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
assert asdict(__UpperCAmelCase ) == expected_output
with pytest.raises(__UpperCAmelCase ):
asdict([1, A(x=10 , y="""foo""" )] )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return text.split()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def __SCREAMING_SNAKE_CASE ( ):
with Pool(2 ) as pool:
_lowercase : Dict = list(iflatmap_unordered(__UpperCAmelCase , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
assert out.count("""hello""" ) == 10
assert out.count("""there""" ) == 10
assert len(__UpperCAmelCase ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
_lowercase : Optional[int] = list(iflatmap_unordered(__UpperCAmelCase , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
assert out.count("""hello""" ) == 10
assert out.count("""there""" ) == 10
assert len(__UpperCAmelCase ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
_lowercase : Any = []
for yield_time, content in iflatmap_unordered(
__UpperCAmelCase , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(__UpperCAmelCase )
assert out.count("""a""" ) == 2
assert out.count("""b""" ) == 2
assert len(__UpperCAmelCase ) == 4
| 336 |
"""simple docstring"""
import pprint
import requests
UpperCAmelCase: Tuple = """https://zenquotes.io/api"""
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
UpperCAmelCase: int = random_quotes()
pprint.pprint(response)
| 336 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase ( snake_case ):
"""simple docstring"""
@staticmethod
@abstractmethod
def lowerCamelCase__ ( UpperCAmelCase_ ):
raise NotImplementedError()
@abstractmethod
def lowerCamelCase__ ( self ):
raise NotImplementedError()
| 336 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : int
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(__UpperCAmelCase ) )]
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
_lowercase : Tuple = all_rotations(__UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_lowercase : BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(__UpperCAmelCase ),
}
return response
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
_lowercase : Optional[Any] = int(__UpperCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(__UpperCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
_lowercase : int = [""""""] * len(__UpperCAmelCase )
for _ in range(len(__UpperCAmelCase ) ):
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = """Provide a string that I will generate its BWT transform: """
UpperCAmelCase: int = input(entry_msg).strip()
UpperCAmelCase: List[str] = bwt_transform(s)
print(
F'Burrows Wheeler transform for string \'{s}\' results '
F'in \'{result["bwt_string"]}\''
)
UpperCAmelCase: Union[str, Any] = reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
print(
F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
F'we get original string \'{original_string}\''
)
| 336 | 1 |
"""simple docstring"""
import os
import string
import sys
UpperCAmelCase: Dict = 1 << 8
UpperCAmelCase: List[str] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""left""": 68 + ARROW_KEY_FLAG,
"""mod_int""": 91,
"""undefined""": sys.maxsize,
"""interrupt""": 3,
"""insert""": 50,
"""delete""": 51,
"""pg_up""": 53,
"""pg_down""": 54,
}
UpperCAmelCase: List[Any] = KEYMAP["""up"""]
UpperCAmelCase: List[Any] = KEYMAP["""left"""]
if sys.platform == "win32":
UpperCAmelCase: Any = []
UpperCAmelCase: Tuple = {
b"""\xe0H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\x00H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
b"""\xe0P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\x00P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
b"""\xe0M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\x00M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
b"""\xe0K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
b"""\x00K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
}
for i in range(10):
UpperCAmelCase: Union[str, Any] = ord(str(i))
def __SCREAMING_SNAKE_CASE ( ):
if os.name == "nt":
import msvcrt
_lowercase : str = """mbcs"""
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(__UpperCAmelCase ) == 0:
# Read the keystroke
_lowercase : Optional[Any] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
_lowercase : Dict = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
_lowercase : List[Any] = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
WIN_CH_BUFFER.append(__UpperCAmelCase )
if ord(__UpperCAmelCase ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
_lowercase : Tuple = chr(KEYMAP["""esc"""] )
except KeyError:
_lowercase : Union[str, Any] = cha[1]
else:
_lowercase : List[str] = ch.decode(__UpperCAmelCase )
else:
_lowercase : Optional[Any] = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
_lowercase : List[str] = sys.stdin.fileno()
_lowercase : List[str] = termios.tcgetattr(__UpperCAmelCase )
try:
tty.setraw(__UpperCAmelCase )
_lowercase : List[str] = sys.stdin.read(1 )
finally:
termios.tcsetattr(__UpperCAmelCase , termios.TCSADRAIN , __UpperCAmelCase )
return ch
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Any = get_raw_chars()
if ord(__UpperCAmelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(__UpperCAmelCase ) == KEYMAP["esc"]:
_lowercase : int = get_raw_chars()
if ord(__UpperCAmelCase ) == KEYMAP["mod_int"]:
_lowercase : str = get_raw_chars()
if ord(__UpperCAmelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__UpperCAmelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(__UpperCAmelCase ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 336 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = [randint(-1000 , 1000 ) for i in range(10 )]
_lowercase : Tuple = randint(-5000 , 5000 )
return (arr, r)
UpperCAmelCase: int = make_dataset()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
for triplet in permutations(__UpperCAmelCase , 3 ):
if sum(__UpperCAmelCase ) == target:
return tuple(sorted(__UpperCAmelCase ) )
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
arr.sort()
_lowercase : Optional[Any] = len(__UpperCAmelCase )
for i in range(n - 1 ):
_lowercase , _lowercase : str = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Tuple = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
_lowercase : Union[str, Any] = """
triplet_sum1(*dataset)
"""
_lowercase : Union[str, Any] = """
triplet_sum2(*dataset)
"""
_lowercase : Dict = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
_lowercase : Any = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
return (min(__UpperCAmelCase ), min(__UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase: Any = solution_times()
print(F'The time for naive implementation is {times[0]}.')
print(F'The time for optimized implementation is {times[1]}.')
| 336 | 1 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase: Union[str, Any] = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
print("""Loading config file...""" )
def flatten_yaml_as_dict(__UpperCAmelCase , __UpperCAmelCase="" , __UpperCAmelCase="." ):
_lowercase : Dict = []
for k, v in d.items():
_lowercase : str = parent_key + sep + k if parent_key else k
if isinstance(__UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(__UpperCAmelCase , __UpperCAmelCase , sep=__UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(__UpperCAmelCase )
_lowercase : Any = argparse.Namespace()
with open(__UpperCAmelCase , """r""" ) as yaml_file:
try:
_lowercase : List[str] = yaml.load(__UpperCAmelCase , Loader=yaml.FullLoader )
_lowercase : int = flatten_yaml_as_dict(__UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error("""Error while loading config file: {}. Error message: {}""".format(__UpperCAmelCase , str(__UpperCAmelCase ) ) )
return config
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : int = MobileViTVaConfig()
_lowercase : Dict = False
# dataset
if task_name.startswith("""imagenet1k_""" ):
_lowercase : str = 1000
if int(task_name.strip().split("""_""" )[-1] ) == 384:
_lowercase : Union[str, Any] = 384
else:
_lowercase : Any = 256
_lowercase : Optional[int] = """imagenet-1k-id2label.json"""
elif task_name.startswith("""imagenet21k_to_1k_""" ):
_lowercase : List[Any] = 21000
if int(task_name.strip().split("""_""" )[-1] ) == 384:
_lowercase : Optional[Any] = 384
else:
_lowercase : Tuple = 256
_lowercase : Dict = """imagenet-22k-id2label.json"""
elif task_name.startswith("""ade20k_""" ):
_lowercase : Optional[Any] = 151
_lowercase : Tuple = 512
_lowercase : Optional[int] = """ade20k-id2label.json"""
_lowercase : int = True
elif task_name.startswith("""voc_""" ):
_lowercase : List[Any] = 21
_lowercase : Tuple = 512
_lowercase : int = """pascal-voc-id2label.json"""
_lowercase : int = True
# orig_config
_lowercase : List[str] = load_orig_config_file(__UpperCAmelCase )
assert getattr(__UpperCAmelCase , """model.classification.name""" , -1 ) == "mobilevit_v2", "Invalid model"
_lowercase : Dict = getattr(__UpperCAmelCase , """model.classification.mitv2.width_multiplier""" , 1.0 )
assert (
getattr(__UpperCAmelCase , """model.classification.mitv2.attn_norm_layer""" , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
_lowercase : int = getattr(__UpperCAmelCase , """model.classification.activation.name""" , """swish""" )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
_lowercase : Optional[Any] = getattr(__UpperCAmelCase , """model.segmentation.output_stride""" , 16 )
if "_deeplabv3" in task_name:
_lowercase : Tuple = getattr(__UpperCAmelCase , """model.segmentation.deeplabv3.aspp_rates""" , [12, 24, 36] )
_lowercase : Union[str, Any] = getattr(__UpperCAmelCase , """model.segmentation.deeplabv3.aspp_out_channels""" , 512 )
_lowercase : str = getattr(__UpperCAmelCase , """model.segmentation.deeplabv3.aspp_dropout""" , 0.1 )
# id2label
_lowercase : List[str] = """huggingface/label-files"""
_lowercase : List[Any] = json.load(open(hf_hub_download(__UpperCAmelCase , __UpperCAmelCase , repo_type="""dataset""" ) , """r""" ) )
_lowercase : Optional[int] = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
_lowercase : int = idalabel
_lowercase : str = {v: k for k, v in idalabel.items()}
return config
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : List[str] = dct.pop(__UpperCAmelCase )
_lowercase : Optional[int] = val
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=False ):
if base_model:
_lowercase : Dict = """"""
else:
_lowercase : Union[str, Any] = """mobilevitv2."""
_lowercase : str = []
for k in state_dict.keys():
if k[:8] == "encoder.":
_lowercase : Any = k[8:]
else:
_lowercase : int = k
if ".block." in k:
_lowercase : List[Any] = k_new.replace(""".block.""" , """.""" )
if ".conv." in k:
_lowercase : Optional[int] = k_new.replace(""".conv.""" , """.convolution.""" )
if ".norm." in k:
_lowercase : Any = k_new.replace(""".norm.""" , """.normalization.""" )
if "conv_1." in k:
_lowercase : Any = k_new.replace("""conv_1.""" , F"""{model_prefix}conv_stem.""" )
for i in [1, 2]:
if F"""layer_{i}.""" in k:
_lowercase : int = k_new.replace(F"""layer_{i}.""" , F"""{model_prefix}encoder.layer.{i-1}.layer.""" )
if ".exp_1x1." in k:
_lowercase : str = k_new.replace(""".exp_1x1.""" , """.expand_1x1.""" )
if ".red_1x1." in k:
_lowercase : Optional[int] = k_new.replace(""".red_1x1.""" , """.reduce_1x1.""" )
for i in [3, 4, 5]:
if F"""layer_{i}.0.""" in k:
_lowercase : Any = k_new.replace(F"""layer_{i}.0.""" , F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer.""" )
if F"""layer_{i}.1.local_rep.0.""" in k:
_lowercase : Dict = k_new.replace(F"""layer_{i}.1.local_rep.0.""" , F"""{model_prefix}encoder.layer.{i-1}.conv_kxk.""" )
if F"""layer_{i}.1.local_rep.1.""" in k:
_lowercase : Tuple = k_new.replace(F"""layer_{i}.1.local_rep.1.""" , F"""{model_prefix}encoder.layer.{i-1}.conv_1x1.""" )
for i in [3, 4, 5]:
if i == 3:
_lowercase : List[Any] = [0, 1]
elif i == 4:
_lowercase : Tuple = [0, 1, 2, 3]
elif i == 5:
_lowercase : List[str] = [0, 1, 2]
for j in j_in:
if F"""layer_{i}.1.global_rep.{j}.""" in k:
_lowercase : Union[str, Any] = k_new.replace(
F"""layer_{i}.1.global_rep.{j}.""" , F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.""" )
if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
_lowercase : int = k_new.replace(
F"""layer_{i}.1.global_rep.{j+1}.""" , F"""{model_prefix}encoder.layer.{i-1}.layernorm.""" )
if F"""layer_{i}.1.conv_proj.""" in k:
_lowercase : Union[str, Any] = k_new.replace(F"""layer_{i}.1.conv_proj.""" , F"""{model_prefix}encoder.layer.{i-1}.conv_projection.""" )
if "pre_norm_attn.0." in k:
_lowercase : str = k_new.replace("""pre_norm_attn.0.""" , """layernorm_before.""" )
if "pre_norm_attn.1." in k:
_lowercase : Tuple = k_new.replace("""pre_norm_attn.1.""" , """attention.""" )
if "pre_norm_ffn.0." in k:
_lowercase : Tuple = k_new.replace("""pre_norm_ffn.0.""" , """layernorm_after.""" )
if "pre_norm_ffn.1." in k:
_lowercase : int = k_new.replace("""pre_norm_ffn.1.""" , """ffn.conv1.""" )
if "pre_norm_ffn.3." in k:
_lowercase : Union[str, Any] = k_new.replace("""pre_norm_ffn.3.""" , """ffn.conv2.""" )
if "classifier.1." in k:
_lowercase : Optional[Any] = k_new.replace("""classifier.1.""" , """classifier.""" )
if "seg_head." in k:
_lowercase : Optional[int] = k_new.replace("""seg_head.""" , """segmentation_head.""" )
if ".aspp_layer." in k:
_lowercase : Dict = k_new.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in k:
_lowercase : int = k_new.replace(""".aspp_pool.""" , """.""" )
rename_keys.append((k, k_new) )
return rename_keys
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Optional[int] = []
for k in state_dict.keys():
if k.startswith("""seg_head.aux_head.""" ):
keys_to_ignore.append(__UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(__UpperCAmelCase , __UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
_lowercase : List[Any] = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : int = get_mobilevitva_config(__UpperCAmelCase , __UpperCAmelCase )
# load original state_dict
_lowercase : Tuple = torch.load(__UpperCAmelCase , map_location="""cpu""" )
# load huggingface model
if task_name.startswith("""ade20k_""" ) or task_name.startswith("""voc_""" ):
_lowercase : Dict = MobileViTVaForSemanticSegmentation(__UpperCAmelCase ).eval()
_lowercase : Tuple = False
else:
_lowercase : int = MobileViTVaForImageClassification(__UpperCAmelCase ).eval()
_lowercase : str = False
# remove and rename some keys of load the original model
_lowercase : Optional[int] = checkpoint
remove_unused_keys(__UpperCAmelCase )
_lowercase : str = create_rename_keys(__UpperCAmelCase , base_model=__UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# load modified state_dict
model.load_state_dict(__UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
_lowercase : int = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_lowercase : List[Any] = image_processor(images=prepare_img() , return_tensors="""pt""" )
_lowercase : List[Any] = model(**__UpperCAmelCase )
# verify classification model
if task_name.startswith("""imagenet""" ):
_lowercase : Tuple = outputs.logits
_lowercase : Dict = logits.argmax(-1 ).item()
print("""Predicted class:""" , model.config.idalabel[predicted_class_idx] )
if task_name.startswith("""imagenet1k_256""" ) and config.width_multiplier == 1.0:
# expected_logits for base variant
_lowercase : Any = torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1] )
assert torch.allclose(logits[0, :3] , __UpperCAmelCase , atol=1E-4 )
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F"""Saving model {task_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 )
if __name__ == "__main__":
UpperCAmelCase: Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--task""",
default="""imagenet1k_256""",
type=str,
help=(
"""Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
"""
Classification (ImageNet-1k)
- MobileViTV2 (256x256) : imagenet1k_256
- MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
- MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
imagenet21k_to_1k_256
- MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
ImageNet-1k 384x384) : imagenet21k_to_1k_384
Segmentation
- ADE20K Dataset : ade20k_deeplabv3
- Pascal VOC 2012 Dataset: voc_deeplabv3
"""
),
choices=[
"""imagenet1k_256""",
"""imagenet1k_384""",
"""imagenet21k_to_1k_256""",
"""imagenet21k_to_1k_384""",
"""ade20k_deeplabv3""",
"""voc_deeplabv3""",
],
)
parser.add_argument(
"""--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
UpperCAmelCase: str = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE_ : Optional[int] = "BlipImageProcessor"
SCREAMING_SNAKE_CASE_ : Tuple = "AutoTokenizer"
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
super().__init__(UpperCAmelCase_ ,UpperCAmelCase_ )
# add QFormer tokenizer
_lowercase : Optional[int] = qformer_tokenizer
def __call__( self ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = True ,UpperCAmelCase_ = False ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
_lowercase : List[Any] = BatchFeature()
if text is not None:
_lowercase : List[str] = self.tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
encoding.update(UpperCAmelCase_ )
_lowercase : Dict = self.qformer_tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
_lowercase : str = qformer_text_encoding.pop("""input_ids""" )
_lowercase : int = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
_lowercase : Optional[Any] = self.image_processor(UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ )
encoding.update(UpperCAmelCase_ )
return encoding
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.tokenizer.model_input_names
_lowercase : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
if os.path.isfile(UpperCAmelCase_ ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(UpperCAmelCase_ ,exist_ok=UpperCAmelCase_ )
_lowercase : Any = os.path.join(UpperCAmelCase_ ,"""qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(UpperCAmelCase_ )
return super().save_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
_lowercase : Optional[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase_ ,subfolder="""qformer_tokenizer""" )
_lowercase : Any = cls._get_arguments_from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
args.append(UpperCAmelCase_ )
return cls(*UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase: Optional[int] = logging.get_logger(__name__)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
warnings.warn(
"""The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use PerceiverImageProcessor instead.""" ,UpperCAmelCase_ ,)
super().__init__(*UpperCAmelCase_ ,**UpperCAmelCase_ )
| 336 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase: Tuple = logging.get_logger(__name__)
UpperCAmelCase: List[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "trajectory_transformer"
SCREAMING_SNAKE_CASE_ : Optional[int] = ["past_key_values"]
SCREAMING_SNAKE_CASE_ : Tuple = {
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self ,UpperCAmelCase_=1_00 ,UpperCAmelCase_=5 ,UpperCAmelCase_=1 ,UpperCAmelCase_=1 ,UpperCAmelCase_=2_49 ,UpperCAmelCase_=6 ,UpperCAmelCase_=17 ,UpperCAmelCase_=25 ,UpperCAmelCase_=4 ,UpperCAmelCase_=4 ,UpperCAmelCase_=1_28 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.0006 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=1 ,UpperCAmelCase_=True ,UpperCAmelCase_=1 ,UpperCAmelCase_=5_02_56 ,UpperCAmelCase_=5_02_56 ,**UpperCAmelCase_ ,):
_lowercase : Dict = vocab_size
_lowercase : List[str] = action_weight
_lowercase : int = reward_weight
_lowercase : List[Any] = value_weight
_lowercase : List[str] = max_position_embeddings
_lowercase : Any = block_size
_lowercase : Any = action_dim
_lowercase : List[str] = observation_dim
_lowercase : Union[str, Any] = transition_dim
_lowercase : str = learning_rate
_lowercase : Tuple = n_layer
_lowercase : Optional[int] = n_head
_lowercase : List[str] = n_embd
_lowercase : List[str] = embd_pdrop
_lowercase : Optional[Any] = attn_pdrop
_lowercase : List[Any] = resid_pdrop
_lowercase : str = initializer_range
_lowercase : Optional[Any] = layer_norm_eps
_lowercase : List[Any] = kaiming_initializer_range
_lowercase : List[Any] = use_cache
super().__init__(pad_token_id=UpperCAmelCase_ ,bos_token_id=UpperCAmelCase_ ,eos_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(__UpperCAmelCase ):
if len(__UpperCAmelCase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(__UpperCAmelCase ) )
return data_lists
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : list[list[float]] = []
for dlist, weight in zip(__UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Dict = min(__UpperCAmelCase )
_lowercase : List[Any] = max(__UpperCAmelCase )
_lowercase : list[float] = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
_lowercase : List[str] = F"""Invalid weight of {weight:f} provided"""
raise ValueError(__UpperCAmelCase )
score_lists.append(__UpperCAmelCase )
return score_lists
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : list[float] = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(__UpperCAmelCase ):
_lowercase : List[Any] = final_scores[j] + ele
return final_scores
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Any = get_data(__UpperCAmelCase )
_lowercase : Optional[Any] = calculate_each_score(__UpperCAmelCase , __UpperCAmelCase )
_lowercase : Union[str, Any] = generate_final_scores(__UpperCAmelCase )
# append scores to source data
for i, ele in enumerate(__UpperCAmelCase ):
source_data[i].append(__UpperCAmelCase )
return source_data
| 336 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase: Any = logging.get_logger(__name__)
UpperCAmelCase: List[str] = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = "instructblip_vision_model"
def __init__( self ,UpperCAmelCase_=14_08 ,UpperCAmelCase_=61_44 ,UpperCAmelCase_=39 ,UpperCAmelCase_=16 ,UpperCAmelCase_=2_24 ,UpperCAmelCase_=14 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=1E-6 ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=1E-10 ,UpperCAmelCase_=True ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : Optional[Any] = hidden_size
_lowercase : Tuple = intermediate_size
_lowercase : List[Any] = num_hidden_layers
_lowercase : Tuple = num_attention_heads
_lowercase : Optional[Any] = patch_size
_lowercase : Optional[Any] = image_size
_lowercase : Union[str, Any] = initializer_range
_lowercase : Optional[Any] = attention_dropout
_lowercase : List[Any] = layer_norm_eps
_lowercase : Optional[int] = hidden_act
_lowercase : Tuple = qkv_bias
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : List[Any] = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : int = 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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "instructblip_qformer"
def __init__( self ,UpperCAmelCase_=3_05_22 ,UpperCAmelCase_=7_68 ,UpperCAmelCase_=12 ,UpperCAmelCase_=12 ,UpperCAmelCase_=30_72 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=0 ,UpperCAmelCase_="absolute" ,UpperCAmelCase_=2 ,UpperCAmelCase_=14_08 ,**UpperCAmelCase_ ,):
super().__init__(pad_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : List[Any] = vocab_size
_lowercase : List[Any] = hidden_size
_lowercase : str = num_hidden_layers
_lowercase : List[str] = num_attention_heads
_lowercase : Optional[Any] = hidden_act
_lowercase : int = intermediate_size
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Optional[Any] = attention_probs_dropout_prob
_lowercase : List[Any] = max_position_embeddings
_lowercase : Tuple = initializer_range
_lowercase : Optional[int] = layer_norm_eps
_lowercase : Any = position_embedding_type
_lowercase : Dict = cross_attention_frequency
_lowercase : Optional[Any] = encoder_hidden_size
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : Dict = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : str = config_dict["""qformer_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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "instructblip"
SCREAMING_SNAKE_CASE_ : List[str] = True
def __init__( self ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=32 ,**UpperCAmelCase_ ):
super().__init__(**UpperCAmelCase_ )
if vision_config is None:
_lowercase : str = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
_lowercase : Any = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
_lowercase : Optional[int] = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
_lowercase : int = InstructBlipVisionConfig(**UpperCAmelCase_ )
_lowercase : Optional[int] = InstructBlipQFormerConfig(**UpperCAmelCase_ )
_lowercase : Dict = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
_lowercase : str = CONFIG_MAPPING[text_model_type](**UpperCAmelCase_ )
_lowercase : str = self.text_config.tie_word_embeddings
_lowercase : Union[str, Any] = self.text_config.is_encoder_decoder
_lowercase : List[str] = num_query_tokens
_lowercase : List[str] = self.vision_config.hidden_size
_lowercase : Dict = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowercase : Union[str, Any] = 1.0
_lowercase : Dict = 0.02
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_lowercase : int = self.vision_config.to_dict()
_lowercase : Any = self.qformer_config.to_dict()
_lowercase : Any = self.text_config.to_dict()
_lowercase : Optional[int] = self.__class__.model_type
return output
| 336 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = field(
metadata={"help": "The output directory where the model will be written."} , )
SCREAMING_SNAKE_CASE_ : str = field(
metadata={
"help": (
"The encoder model checkpoint for weights initialization."
"Don't set if you want to train an encoder model from scratch."
)
} , )
SCREAMING_SNAKE_CASE_ : str = field(
metadata={
"help": (
"The decoder model checkpoint for weights initialization."
"Don't set if you want to train a decoder model from scratch."
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"} )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : List[Any] = HfArgumentParser((ModelArguments,) )
((_lowercase) , ) : Tuple = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
_lowercase : Tuple = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
_lowercase : Dict = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
_lowercase : Any = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
_lowercase : Tuple = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
_lowercase : str = True
_lowercase : Tuple = True
_lowercase : int = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=__UpperCAmelCase , decoder_config=__UpperCAmelCase , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
_lowercase : Union[str, Any] = decoder_config.decoder_start_token_id
_lowercase : str = decoder_config.pad_token_id
if decoder_start_token_id is None:
_lowercase : Dict = decoder_config.bos_token_id
if pad_token_id is None:
_lowercase : Union[str, Any] = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
_lowercase : List[Any] = decoder_config.eos_token_id
_lowercase : Union[str, Any] = decoder_start_token_id
_lowercase : Dict = pad_token_id
_lowercase : List[Any] = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
_lowercase : List[str] = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
_lowercase : Dict = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 336 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if k in (0.04, 0.06):
_lowercase : Optional[Any] = k
_lowercase : Optional[Any] = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ):
return str(self.k )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = cva.imread(UpperCAmelCase_ ,0 )
_lowercase , _lowercase : Dict = img.shape
_lowercase : list[list[int]] = []
_lowercase : int = img.copy()
_lowercase : List[str] = cva.cvtColor(UpperCAmelCase_ ,cva.COLOR_GRAY2RGB )
_lowercase , _lowercase : Optional[Any] = np.gradient(UpperCAmelCase_ )
_lowercase : Optional[int] = dx**2
_lowercase : Optional[Any] = dy**2
_lowercase : Optional[Any] = dx * dy
_lowercase : List[str] = 0.04
_lowercase : Optional[Any] = self.window_size // 2
for y in range(UpperCAmelCase_ ,h - offset ):
for x in range(UpperCAmelCase_ ,w - offset ):
_lowercase : Optional[Any] = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Dict = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Union[str, Any] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : int = (wxx * wyy) - (wxy**2)
_lowercase : Union[str, Any] = wxx + wyy
_lowercase : Union[str, Any] = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) ,0 )
color_img.itemset((y, x, 1) ,0 )
color_img.itemset((y, x, 2) ,2_55 )
return color_img, corner_list
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = HarrisCorner(0.04, 3)
UpperCAmelCase , UpperCAmelCase: List[Any] = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 336 | 1 |
"""simple docstring"""
UpperCAmelCase: Optional[Any] = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 336 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = LEDTokenizer
SCREAMING_SNAKE_CASE_ : List[str] = LEDTokenizerFast
SCREAMING_SNAKE_CASE_ : List[str] = True
def lowerCamelCase__ ( self ):
super().setUp()
_lowercase : Union[str, Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
_lowercase : List[Any] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
_lowercase : Dict = {"""unk_token""": """<unk>"""}
_lowercase : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
_lowercase : Any = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(UpperCAmelCase_ ,max_length=len(UpperCAmelCase_ ) ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
_lowercase : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIn("""input_ids""" ,UpperCAmelCase_ )
self.assertIn("""attention_mask""" ,UpperCAmelCase_ )
self.assertNotIn("""labels""" ,UpperCAmelCase_ )
self.assertNotIn("""decoder_attention_mask""" ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Dict = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(text_target=UpperCAmelCase_ ,max_length=32 ,padding="""max_length""" ,return_tensors="""pt""" )
self.assertEqual(32 ,targets["""input_ids"""].shape[1] )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : List[Any] = tokenizer(
["""I am a small frog""" * 10_24, """I am a small frog"""] ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = ["""A long paragraph for summarization."""]
_lowercase : Dict = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : List[str] = tokenizer(text_target=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : Union[str, Any] = inputs["""input_ids"""]
_lowercase : List[str] = targets["""input_ids"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : str = ["""Summary of the text.""", """Another summary."""]
_lowercase : Optional[int] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
_lowercase : Any = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ )
_lowercase : str = [[0] * len(UpperCAmelCase_ ) for x in encoded_output["""input_ids"""]]
_lowercase : Optional[int] = tokenizer.pad(UpperCAmelCase_ )
self.assertSequenceEqual(outputs["""global_attention_mask"""] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowercase : int = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Optional[int] = self.tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = """A, <mask> AllenNLP sentence."""
_lowercase : List[Any] = tokenizer_r.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
_lowercase : Any = tokenizer_p.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) ,sum(tokens_p["""token_type_ids"""] ) )
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) ,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) ,)
_lowercase : str = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
_lowercase : str = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
self.assertSequenceEqual(tokens_p["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
| 336 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class UpperCamelCase :
"""simple docstring"""
@staticmethod
def lowerCamelCase__ ( *UpperCAmelCase_ ,**UpperCAmelCase_ ):
pass
@is_pipeline_test
@require_vision
@require_torch
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = pipeline(
"""zero-shot-object-detection""" ,model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )
_lowercase : Optional[Any] = [
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
}
]
return object_detector, examples
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Dict = object_detector(examples[0] ,threshold=0.0 )
_lowercase : int = len(UpperCAmelCase_ )
self.assertGreater(UpperCAmelCase_ ,0 )
self.assertEqual(
UpperCAmelCase_ ,[
{
"""score""": ANY(UpperCAmelCase_ ),
"""label""": ANY(UpperCAmelCase_ ),
"""box""": {"""xmin""": ANY(UpperCAmelCase_ ), """ymin""": ANY(UpperCAmelCase_ ), """xmax""": ANY(UpperCAmelCase_ ), """ymax""": ANY(UpperCAmelCase_ )},
}
for i in range(UpperCAmelCase_ )
] ,)
@require_tf
@unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
def lowerCamelCase__ ( self ):
pass
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Any = pipeline(
"""zero-shot-object-detection""" ,model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )
_lowercase : List[Any] = object_detector(
"""./tests/fixtures/tests_samples/COCO/000000039769.png""" ,candidate_labels=["""cat""", """remote""", """couch"""] ,threshold=0.64 ,)
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[
{"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
{"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 2_74, """xmax""": 93, """ymax""": 2_97}},
{"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
] ,)
_lowercase : List[str] = object_detector(
[
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
}
] ,threshold=0.64 ,)
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[
[
{"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
{"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 2_74, """xmax""": 93, """ymax""": 2_97}},
{"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
]
] ,)
@require_torch
@slow
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = pipeline("""zero-shot-object-detection""" )
_lowercase : int = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" ,candidate_labels=["""cat""", """remote""", """couch"""] ,)
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
{"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
{"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
{"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
] ,)
_lowercase : Optional[int] = object_detector(
[
{
"""image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
},
{
"""image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
},
] ,)
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[
[
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
{"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
{"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
{"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
],
[
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
{"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
{"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
{"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
],
] ,)
@require_tf
@unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
def lowerCamelCase__ ( self ):
pass
@require_torch
@slow
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = 0.2
_lowercase : List[str] = pipeline("""zero-shot-object-detection""" )
_lowercase : Optional[Any] = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" ,candidate_labels=["""cat""", """remote""", """couch"""] ,threshold=UpperCAmelCase_ ,)
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
{"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
] ,)
@require_torch
@slow
def lowerCamelCase__ ( self ):
_lowercase : Any = 2
_lowercase : Dict = pipeline("""zero-shot-object-detection""" )
_lowercase : List[str] = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" ,candidate_labels=["""cat""", """remote""", """couch"""] ,top_k=UpperCAmelCase_ ,)
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[
{"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
] ,)
| 336 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Any = f.readlines()
_lowercase : Optional[int] = F"""class {class_name}("""
_lowercase : List[str] = F"""{4 * " "}def {test_name}("""
_lowercase : List[Any] = F"""{8 * " "}{correct_line.split()[0]}"""
_lowercase : int = F"""{16 * " "}{correct_line.split()[0]}"""
_lowercase : str = False
_lowercase : Optional[Any] = False
_lowercase : Union[str, Any] = False
_lowercase : Any = False
_lowercase : int = 0
_lowercase : Tuple = 0
_lowercase : Union[str, Any] = []
for line in lines:
if line.startswith(__UpperCAmelCase ):
_lowercase : List[str] = True
elif in_class and line.startswith(__UpperCAmelCase ):
_lowercase : str = True
elif in_class and in_func and (line.startswith(__UpperCAmelCase ) or line.startswith(__UpperCAmelCase )):
_lowercase : Union[str, Any] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
_lowercase : Optional[int] = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
_lowercase : Optional[Any] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(F"""{spaces * " "}{correct_line}""" )
_lowercase : Union[str, Any] = False
else:
new_lines.append(__UpperCAmelCase )
with open(__UpperCAmelCase , """w""" ) as f:
for line in new_lines:
f.write(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None ):
if fail is not None:
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Dict = {l.strip() for l in f.readlines()}
else:
_lowercase : int = None
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : int = f.readlines()
_lowercase : int = defaultdict(__UpperCAmelCase )
for line in correct_lines:
_lowercase , _lowercase , _lowercase , _lowercase : int = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase: List[Any] = argparse.ArgumentParser()
parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
UpperCAmelCase: Any = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 336 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase: Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase: Dict = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase: Any = {
"""vocab_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
},
"""merges_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
},
}
UpperCAmelCase: List[str] = {
"""facebook/bart-base""": 1_024,
"""facebook/bart-large""": 1_024,
"""facebook/bart-large-mnli""": 1_024,
"""facebook/bart-large-cnn""": 1_024,
"""facebook/bart-large-xsum""": 1_024,
"""yjernite/bart_eli5""": 1_024,
}
@lru_cache()
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Optional[int] = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
_lowercase : Tuple = bs[:]
_lowercase : Optional[Any] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCAmelCase )
cs.append(2**8 + n )
n += 1
_lowercase : List[str] = [chr(__UpperCAmelCase ) for n in cs]
return dict(zip(__UpperCAmelCase , __UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = set()
_lowercase : Optional[int] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowercase : int = char
return pairs
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE_ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE_ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE_ : str = ["input_ids", "attention_mask"]
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_="replace" ,UpperCAmelCase_="<s>" ,UpperCAmelCase_="</s>" ,UpperCAmelCase_="</s>" ,UpperCAmelCase_="<s>" ,UpperCAmelCase_="<unk>" ,UpperCAmelCase_="<pad>" ,UpperCAmelCase_="<mask>" ,UpperCAmelCase_=False ,**UpperCAmelCase_ ,):
_lowercase : Union[str, Any] = AddedToken(UpperCAmelCase_ ,lstrip=UpperCAmelCase_ ,rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ) else bos_token
_lowercase : List[str] = AddedToken(UpperCAmelCase_ ,lstrip=UpperCAmelCase_ ,rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ) else eos_token
_lowercase : Optional[Any] = AddedToken(UpperCAmelCase_ ,lstrip=UpperCAmelCase_ ,rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ) else sep_token
_lowercase : List[str] = AddedToken(UpperCAmelCase_ ,lstrip=UpperCAmelCase_ ,rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ) else cls_token
_lowercase : int = AddedToken(UpperCAmelCase_ ,lstrip=UpperCAmelCase_ ,rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ) else unk_token
_lowercase : Union[str, Any] = AddedToken(UpperCAmelCase_ ,lstrip=UpperCAmelCase_ ,rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowercase : int = AddedToken(UpperCAmelCase_ ,lstrip=UpperCAmelCase_ ,rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ) else mask_token
super().__init__(
errors=UpperCAmelCase_ ,bos_token=UpperCAmelCase_ ,eos_token=UpperCAmelCase_ ,unk_token=UpperCAmelCase_ ,sep_token=UpperCAmelCase_ ,cls_token=UpperCAmelCase_ ,pad_token=UpperCAmelCase_ ,mask_token=UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
with open(UpperCAmelCase_ ,encoding="""utf-8""" ) as vocab_handle:
_lowercase : Dict = json.load(UpperCAmelCase_ )
_lowercase : int = {v: k for k, v in self.encoder.items()}
_lowercase : int = errors # how to handle errors in decoding
_lowercase : Any = bytes_to_unicode()
_lowercase : Optional[int] = {v: k for k, v in self.byte_encoder.items()}
with open(UpperCAmelCase_ ,encoding="""utf-8""" ) as merges_handle:
_lowercase : List[Any] = merges_handle.read().split("""\n""" )[1:-1]
_lowercase : Any = [tuple(merge.split() ) for merge in bpe_merges]
_lowercase : Any = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Tuple = {}
_lowercase : int = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowercase : List[Any] = re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
def lowerCamelCase__ ( self ):
return len(self.encoder )
def lowerCamelCase__ ( self ):
return dict(self.encoder ,**self.added_tokens_encoder )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
if token in self.cache:
return self.cache[token]
_lowercase : Dict = tuple(UpperCAmelCase_ )
_lowercase : List[Any] = get_pairs(UpperCAmelCase_ )
if not pairs:
return token
while True:
_lowercase : List[str] = min(UpperCAmelCase_ ,key=lambda UpperCAmelCase_ : self.bpe_ranks.get(UpperCAmelCase_ ,float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
_lowercase , _lowercase : int = bigram
_lowercase : int = []
_lowercase : Optional[int] = 0
while i < len(UpperCAmelCase_ ):
try:
_lowercase : List[str] = word.index(UpperCAmelCase_ ,UpperCAmelCase_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowercase : Tuple = j
if word[i] == first and i < len(UpperCAmelCase_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowercase : Optional[int] = tuple(UpperCAmelCase_ )
_lowercase : Union[str, Any] = new_word
if len(UpperCAmelCase_ ) == 1:
break
else:
_lowercase : Dict = get_pairs(UpperCAmelCase_ )
_lowercase : List[str] = """ """.join(UpperCAmelCase_ )
_lowercase : List[str] = word
return word
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = []
for token in re.findall(self.pat ,UpperCAmelCase_ ):
_lowercase : Dict = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCAmelCase_ ).split(""" """ ) )
return bpe_tokens
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return self.encoder.get(UpperCAmelCase_ ,self.encoder.get(self.unk_token ) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return self.decoder.get(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : Dict = """""".join(UpperCAmelCase_ )
_lowercase : Optional[int] = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" ,errors=self.errors )
return text
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ):
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowercase : Union[str, Any] = os.path.join(
UpperCAmelCase_ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : Optional[int] = os.path.join(
UpperCAmelCase_ ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(UpperCAmelCase_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder ,indent=2 ,sort_keys=UpperCAmelCase_ ,ensure_ascii=UpperCAmelCase_ ) + """\n""" )
_lowercase : str = 0
with open(UpperCAmelCase_ ,"""w""" ,encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,key=lambda UpperCAmelCase_ : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
""" Please check that the tokenizer is not corrupted!""" )
_lowercase : Optional[Any] = token_index
writer.write(""" """.join(UpperCAmelCase_ ) + """\n""" )
index += 1
return vocab_file, merge_file
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowercase : str = [self.cls_token_id]
_lowercase : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase_ ,token_ids_a=UpperCAmelCase_ ,already_has_special_tokens=UpperCAmelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(UpperCAmelCase_ )) + [1]
return [1] + ([0] * len(UpperCAmelCase_ )) + [1, 1] + ([0] * len(UpperCAmelCase_ )) + [1]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ):
_lowercase : Optional[int] = [self.sep_token_id]
_lowercase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=False ,**UpperCAmelCase_ ):
_lowercase : Any = kwargs.pop("""add_prefix_space""" ,self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase_ ) > 0 and not text[0].isspace()):
_lowercase : Optional[Any] = """ """ + text
return (text, kwargs)
| 336 |
"""simple docstring"""
UpperCAmelCase: List[str] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 336 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : jnp.ndarray
SCREAMING_SNAKE_CASE_ : jnp.ndarray
class UpperCamelCase ( nn.Module ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int
SCREAMING_SNAKE_CASE_ : Tuple[int] = (1_6, 3_2, 9_6, 2_5_6)
SCREAMING_SNAKE_CASE_ : jnp.dtype = jnp.floataa
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = nn.Conv(
self.block_out_channels[0] ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
_lowercase : Optional[Any] = []
for i in range(len(self.block_out_channels ) - 1 ):
_lowercase : Tuple = self.block_out_channels[i]
_lowercase : str = self.block_out_channels[i + 1]
_lowercase : Optional[int] = nn.Conv(
UpperCAmelCase_ ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
blocks.append(UpperCAmelCase_ )
_lowercase : Optional[Any] = nn.Conv(
UpperCAmelCase_ ,kernel_size=(3, 3) ,strides=(2, 2) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
blocks.append(UpperCAmelCase_ )
_lowercase : Dict = blocks
_lowercase : Any = nn.Conv(
self.conditioning_embedding_channels ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
def __call__( self ,UpperCAmelCase_ ):
_lowercase : int = self.conv_in(UpperCAmelCase_ )
_lowercase : Optional[int] = nn.silu(UpperCAmelCase_ )
for block in self.blocks:
_lowercase : Dict = block(UpperCAmelCase_ )
_lowercase : str = nn.silu(UpperCAmelCase_ )
_lowercase : Tuple = self.conv_out(UpperCAmelCase_ )
return embedding
@flax_register_to_config
class UpperCamelCase ( nn.Module , snake_case , snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = 3_2
SCREAMING_SNAKE_CASE_ : int = 4
SCREAMING_SNAKE_CASE_ : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
SCREAMING_SNAKE_CASE_ : Union[bool, Tuple[bool]] = False
SCREAMING_SNAKE_CASE_ : Tuple[int] = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
SCREAMING_SNAKE_CASE_ : int = 2
SCREAMING_SNAKE_CASE_ : Union[int, Tuple[int]] = 8
SCREAMING_SNAKE_CASE_ : Optional[Union[int, Tuple[int]]] = None
SCREAMING_SNAKE_CASE_ : int = 1_2_8_0
SCREAMING_SNAKE_CASE_ : float = 0.0
SCREAMING_SNAKE_CASE_ : bool = False
SCREAMING_SNAKE_CASE_ : jnp.dtype = jnp.floataa
SCREAMING_SNAKE_CASE_ : bool = True
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : str = "rgb"
SCREAMING_SNAKE_CASE_ : Tuple[int] = (1_6, 3_2, 9_6, 2_5_6)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# init input tensors
_lowercase : Dict = (1, self.in_channels, self.sample_size, self.sample_size)
_lowercase : Dict = jnp.zeros(UpperCAmelCase_ ,dtype=jnp.floataa )
_lowercase : Union[str, Any] = jnp.ones((1,) ,dtype=jnp.intaa )
_lowercase : int = jnp.zeros((1, 1, self.cross_attention_dim) ,dtype=jnp.floataa )
_lowercase : Tuple = (1, 3, self.sample_size * 8, self.sample_size * 8)
_lowercase : List[str] = jnp.zeros(UpperCAmelCase_ ,dtype=jnp.floataa )
_lowercase , _lowercase : Union[str, Any] = jax.random.split(UpperCAmelCase_ )
_lowercase : List[Any] = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )["params"]
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.block_out_channels
_lowercase : List[str] = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_lowercase : Any = self.num_attention_heads or self.attention_head_dim
# input
_lowercase : Optional[int] = nn.Conv(
block_out_channels[0] ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
# time
_lowercase : Optional[int] = FlaxTimesteps(
block_out_channels[0] ,flip_sin_to_cos=self.flip_sin_to_cos ,freq_shift=self.config.freq_shift )
_lowercase : Optional[int] = FlaxTimestepEmbedding(UpperCAmelCase_ ,dtype=self.dtype )
_lowercase : List[Any] = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] ,block_out_channels=self.conditioning_embedding_out_channels ,)
_lowercase : Any = self.only_cross_attention
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : int = (only_cross_attention,) * len(self.down_block_types )
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Dict = (num_attention_heads,) * len(self.down_block_types )
# down
_lowercase : List[Any] = []
_lowercase : int = []
_lowercase : Optional[Any] = block_out_channels[0]
_lowercase : Tuple = nn.Conv(
UpperCAmelCase_ ,kernel_size=(1, 1) ,padding="""VALID""" ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(UpperCAmelCase_ )
for i, down_block_type in enumerate(self.down_block_types ):
_lowercase : List[str] = output_channel
_lowercase : Optional[Any] = block_out_channels[i]
_lowercase : Dict = i == len(UpperCAmelCase_ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_lowercase : List[Any] = FlaxCrossAttnDownBlockaD(
in_channels=UpperCAmelCase_ ,out_channels=UpperCAmelCase_ ,dropout=self.dropout ,num_layers=self.layers_per_block ,num_attention_heads=num_attention_heads[i] ,add_downsample=not is_final_block ,use_linear_projection=self.use_linear_projection ,only_cross_attention=only_cross_attention[i] ,dtype=self.dtype ,)
else:
_lowercase : int = FlaxDownBlockaD(
in_channels=UpperCAmelCase_ ,out_channels=UpperCAmelCase_ ,dropout=self.dropout ,num_layers=self.layers_per_block ,add_downsample=not is_final_block ,dtype=self.dtype ,)
down_blocks.append(UpperCAmelCase_ )
for _ in range(self.layers_per_block ):
_lowercase : str = nn.Conv(
UpperCAmelCase_ ,kernel_size=(1, 1) ,padding="""VALID""" ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(UpperCAmelCase_ )
if not is_final_block:
_lowercase : Any = nn.Conv(
UpperCAmelCase_ ,kernel_size=(1, 1) ,padding="""VALID""" ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(UpperCAmelCase_ )
_lowercase : Optional[int] = down_blocks
_lowercase : List[str] = controlnet_down_blocks
# mid
_lowercase : List[Any] = block_out_channels[-1]
_lowercase : str = FlaxUNetMidBlockaDCrossAttn(
in_channels=UpperCAmelCase_ ,dropout=self.dropout ,num_attention_heads=num_attention_heads[-1] ,use_linear_projection=self.use_linear_projection ,dtype=self.dtype ,)
_lowercase : List[Any] = nn.Conv(
UpperCAmelCase_ ,kernel_size=(1, 1) ,padding="""VALID""" ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
def __call__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = 1.0 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = False ,):
_lowercase : Optional[int] = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
_lowercase : Dict = jnp.flip(UpperCAmelCase_ ,axis=1 )
# 1. time
if not isinstance(UpperCAmelCase_ ,jnp.ndarray ):
_lowercase : Any = jnp.array([timesteps] ,dtype=jnp.intaa )
elif isinstance(UpperCAmelCase_ ,jnp.ndarray ) and len(timesteps.shape ) == 0:
_lowercase : Tuple = timesteps.astype(dtype=jnp.floataa )
_lowercase : Tuple = jnp.expand_dims(UpperCAmelCase_ ,0 )
_lowercase : str = self.time_proj(UpperCAmelCase_ )
_lowercase : str = self.time_embedding(UpperCAmelCase_ )
# 2. pre-process
_lowercase : Dict = jnp.transpose(UpperCAmelCase_ ,(0, 2, 3, 1) )
_lowercase : Optional[int] = self.conv_in(UpperCAmelCase_ )
_lowercase : Dict = jnp.transpose(UpperCAmelCase_ ,(0, 2, 3, 1) )
_lowercase : Any = self.controlnet_cond_embedding(UpperCAmelCase_ )
sample += controlnet_cond
# 3. down
_lowercase : int = (sample,)
for down_block in self.down_blocks:
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase , _lowercase : List[str] = down_block(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,deterministic=not train )
else:
_lowercase , _lowercase : Any = down_block(UpperCAmelCase_ ,UpperCAmelCase_ ,deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
_lowercase : Tuple = self.mid_block(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,deterministic=not train )
# 5. contronet blocks
_lowercase : Optional[Any] = ()
for down_block_res_sample, controlnet_block in zip(UpperCAmelCase_ ,self.controlnet_down_blocks ):
_lowercase : Dict = controlnet_block(UpperCAmelCase_ )
controlnet_down_block_res_samples += (down_block_res_sample,)
_lowercase : Any = controlnet_down_block_res_samples
_lowercase : Optional[int] = self.controlnet_mid_block(UpperCAmelCase_ )
# 6. scaling
_lowercase : str = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=UpperCAmelCase_ ,mid_block_res_sample=UpperCAmelCase_ )
| 336 |
"""simple docstring"""
UpperCAmelCase: str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCAmelCase: Any = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase: int = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 336 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase: Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase: Union[str, Any] = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = "vit_msn"
def __init__( self ,UpperCAmelCase_=7_68 ,UpperCAmelCase_=12 ,UpperCAmelCase_=12 ,UpperCAmelCase_=30_72 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-06 ,UpperCAmelCase_=2_24 ,UpperCAmelCase_=16 ,UpperCAmelCase_=3 ,UpperCAmelCase_=True ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : Union[str, Any] = hidden_size
_lowercase : Dict = num_hidden_layers
_lowercase : int = num_attention_heads
_lowercase : str = intermediate_size
_lowercase : Tuple = hidden_act
_lowercase : List[Any] = hidden_dropout_prob
_lowercase : str = attention_probs_dropout_prob
_lowercase : List[str] = initializer_range
_lowercase : List[Any] = layer_norm_eps
_lowercase : List[Any] = image_size
_lowercase : str = patch_size
_lowercase : List[Any] = num_channels
_lowercase : Any = qkv_bias
| 336 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
UpperCAmelCase: List[Any] = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
def constraint_to_multiple_of(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=0 , __UpperCAmelCase=None ):
_lowercase : Union[str, Any] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowercase : str = math.floor(val / multiple ) * multiple
if x < min_val:
_lowercase : Dict = math.ceil(val / multiple ) * multiple
return x
_lowercase : List[str] = (output_size, output_size) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else output_size
_lowercase , _lowercase : List[Any] = get_image_size(__UpperCAmelCase )
_lowercase , _lowercase : Union[str, Any] = output_size
# determine new height and width
_lowercase : str = output_height / input_height
_lowercase : List[Any] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowercase : str = scale_width
else:
# fit height
_lowercase : int = scale_height
_lowercase : List[Any] = constraint_to_multiple_of(scale_height * input_height , multiple=__UpperCAmelCase )
_lowercase : Optional[Any] = constraint_to_multiple_of(scale_width * input_width , multiple=__UpperCAmelCase )
return (new_height, new_width)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["pixel_values"]
def __init__( self ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = PILImageResampling.BILINEAR ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = 1 / 2_55 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : List[Any] = size if size is not None else {"""height""": 3_84, """width""": 3_84}
_lowercase : str = get_size_dict(UpperCAmelCase_ )
_lowercase : Tuple = do_resize
_lowercase : Any = size
_lowercase : List[Any] = keep_aspect_ratio
_lowercase : Any = ensure_multiple_of
_lowercase : str = resample
_lowercase : Optional[Any] = do_rescale
_lowercase : List[Any] = rescale_factor
_lowercase : Union[str, Any] = do_normalize
_lowercase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowercase : Any = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = PILImageResampling.BICUBIC ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
_lowercase : Optional[Any] = get_size_dict(UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_lowercase : Dict = get_resize_output_image_size(
UpperCAmelCase_ ,output_size=(size["""height"""], size["""width"""]) ,keep_aspect_ratio=UpperCAmelCase_ ,multiple=UpperCAmelCase_ ,)
return resize(UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return rescale(UpperCAmelCase_ ,scale=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return normalize(UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = ChannelDimension.FIRST ,**UpperCAmelCase_ ,):
_lowercase : Any = do_resize if do_resize is not None else self.do_resize
_lowercase : List[str] = size if size is not None else self.size
_lowercase : int = get_size_dict(UpperCAmelCase_ )
_lowercase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowercase : List[str] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowercase : List[str] = resample if resample is not None else self.resample
_lowercase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowercase : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase : str = do_normalize if do_normalize is not None else self.do_normalize
_lowercase : Optional[int] = image_mean if image_mean is not None else self.image_mean
_lowercase : int = image_std if image_std is not None else self.image_std
_lowercase : Union[str, Any] = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_lowercase : int = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
_lowercase : Union[str, Any] = [self.resize(image=UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ) for image in images]
if do_rescale:
_lowercase : int = [self.rescale(image=UpperCAmelCase_ ,scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
_lowercase : str = [self.normalize(image=UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ) for image in images]
_lowercase : Tuple = [to_channel_dimension_format(UpperCAmelCase_ ,UpperCAmelCase_ ) for image in images]
_lowercase : int = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase_ ,tensor_type=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ):
_lowercase : Union[str, Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(UpperCAmelCase_ ):
_lowercase : Tuple = target_sizes.numpy()
_lowercase : Optional[Any] = []
for idx in range(len(UpperCAmelCase_ ) ):
_lowercase : Dict = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=UpperCAmelCase_ )
_lowercase : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCAmelCase_ )
else:
_lowercase : Union[str, Any] = logits.argmax(dim=1 )
_lowercase : Optional[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 4000000 ):
_lowercase : int = [0, 1]
_lowercase : Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
_lowercase : Any = 0
for j in range(len(__UpperCAmelCase ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(F'{solution() = }')
| 336 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase: Optional[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
UpperCAmelCase: Tuple = [0, 25, 50]
UpperCAmelCase: List[Any] = [25, 50, 75]
UpperCAmelCase: Optional[int] = fuzz.membership.trimf(X, abca)
UpperCAmelCase: Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
UpperCAmelCase: List[Any] = np.ones(75)
UpperCAmelCase: Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
UpperCAmelCase: str = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
UpperCAmelCase: List[Any] = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
UpperCAmelCase: int = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
UpperCAmelCase: int = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
UpperCAmelCase: List[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
UpperCAmelCase: int = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("""Young""")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("""Middle aged""")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("""union""")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("""intersection""")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("""complement_a""")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("""difference a/b""")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("""alg_sum""")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("""alg_product""")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("""bdd_sum""")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("""bdd_difference""")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 336 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
UpperCAmelCase: Tuple = logging.get_logger(__name__)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
warnings.warn(
"""The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DeiTImageProcessor instead.""" ,UpperCAmelCase_ ,)
super().__init__(*UpperCAmelCase_ ,**UpperCAmelCase_ )
| 336 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
_lowercase : str = tempfile.mkdtemp()
# fmt: off
_lowercase : List[Any] = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
_lowercase : Optional[int] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
_lowercase : Optional[int] = {"""unk_token""": """<unk>"""}
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
_lowercase : Dict = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48145466, 0.4578275, 0.40821073],
"""image_std""": [0.26862954, 0.26130258, 0.27577711],
}
_lowercase : List[Any] = os.path.join(self.tmpdirname ,UpperCAmelCase_ )
with open(self.image_processor_file ,"""w""" ,encoding="""utf-8""" ) as fp:
json.dump(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPTokenizer.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
_lowercase : Tuple = [Image.fromarray(np.moveaxis(UpperCAmelCase_ ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.get_tokenizer()
_lowercase : List[Any] = self.get_rust_tokenizer()
_lowercase : List[Any] = self.get_image_processor()
_lowercase : List[str] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
_lowercase : Optional[int] = CLIPProcessor.from_pretrained(self.tmpdirname ,use_fast=UpperCAmelCase_ )
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
_lowercase : List[str] = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() ,tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.image_processor ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = CLIPProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_lowercase : Union[str, Any] = self.get_tokenizer(bos_token="""(BOS)""" ,eos_token="""(EOS)""" )
_lowercase : Optional[int] = self.get_image_processor(do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
_lowercase : int = CLIPProcessor.from_pretrained(
self.tmpdirname ,bos_token="""(BOS)""" ,eos_token="""(EOS)""" ,do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : Optional[int] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : int = self.prepare_image_inputs()
_lowercase : str = image_processor(UpperCAmelCase_ ,return_tensors="""np""" )
_lowercase : int = processor(images=UpperCAmelCase_ ,return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : Optional[Any] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : List[Any] = """lower newer"""
_lowercase : Any = processor(text=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer(UpperCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : str = """lower newer"""
_lowercase : List[Any] = self.prepare_image_inputs()
_lowercase : Optional[int] = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase_ ):
processor()
def lowerCamelCase__ ( self ):
_lowercase : Dict = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowercase : int = processor.batch_decode(UpperCAmelCase_ )
_lowercase : Tuple = tokenizer.batch_decode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : Optional[Any] = """lower newer"""
_lowercase : Any = self.prepare_image_inputs()
_lowercase : Optional[int] = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,processor.model_input_names )
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if exponent == 1:
return base
if exponent % 2 == 0:
_lowercase : int = _modexpt(__UpperCAmelCase , exponent // 2 , __UpperCAmelCase ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(__UpperCAmelCase , exponent - 1 , __UpperCAmelCase )) % modulo_value
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 1777 , __UpperCAmelCase = 1855 , __UpperCAmelCase = 8 ):
_lowercase : Any = base
for _ in range(1 , __UpperCAmelCase ):
_lowercase : Optional[Any] = _modexpt(__UpperCAmelCase , __UpperCAmelCase , 10**digits )
return result
if __name__ == "__main__":
print(F'{solution() = }')
| 336 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
UpperCAmelCase: str = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[datasets.Features] = None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , ):
import pyspark
def generate_fn():
_lowercase : List[Any] = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
_lowercase : Optional[int] = df_with_partition_id.select("""*""" ).where(F"""part_id = {partition_id}""" ).drop("""part_id""" )
_lowercase : int = partition_df.collect()
_lowercase : Dict = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class UpperCamelCase ( _BaseExamplesIterable ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ,):
_lowercase : Union[str, Any] = df
_lowercase : List[str] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowercase : Tuple = _generate_iterable_examples(self.df ,self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.split_shard_indices_by_worker(UpperCAmelCase_ ,UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
@property
def lowerCamelCase__ ( self ):
return len(self.partition_order )
class UpperCamelCase ( datasets.DatasetBuilder ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = SparkConfig
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
import pyspark
_lowercase : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowercase : List[Any] = df
_lowercase : int = working_dir
super().__init__(
cache_dir=UpperCAmelCase_ ,config_name=str(self.df.semanticHash() ) ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(UpperCAmelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir ,exist_ok=UpperCAmelCase_ )
_lowercase : Union[str, Any] = os.path.join(self._cache_dir ,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(UpperCAmelCase_ ,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" ,"""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowercase : List[str] = (
self._spark.sparkContext.parallelize(range(1 ) ,1 ).mapPartitions(UpperCAmelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase__ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
import pyspark
def get_arrow_batch_size(UpperCAmelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
_lowercase : List[str] = self.df.count()
_lowercase : List[str] = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowercase : Union[str, Any] = (
self.df.limit(UpperCAmelCase_ )
.repartition(1 )
.mapInArrow(UpperCAmelCase_ ,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowercase : List[Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowercase : int = min(UpperCAmelCase_ ,int(approx_total_size / max_shard_size ) )
_lowercase : List[Any] = self.df.repartition(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
import pyspark
_lowercase : Union[str, Any] = ParquetWriter if file_format == """parquet""" else ArrowWriter
_lowercase : List[Any] = os.path.join(self._working_dir ,os.path.basename(UpperCAmelCase_ ) ) if self._working_dir else fpath
_lowercase : Any = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowercase : Union[str, Any] = self.config.features
_lowercase : Optional[int] = self._writer_batch_size
_lowercase : Optional[Any] = self._fs.storage_options
def write_arrow(UpperCAmelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowercase : Any = pyspark.TaskContext().taskAttemptId()
_lowercase : List[str] = next(UpperCAmelCase_ ,UpperCAmelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
_lowercase : List[Any] = 0
_lowercase : int = writer_class(
features=UpperCAmelCase_ ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Optional[int] = pa.Table.from_batches([first_batch] )
writer.write_table(UpperCAmelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowercase , _lowercase : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
shard_id += 1
_lowercase : Union[str, Any] = writer_class(
features=writer._features ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Dict = pa.Table.from_batches([batch] )
writer.write_table(UpperCAmelCase_ )
if writer._num_bytes > 0:
_lowercase , _lowercase : Dict = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(UpperCAmelCase_ ) ):
_lowercase : Dict = os.path.join(os.path.dirname(UpperCAmelCase_ ) ,os.path.basename(UpperCAmelCase_ ) )
shutil.move(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : List[str] = (
self.df.mapInArrow(UpperCAmelCase_ ,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) ,pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) ,pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) ,pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) ,)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = "arrow" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
self._validate_cache_dir()
_lowercase : Tuple = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(UpperCAmelCase_ )
_lowercase : Optional[int] = not is_remote_filesystem(self._fs )
_lowercase : Dict = os.path.join if is_local else posixpath.join
_lowercase : int = """-TTTTT-SSSSS-of-NNNNN"""
_lowercase : Optional[Any] = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
_lowercase : Dict = path_join(self._output_dir ,UpperCAmelCase_ )
_lowercase : List[Any] = 0
_lowercase : Optional[Any] = 0
_lowercase : int = 0
_lowercase : Any = []
_lowercase : Any = []
for task_id, content in self._prepare_split_single(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : Tuple = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(UpperCAmelCase_ )
_lowercase : Optional[int] = total_num_examples
_lowercase : List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
_lowercase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowercase : Union[str, Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
rename(
UpperCAmelCase_ ,fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace("""TTTTT-SSSSS""" ,f"""{global_shard_id:05d}""" ).replace("""NNNNN""" ,f"""{total_shards:05d}""" ) ,)
_lowercase : Optional[Any] = []
_lowercase : List[str] = 0
for i in range(len(UpperCAmelCase_ ) ):
_lowercase , _lowercase : List[str] = task_id_and_num_shards[i]
for shard_id in range(UpperCAmelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(UpperCAmelCase_ ,len(UpperCAmelCase_ ) ).map(lambda UpperCAmelCase_ : _rename_shard(*UpperCAmelCase_ ) ).collect()
else:
# don't use any pattern
_lowercase : Tuple = 0
_lowercase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace(UpperCAmelCase_ ,"""""" ) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,):
return SparkExamplesIterable(self.df )
| 336 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase: Optional[int] = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase: List[Any] = [
"""INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""InformerForPrediction""",
"""InformerModel""",
"""InformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase: Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 336 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Optional[int] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = XLNetTokenizer
SCREAMING_SNAKE_CASE_ : Dict = XLNetTokenizerFast
SCREAMING_SNAKE_CASE_ : int = True
SCREAMING_SNAKE_CASE_ : Union[str, Any] = True
def lowerCamelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowercase : Dict = XLNetTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = """<s>"""
_lowercase : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<unk>""" )
self.assertEqual(vocab_keys[1] ,"""<s>""" )
self.assertEqual(vocab_keys[-1] ,"""<eod>""" )
self.assertEqual(len(UpperCAmelCase_ ) ,10_06 )
def lowerCamelCase__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size ,10_00 )
def lowerCamelCase__ ( self ):
_lowercase : int = XLNetTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCAmelCase_ ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) ,[2_85, 46, 10, 1_70, 3_82] )
_lowercase : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] ,)
_lowercase : List[Any] = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
_lowercase : Optional[Any] = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] ,)
def lowerCamelCase__ ( self ):
_lowercase : Dict = XLNetTokenizer(UpperCAmelCase_ ,do_lower_case=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """""",
"""i""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] ,)
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,["""▁he""", """ll""", """o"""] )
def lowerCamelCase__ ( self ):
_lowercase : int = XLNetTokenizer(UpperCAmelCase_ ,do_lower_case=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] ,)
@slow
def lowerCamelCase__ ( self ):
_lowercase : Dict = XLNetTokenizer.from_pretrained("""xlnet-base-cased""" )
_lowercase : int = tokenizer.encode("""sequence builders""" ,add_special_tokens=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.encode("""multi-sequence build""" ,add_special_tokens=UpperCAmelCase_ )
_lowercase : Optional[int] = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ )
_lowercase : Dict = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ ,UpperCAmelCase_ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def lowerCamelCase__ ( self ):
# fmt: off
_lowercase : Union[str, Any] = {"""input_ids""": [[17, 2_14_42, 2_70, 17, 10, 1_46_45, 3_18, 34, 17, 45_46, 31_45, 7_87, 13, 77_52, 2_20_18, 23, 21, 17, 45_46, 31_45, 7_87, 13, 33_52, 1_44_31, 13, 55_00, 11, 11_76, 5_80, 13, 1_68_19, 47_97, 23, 17, 10, 1_71_35, 6_58, 19, 4_57, 79_32, 13, 1_84, 19, 31_54, 1_71_35, 64_68, 19, 14_04, 1_22_69, 19, 42_29, 53_56, 1_62_64, 46, 19, 17, 2_05_45, 1_03_95, 9, 9, 9, 11, 28, 64_21, 95_31, 2_07_29, 17, 10, 3_53, 1_70_22, 11, 21, 64_21, 95_31, 1_69_49, 17, 10, 1_15_09, 7_53, 11, 33, 95, 24_21, 73_85, 9_56, 1_44_31, 26_26, 25, 8_42, 73_85, 48_36, 21, 14_29, 22_72, 98_55, 31_20, 1_61, 2_47_38, 19, 1_32_03, 6_58, 2_18, 7_87, 21, 4_30, 1_84_82, 8_47, 26_37, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_22, 2_21_78, 27, 10_64, 22, 9_56, 13, 1_11_01, 14_29, 58_54, 2_43_13, 1_89_53, 40, 4_22, 2_43_66, 68, 17_58, 37, 1_04_83, 1_42_57, 31, 2_07, 2_63, 21, 2_03, 37_73, 25, 71, 97_35, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 20_49, 34_42, 17, 1_38_94, 33_80, 23, 95, 18, 1_76_34, 22_88, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ ,model_name="""xlnet-base-cased""" ,revision="""c841166438c31ec7ca9a106dee7bb312b73ae511""" ,)
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : list[list[int]] = [[0 for _ in range(__UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_lowercase : str = 1
for n in range(m + 1 ):
for k in range(1 , __UpperCAmelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
UpperCAmelCase: List[str] = int(input("""Enter a number: """).strip())
print(partition(n))
except ValueError:
print("""Please enter a number.""")
else:
try:
UpperCAmelCase: Any = int(sys.argv[1])
print(partition(n))
except ValueError:
print("""Please pass a number.""")
| 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCamelCase__ ( self ):
_lowercase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
_lowercase : List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
_lowercase : Optional[Any] = """xvjiarui/stable-diffusion-2-inpainting"""
_lowercase , _lowercase : Any = FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase_ ,safety_checker=UpperCAmelCase_ )
_lowercase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
_lowercase : Optional[Any] = jax.random.PRNGKey(0 )
_lowercase : Tuple = 50
_lowercase : List[str] = jax.device_count()
_lowercase : List[str] = num_samples * [prompt]
_lowercase : List[Any] = num_samples * [init_image]
_lowercase : List[Any] = num_samples * [mask_image]
_lowercase , _lowercase , _lowercase : Dict = pipeline.prepare_inputs(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
# shard inputs and rng
_lowercase : int = replicate(UpperCAmelCase_ )
_lowercase : Optional[Any] = jax.random.split(UpperCAmelCase_ ,jax.device_count() )
_lowercase : int = shard(UpperCAmelCase_ )
_lowercase : List[str] = shard(UpperCAmelCase_ )
_lowercase : str = shard(UpperCAmelCase_ )
_lowercase : Optional[Any] = pipeline(
UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,jit=UpperCAmelCase_ )
_lowercase : Dict = output.images.reshape(UpperCAmelCase_ ,5_12 ,5_12 ,3 )
_lowercase : Optional[int] = images[0, 2_53:2_56, 2_53:2_56, -1]
_lowercase : Optional[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_lowercase : List[Any] = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 336 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : int = []
for line in lines:
_lowercase : Dict = re.sub(R"""#.*""" , """""" , __UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(__UpperCAmelCase )
_lowercase : Tuple = """\n""".join(__UpperCAmelCase )
# Make a hash from all this code
_lowercase : Tuple = full_str.encode("""utf-8""" )
return shaaaa(__UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCAmelCase: Tuple = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCAmelCase: List[str] = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase: Any = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
UpperCAmelCase: Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 336 | 1 |
"""simple docstring"""
UpperCAmelCase: List[Any] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Dict = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
number //= 100000
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
UpperCAmelCase: list[bool | None] = [None] * 10_000_000
UpperCAmelCase: int = True
UpperCAmelCase: Any = False
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
_lowercase : Any = chain(next_number(__UpperCAmelCase ) )
_lowercase : Dict = number_chain
while number < 10000000:
_lowercase : str = number_chain
number *= 10
return number_chain
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 10000000 ):
for i in range(1 , __UpperCAmelCase ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(__UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{solution() = }')
| 336 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 336 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Any = f.readlines()
_lowercase : Optional[int] = F"""class {class_name}("""
_lowercase : List[str] = F"""{4 * " "}def {test_name}("""
_lowercase : List[Any] = F"""{8 * " "}{correct_line.split()[0]}"""
_lowercase : int = F"""{16 * " "}{correct_line.split()[0]}"""
_lowercase : str = False
_lowercase : Optional[Any] = False
_lowercase : Union[str, Any] = False
_lowercase : Any = False
_lowercase : int = 0
_lowercase : Tuple = 0
_lowercase : Union[str, Any] = []
for line in lines:
if line.startswith(__UpperCAmelCase ):
_lowercase : List[str] = True
elif in_class and line.startswith(__UpperCAmelCase ):
_lowercase : str = True
elif in_class and in_func and (line.startswith(__UpperCAmelCase ) or line.startswith(__UpperCAmelCase )):
_lowercase : Union[str, Any] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
_lowercase : Optional[int] = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
_lowercase : Optional[Any] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(F"""{spaces * " "}{correct_line}""" )
_lowercase : Union[str, Any] = False
else:
new_lines.append(__UpperCAmelCase )
with open(__UpperCAmelCase , """w""" ) as f:
for line in new_lines:
f.write(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None ):
if fail is not None:
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Dict = {l.strip() for l in f.readlines()}
else:
_lowercase : int = None
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : int = f.readlines()
_lowercase : int = defaultdict(__UpperCAmelCase )
for line in correct_lines:
_lowercase , _lowercase , _lowercase , _lowercase : int = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase: List[Any] = argparse.ArgumentParser()
parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
UpperCAmelCase: Any = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCAmelCase: Any = generate_large_matrix()
UpperCAmelCase: Dict = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
assert all(row == sorted(__UpperCAmelCase , reverse=__UpperCAmelCase ) for row in grid )
assert all(list(__UpperCAmelCase ) == sorted(__UpperCAmelCase , reverse=__UpperCAmelCase ) for col in zip(*__UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = 0
_lowercase : List[Any] = len(__UpperCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
_lowercase : Tuple = (left + right) // 2
_lowercase : List[Any] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
_lowercase : Dict = mid + 1
else:
_lowercase : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Any = 0
_lowercase : Optional[int] = len(grid[0] )
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = find_negative_index(grid[i][:bound] )
total += bound
return (len(__UpperCAmelCase ) * len(grid[0] )) - total
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return len([number for row in grid for number in row if number < 0] )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = 0
for row in grid:
for i, number in enumerate(__UpperCAmelCase ):
if number < 0:
total += len(__UpperCAmelCase ) - i
break
return total
def __SCREAMING_SNAKE_CASE ( ):
from timeit import timeit
print("""Running benchmarks""" )
_lowercase : Tuple = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
_lowercase : Dict = timeit(F"""{func}(grid=grid)""" , setup=__UpperCAmelCase , number=500 )
print(F"""{func}() took {time:0.4f} seconds""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 336 | 1 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase: str = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Dict = SwinConfig.from_pretrained(
"""microsoft/swin-tiny-patch4-window7-224""" , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
_lowercase : List[str] = MaskFormerConfig(backbone_config=__UpperCAmelCase )
_lowercase : str = """huggingface/label-files"""
if "ade20k-full" in model_name:
# this should be ok
_lowercase : str = 847
_lowercase : str = """maskformer-ade20k-full-id2label.json"""
elif "ade" in model_name:
# this should be ok
_lowercase : Dict = 150
_lowercase : Union[str, Any] = """ade20k-id2label.json"""
elif "coco-stuff" in model_name:
# this should be ok
_lowercase : Dict = 171
_lowercase : str = """maskformer-coco-stuff-id2label.json"""
elif "coco" in model_name:
# TODO
_lowercase : str = 133
_lowercase : Union[str, Any] = """coco-panoptic-id2label.json"""
elif "cityscapes" in model_name:
# this should be ok
_lowercase : Dict = 19
_lowercase : Any = """cityscapes-id2label.json"""
elif "vistas" in model_name:
# this should be ok
_lowercase : str = 65
_lowercase : List[str] = """mapillary-vistas-id2label.json"""
_lowercase : Dict = json.load(open(hf_hub_download(__UpperCAmelCase , __UpperCAmelCase , repo_type="""dataset""" ) , """r""" ) )
_lowercase : Optional[int] = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
return config
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Dict = []
# stem
# fmt: off
rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") )
rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") )
# heads on top
rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Any = dct.pop(__UpperCAmelCase )
_lowercase : Any = val
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[int] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_lowercase : int = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_lowercase : List[str] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_lowercase : int = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_lowercase : Optional[int] = in_proj_weight[:dim, :]
_lowercase : Optional[Any] = in_proj_bias[: dim]
_lowercase : Tuple = in_proj_weight[
dim : dim * 2, :
]
_lowercase : List[str] = in_proj_bias[
dim : dim * 2
]
_lowercase : List[Any] = in_proj_weight[
-dim :, :
]
_lowercase : int = in_proj_bias[-dim :]
# fmt: on
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
# fmt: off
_lowercase : List[Any] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowercase : Union[str, Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_lowercase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_lowercase : Dict = in_proj_weight[: hidden_size, :]
_lowercase : Optional[Any] = in_proj_bias[:config.hidden_size]
_lowercase : Optional[int] = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowercase : Union[str, Any] = in_proj_bias[hidden_size : hidden_size * 2]
_lowercase : Any = in_proj_weight[-hidden_size :, :]
_lowercase : str = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_lowercase : Union[str, Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_lowercase : List[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_lowercase : str = in_proj_weight[: hidden_size, :]
_lowercase : str = in_proj_bias[:config.hidden_size]
_lowercase : Any = in_proj_weight[hidden_size : hidden_size * 2, :]
_lowercase : str = in_proj_bias[hidden_size : hidden_size * 2]
_lowercase : Union[str, Any] = in_proj_weight[-hidden_size :, :]
_lowercase : int = in_proj_bias[-hidden_size :]
# fmt: on
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowercase : Union[str, Any] = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = False ):
_lowercase : Union[str, Any] = get_maskformer_config(__UpperCAmelCase )
# load original state_dict
with open(__UpperCAmelCase , """rb""" ) as f:
_lowercase : Any = pickle.load(__UpperCAmelCase )
_lowercase : str = data["""model"""]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_lowercase : Optional[Any] = create_rename_keys(__UpperCAmelCase )
for src, dest in rename_keys:
rename_key(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
read_in_swin_q_k_v(__UpperCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(__UpperCAmelCase , __UpperCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_lowercase : Union[str, Any] = torch.from_numpy(__UpperCAmelCase )
# load 🤗 model
_lowercase : str = MaskFormerForInstanceSegmentation(__UpperCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(__UpperCAmelCase , param.shape )
_lowercase , _lowercase : List[str] = model.load_state_dict(__UpperCAmelCase , strict=__UpperCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(__UpperCAmelCase ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_lowercase : Tuple = prepare_img()
if "vistas" in model_name:
_lowercase : int = 65
elif "cityscapes" in model_name:
_lowercase : List[str] = 65535
else:
_lowercase : Any = 255
_lowercase : List[str] = True if """ade""" in model_name else False
_lowercase : int = MaskFormerImageProcessor(ignore_index=__UpperCAmelCase , reduce_labels=__UpperCAmelCase )
_lowercase : Optional[int] = image_processor(__UpperCAmelCase , return_tensors="""pt""" )
_lowercase : Union[str, Any] = model(**__UpperCAmelCase )
print("""Logits:""" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_lowercase : Union[str, Any] = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , __UpperCAmelCase , atol=1E-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
model.save_pretrained(__UpperCAmelCase )
image_processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
print("""Pushing model and image processor to the hub...""" )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
UpperCAmelCase: str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
UpperCAmelCase: Optional[int] = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 336 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase: List[str] = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase: int = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
re.sub("""<n>""" , """""" , __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 336 | 1 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE_ : Optional[int] = "BlipImageProcessor"
SCREAMING_SNAKE_CASE_ : Tuple = "AutoTokenizer"
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
super().__init__(UpperCAmelCase_ ,UpperCAmelCase_ )
# add QFormer tokenizer
_lowercase : Optional[int] = qformer_tokenizer
def __call__( self ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = True ,UpperCAmelCase_ = False ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
_lowercase : List[Any] = BatchFeature()
if text is not None:
_lowercase : List[str] = self.tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
encoding.update(UpperCAmelCase_ )
_lowercase : Dict = self.qformer_tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
_lowercase : str = qformer_text_encoding.pop("""input_ids""" )
_lowercase : int = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
_lowercase : Optional[Any] = self.image_processor(UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ )
encoding.update(UpperCAmelCase_ )
return encoding
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.tokenizer.model_input_names
_lowercase : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
if os.path.isfile(UpperCAmelCase_ ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(UpperCAmelCase_ ,exist_ok=UpperCAmelCase_ )
_lowercase : Any = os.path.join(UpperCAmelCase_ ,"""qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(UpperCAmelCase_ )
return super().save_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
_lowercase : Optional[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase_ ,subfolder="""qformer_tokenizer""" )
_lowercase : Any = cls._get_arguments_from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
args.append(UpperCAmelCase_ )
return cls(*UpperCAmelCase_ )
| 336 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=0.9_9_9 , __UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_lowercase : str = []
for i in range(__UpperCAmelCase ):
_lowercase : Any = i / num_diffusion_timesteps
_lowercase : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ) , __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
class UpperCamelCase ( snake_case , snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [e.name for e in KarrasDiffusionSchedulers]
SCREAMING_SNAKE_CASE_ : str = 2
@register_to_config
def __init__( self ,UpperCAmelCase_ = 10_00 ,UpperCAmelCase_ = 0.00085 ,UpperCAmelCase_ = 0.012 ,UpperCAmelCase_ = "linear" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = "epsilon" ,UpperCAmelCase_ = "linspace" ,UpperCAmelCase_ = 0 ,):
if trained_betas is not None:
_lowercase : str = torch.tensor(UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
_lowercase : Optional[Any] = torch.linspace(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_lowercase : Any = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,UpperCAmelCase_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_lowercase : Any = betas_for_alpha_bar(UpperCAmelCase_ )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
_lowercase : Tuple = 1.0 - self.betas
_lowercase : Dict = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ):
if schedule_timesteps is None:
_lowercase : Optional[int] = self.timesteps
_lowercase : Union[str, Any] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_lowercase : Optional[Any] = 1 if len(UpperCAmelCase_ ) > 1 else 0
else:
_lowercase : Dict = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
_lowercase : List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCamelCase__ ( self ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
_lowercase : str = self.index_for_timestep(UpperCAmelCase_ )
if self.state_in_first_order:
_lowercase : Optional[Any] = self.sigmas[step_index]
else:
_lowercase : Dict = self.sigmas_interpol[step_index]
_lowercase : Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,):
_lowercase : List[str] = num_inference_steps
_lowercase : Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_lowercase : Dict = np.linspace(0 ,num_train_timesteps - 1 ,UpperCAmelCase_ ,dtype=UpperCAmelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_lowercase : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : str = (np.arange(0 ,UpperCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_lowercase : str = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : Optional[int] = (np.arange(UpperCAmelCase_ ,0 ,-step_ratio )).round().copy().astype(UpperCAmelCase_ )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_lowercase : Union[str, Any] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_lowercase : Optional[Any] = torch.from_numpy(np.log(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_lowercase : List[str] = np.interp(UpperCAmelCase_ ,np.arange(0 ,len(UpperCAmelCase_ ) ) ,UpperCAmelCase_ )
_lowercase : int = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_lowercase : Any = torch.from_numpy(UpperCAmelCase_ ).to(device=UpperCAmelCase_ )
# interpolate sigmas
_lowercase : List[str] = sigmas.log().lerp(sigmas.roll(1 ).log() ,0.5 ).exp()
_lowercase : Union[str, Any] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
_lowercase : Tuple = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(UpperCAmelCase_ ).startswith("""mps""" ):
# mps does not support float64
_lowercase : Tuple = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=torch.floataa )
else:
_lowercase : str = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ )
# interpolate timesteps
_lowercase : int = self.sigma_to_t(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=timesteps.dtype )
_lowercase : Union[str, Any] = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,dim=-1 ).flatten()
_lowercase : str = torch.cat([timesteps[:1], interleaved_timesteps] )
_lowercase : List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_lowercase : Optional[Any] = defaultdict(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# get log sigma
_lowercase : Optional[Any] = sigma.log()
# get distribution
_lowercase : Optional[int] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
_lowercase : Tuple = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
_lowercase : List[Any] = low_idx + 1
_lowercase : int = self.log_sigmas[low_idx]
_lowercase : Any = self.log_sigmas[high_idx]
# interpolate sigmas
_lowercase : Any = (low - log_sigma) / (low - high)
_lowercase : Dict = w.clamp(0 ,1 )
# transform interpolation to time range
_lowercase : List[str] = (1 - w) * low_idx + w * high_idx
_lowercase : Optional[int] = t.view(sigma.shape )
return t
@property
def lowerCamelCase__ ( self ):
return self.sample is None
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = True ,):
_lowercase : Optional[int] = self.index_for_timestep(UpperCAmelCase_ )
# advance index counter by 1
_lowercase : str = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_lowercase : Any = self.sigmas[step_index]
_lowercase : Any = self.sigmas_interpol[step_index + 1]
_lowercase : Tuple = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
_lowercase : Union[str, Any] = self.sigmas[step_index - 1]
_lowercase : int = self.sigmas_interpol[step_index]
_lowercase : Tuple = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_lowercase : Any = 0
_lowercase : int = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_lowercase : Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : Optional[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_lowercase : str = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_lowercase : List[str] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_lowercase : Any = sigma_interpol - sigma_hat
# store for 2nd order step
_lowercase : List[Any] = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
_lowercase : Optional[Any] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
_lowercase : Optional[Any] = sigma_next - sigma_hat
_lowercase : Any = self.sample
_lowercase : Optional[int] = None
_lowercase : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_lowercase : int = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_ ):
# mps does not support float64
_lowercase : str = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
_lowercase : Any = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
_lowercase : List[Any] = self.timesteps.to(original_samples.device )
_lowercase : Union[str, Any] = timesteps.to(original_samples.device )
_lowercase : List[Any] = [self.index_for_timestep(UpperCAmelCase_ ,UpperCAmelCase_ ) for t in timesteps]
_lowercase : Optional[Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_lowercase : List[Any] = sigma.unsqueeze(-1 )
_lowercase : int = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
return self.config.num_train_timesteps
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if number > 0:
raise ValueError("""input must be a negative integer""" )
_lowercase : List[Any] = len(bin(__UpperCAmelCase )[3:] )
_lowercase : Optional[Any] = bin(abs(__UpperCAmelCase ) - (1 << binary_number_length) )[3:]
_lowercase : List[Any] = (
(
"""1"""
+ """0""" * (binary_number_length - len(__UpperCAmelCase ))
+ twos_complement_number
)
if number < 0
else """0"""
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import pprint
import requests
UpperCAmelCase: Tuple = """https://zenquotes.io/api"""
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
UpperCAmelCase: int = random_quotes()
pprint.pprint(response)
| 336 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase: int = logging.get_logger(__name__)
UpperCAmelCase: Optional[Any] = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
UpperCAmelCase: Dict = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
for attribute in key.split(""".""" ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
_lowercase : Dict = """lm_head"""
_lowercase : List[Any] = getattr(__UpperCAmelCase , __UpperCAmelCase )
if weight_type is not None:
_lowercase : str = getattr(__UpperCAmelCase , __UpperCAmelCase ).shape
else:
_lowercase : List[str] = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
_lowercase : str = value
elif weight_type == "weight_g":
_lowercase : Tuple = value
elif weight_type == "weight_v":
_lowercase : Optional[Any] = value
elif weight_type == "bias":
_lowercase : Optional[int] = value
else:
_lowercase : int = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[Any] = []
_lowercase : Any = fairseq_model.state_dict()
_lowercase : Optional[Any] = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
_lowercase : Any = False
if "conv_layers" in name:
load_conv_layer(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
_lowercase : Optional[int] = True
else:
for key, mapped_key in MAPPING.items():
_lowercase : Dict = """unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowercase : Optional[Any] = True
if "*" in mapped_key:
_lowercase : str = name.split(__UpperCAmelCase )[0].split(""".""" )[-2]
_lowercase : Any = mapped_key.replace("""*""" , __UpperCAmelCase )
if "weight_g" in name:
_lowercase : Tuple = """weight_g"""
elif "weight_v" in name:
_lowercase : Union[str, Any] = """weight_v"""
elif "bias" in name:
_lowercase : Dict = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowercase : Any = """weight"""
else:
_lowercase : Optional[int] = None
set_recursively(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
continue
if not is_used:
unused_weights.append(__UpperCAmelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Dict = full_name.split("""conv_layers.""" )[-1]
_lowercase : List[str] = name.split(""".""" )
_lowercase : Optional[Any] = int(items[0] )
_lowercase : int = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
_lowercase : int = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
_lowercase : Union[str, Any] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
_lowercase : Optional[int] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
_lowercase : Optional[int] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__UpperCAmelCase )
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=True ):
if config_path is not None:
_lowercase : List[Any] = UniSpeechConfig.from_pretrained(__UpperCAmelCase )
else:
_lowercase : Dict = UniSpeechConfig()
if is_finetuned:
if dict_path:
_lowercase : int = Dictionary.load_from_json(__UpperCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowercase : int = target_dict.pad_index
_lowercase : Any = target_dict.bos_index
_lowercase : Optional[int] = target_dict.eos_index
_lowercase : Optional[int] = len(target_dict.symbols )
_lowercase : Dict = os.path.join(__UpperCAmelCase , """vocab.json""" )
if not os.path.isdir(__UpperCAmelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCAmelCase ) )
return
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
_lowercase : List[str] = target_dict.indices
# fairseq has the <pad> and <s> switched
_lowercase : Any = 42
_lowercase : List[Any] = 43
with open(__UpperCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
_lowercase : Optional[int] = WavaVecaPhonemeCTCTokenizer(
__UpperCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__UpperCAmelCase , )
_lowercase : Optional[Any] = True if config.feat_extract_norm == """layer""" else False
_lowercase : int = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , )
_lowercase : List[str] = WavaVecaProcessor(feature_extractor=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
processor.save_pretrained(__UpperCAmelCase )
_lowercase : Dict = UniSpeechForCTC(__UpperCAmelCase )
else:
_lowercase : Dict = UniSpeechForPreTraining(__UpperCAmelCase )
if is_finetuned:
_lowercase , _lowercase , _lowercase : Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] ), """w2v_path""": checkpoint_path} )
else:
_lowercase , _lowercase , _lowercase : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
_lowercase : List[Any] = model[0].eval()
recursively_load_weights(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
hf_unispeech.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase: Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
UpperCAmelCase: str = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 336 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : int
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(__UpperCAmelCase ) )]
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
_lowercase : Tuple = all_rotations(__UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_lowercase : BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(__UpperCAmelCase ),
}
return response
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
_lowercase : Optional[Any] = int(__UpperCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(__UpperCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
_lowercase : int = [""""""] * len(__UpperCAmelCase )
for _ in range(len(__UpperCAmelCase ) ):
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = """Provide a string that I will generate its BWT transform: """
UpperCAmelCase: int = input(entry_msg).strip()
UpperCAmelCase: List[str] = bwt_transform(s)
print(
F'Burrows Wheeler transform for string \'{s}\' results '
F'in \'{result["bwt_string"]}\''
)
UpperCAmelCase: Union[str, Any] = reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
print(
F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
F'we get original string \'{original_string}\''
)
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__UpperCAmelCase , n - 1 , __UpperCAmelCase ) * a) % mod
else:
_lowercase : Dict = binary_exponentiation(__UpperCAmelCase , n / 2 , __UpperCAmelCase )
return (b * b) % mod
# a prime number
UpperCAmelCase: Optional[int] = 701
UpperCAmelCase: List[str] = 1_000_000_000
UpperCAmelCase: Union[str, Any] = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 336 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = [randint(-1000 , 1000 ) for i in range(10 )]
_lowercase : Tuple = randint(-5000 , 5000 )
return (arr, r)
UpperCAmelCase: int = make_dataset()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
for triplet in permutations(__UpperCAmelCase , 3 ):
if sum(__UpperCAmelCase ) == target:
return tuple(sorted(__UpperCAmelCase ) )
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
arr.sort()
_lowercase : Optional[Any] = len(__UpperCAmelCase )
for i in range(n - 1 ):
_lowercase , _lowercase : str = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Tuple = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
_lowercase : Union[str, Any] = """
triplet_sum1(*dataset)
"""
_lowercase : Union[str, Any] = """
triplet_sum2(*dataset)
"""
_lowercase : Dict = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
_lowercase : Any = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
return (min(__UpperCAmelCase ), min(__UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase: Any = solution_times()
print(F'The time for naive implementation is {times[0]}.')
print(F'The time for optimized implementation is {times[1]}.')
| 336 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(UpperCAmelCase_ ,"""hidden_sizes""" ) )
self.parent.assertTrue(hasattr(UpperCAmelCase_ ,"""num_attention_heads""" ) )
self.parent.assertTrue(hasattr(UpperCAmelCase_ ,"""num_encoder_blocks""" ) )
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=13 ,UpperCAmelCase_=64 ,UpperCAmelCase_=3 ,UpperCAmelCase_=4 ,UpperCAmelCase_=[2, 2, 2, 2] ,UpperCAmelCase_=[8, 4, 2, 1] ,UpperCAmelCase_=[16, 32, 64, 1_28] ,UpperCAmelCase_=[1, 4, 8, 16] ,UpperCAmelCase_=[1, 2, 4, 8] ,UpperCAmelCase_=True ,UpperCAmelCase_=True ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=3 ,UpperCAmelCase_=None ,):
_lowercase : int = parent
_lowercase : Dict = batch_size
_lowercase : Optional[int] = image_size
_lowercase : List[Any] = num_channels
_lowercase : Union[str, Any] = num_encoder_blocks
_lowercase : Any = sr_ratios
_lowercase : str = depths
_lowercase : List[Any] = hidden_sizes
_lowercase : str = downsampling_rates
_lowercase : List[str] = num_attention_heads
_lowercase : Optional[int] = is_training
_lowercase : Optional[int] = use_labels
_lowercase : Any = hidden_act
_lowercase : Dict = hidden_dropout_prob
_lowercase : Union[str, Any] = attention_probs_dropout_prob
_lowercase : Any = initializer_range
_lowercase : List[Any] = num_labels
_lowercase : Union[str, Any] = scope
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowercase : Tuple = None
if self.use_labels:
_lowercase : Any = ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels )
_lowercase : Union[str, Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
return SegformerConfig(
image_size=self.image_size ,num_channels=self.num_channels ,num_encoder_blocks=self.num_encoder_blocks ,depths=self.depths ,hidden_sizes=self.hidden_sizes ,num_attention_heads=self.num_attention_heads ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,initializer_range=self.initializer_range ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = SegformerModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
_lowercase : Dict = model(UpperCAmelCase_ )
_lowercase : Tuple = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[int] = self.num_labels
_lowercase : int = SegformerForSemanticSegmentation(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
_lowercase : Any = model(UpperCAmelCase_ )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
_lowercase : List[Any] = model(UpperCAmelCase_ ,labels=UpperCAmelCase_ )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss ,0.0 )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[int] = 1
_lowercase : Optional[Any] = SegformerForSemanticSegmentation(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
_lowercase : Dict = torch.randint(0 ,1 ,(self.batch_size, self.image_size, self.image_size) ).to(UpperCAmelCase_ )
_lowercase : List[Any] = model(UpperCAmelCase_ ,labels=UpperCAmelCase_ )
self.parent.assertGreater(result.loss ,0.0 )
def lowerCamelCase__ ( self ):
_lowercase : str = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase : Tuple = config_and_inputs
_lowercase : List[str] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ : Optional[int] = (
{
"feature-extraction": SegformerModel,
"image-classification": SegformerForImageClassification,
"image-segmentation": SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = True
SCREAMING_SNAKE_CASE_ : Tuple = False
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : Any = False
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = SegformerModelTester(self )
_lowercase : List[Any] = SegformerConfigTester(self ,config_class=UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*UpperCAmelCase_ )
@unittest.skip("""SegFormer does not use inputs_embeds""" )
def lowerCamelCase__ ( self ):
pass
@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods""" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : int = model_class(UpperCAmelCase_ )
_lowercase : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase : Union[str, Any] = [*signature.parameters.keys()]
_lowercase : List[str] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : List[str] = True
for model_class in self.all_model_classes:
_lowercase : Any = True
_lowercase : Optional[int] = False
_lowercase : int = True
_lowercase : Dict = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
_lowercase : Any = model(**self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ) )
_lowercase : Union[str, Any] = outputs.attentions
_lowercase : List[str] = sum(self.model_tester.depths )
self.assertEqual(len(UpperCAmelCase_ ) ,UpperCAmelCase_ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_lowercase : Optional[Any] = True
_lowercase : Tuple = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
_lowercase : str = model(**self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ) )
_lowercase : str = outputs.attentions
self.assertEqual(len(UpperCAmelCase_ ) ,UpperCAmelCase_ )
# verify the first attentions (first block, first layer)
_lowercase : Optional[Any] = (self.model_tester.image_size // 4) ** 2
_lowercase : List[Any] = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,)
# verify the last attentions (last block, last layer)
_lowercase : List[Any] = (self.model_tester.image_size // 32) ** 2
_lowercase : int = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) ,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] ,)
_lowercase : Optional[Any] = len(UpperCAmelCase_ )
# Check attention is always last and order is fine
_lowercase : List[Any] = True
_lowercase : str = True
_lowercase : Tuple = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
_lowercase : Dict = model(**self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ) )
self.assertEqual(out_len + 1 ,len(UpperCAmelCase_ ) )
_lowercase : List[str] = outputs.attentions
self.assertEqual(len(UpperCAmelCase_ ) ,UpperCAmelCase_ )
# verify the first attentions (first block, first layer)
_lowercase : Any = (self.model_tester.image_size // 4) ** 2
_lowercase : Union[str, Any] = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,)
def lowerCamelCase__ ( self ):
def check_hidden_states_output(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : int = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
_lowercase : Optional[int] = model(**self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ) )
_lowercase : Any = outputs.hidden_states
_lowercase : Dict = self.model_tester.num_encoder_blocks
self.assertEqual(len(UpperCAmelCase_ ) ,UpperCAmelCase_ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) ,[
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] ,)
_lowercase , _lowercase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : str = True
check_hidden_states_output(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowercase : List[Any] = True
check_hidden_states_output(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
if not self.model_tester.is_training:
return
_lowercase , _lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : str = True
for model_class in self.all_model_classes:
if model_class in get_values(UpperCAmelCase_ ):
continue
_lowercase : int = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.train()
_lowercase : Tuple = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
_lowercase : Optional[Any] = model(**UpperCAmelCase_ ).loss
loss.backward()
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def lowerCamelCase__ ( self ):
pass
@slow
def lowerCamelCase__ ( self ):
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : Optional[Any] = SegformerModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCamelCase__ ( self ):
# only resize + normalize
_lowercase : Dict = SegformerImageProcessor(
image_scale=(5_12, 5_12) ,keep_ratio=UpperCAmelCase_ ,align=UpperCAmelCase_ ,do_random_crop=UpperCAmelCase_ )
_lowercase : int = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
UpperCAmelCase_ )
_lowercase : Union[str, Any] = prepare_img()
_lowercase : Union[str, Any] = image_processor(images=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : Optional[int] = encoded_inputs.pixel_values.to(UpperCAmelCase_ )
with torch.no_grad():
_lowercase : Any = model(UpperCAmelCase_ )
_lowercase : Dict = torch.Size((1, model.config.num_labels, 1_28, 1_28) )
self.assertEqual(outputs.logits.shape ,UpperCAmelCase_ )
_lowercase : int = torch.tensor(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] ,UpperCAmelCase_ ,atol=1E-4 ) )
@slow
def lowerCamelCase__ ( self ):
# only resize + normalize
_lowercase : str = SegformerImageProcessor(
image_scale=(5_12, 5_12) ,keep_ratio=UpperCAmelCase_ ,align=UpperCAmelCase_ ,do_random_crop=UpperCAmelCase_ )
_lowercase : Optional[int] = SegformerForSemanticSegmentation.from_pretrained(
"""nvidia/segformer-b1-finetuned-cityscapes-1024-1024""" ).to(UpperCAmelCase_ )
_lowercase : Dict = prepare_img()
_lowercase : List[str] = image_processor(images=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : Dict = encoded_inputs.pixel_values.to(UpperCAmelCase_ )
with torch.no_grad():
_lowercase : Optional[Any] = model(UpperCAmelCase_ )
_lowercase : int = torch.Size((1, model.config.num_labels, 1_28, 1_28) )
self.assertEqual(outputs.logits.shape ,UpperCAmelCase_ )
_lowercase : Any = torch.tensor(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] ,UpperCAmelCase_ ,atol=1E-1 ) )
@slow
def lowerCamelCase__ ( self ):
# only resize + normalize
_lowercase : List[str] = SegformerImageProcessor(
image_scale=(5_12, 5_12) ,keep_ratio=UpperCAmelCase_ ,align=UpperCAmelCase_ ,do_random_crop=UpperCAmelCase_ )
_lowercase : Any = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
UpperCAmelCase_ )
_lowercase : Optional[int] = prepare_img()
_lowercase : List[str] = image_processor(images=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : int = encoded_inputs.pixel_values.to(UpperCAmelCase_ )
with torch.no_grad():
_lowercase : Any = model(UpperCAmelCase_ )
_lowercase : str = outputs.logits.detach().cpu()
_lowercase : int = image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_ ,target_sizes=[(5_00, 3_00)] )
_lowercase : Tuple = torch.Size((5_00, 3_00) )
self.assertEqual(segmentation[0].shape ,UpperCAmelCase_ )
_lowercase : Union[str, Any] = image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_ )
_lowercase : List[Any] = torch.Size((1_28, 1_28) )
self.assertEqual(segmentation[0].shape ,UpperCAmelCase_ )
| 336 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE_ : Optional[int] = "BlipImageProcessor"
SCREAMING_SNAKE_CASE_ : Tuple = "AutoTokenizer"
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
super().__init__(UpperCAmelCase_ ,UpperCAmelCase_ )
# add QFormer tokenizer
_lowercase : Optional[int] = qformer_tokenizer
def __call__( self ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = True ,UpperCAmelCase_ = False ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
_lowercase : List[Any] = BatchFeature()
if text is not None:
_lowercase : List[str] = self.tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
encoding.update(UpperCAmelCase_ )
_lowercase : Dict = self.qformer_tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
_lowercase : str = qformer_text_encoding.pop("""input_ids""" )
_lowercase : int = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
_lowercase : Optional[Any] = self.image_processor(UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ )
encoding.update(UpperCAmelCase_ )
return encoding
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.tokenizer.model_input_names
_lowercase : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
if os.path.isfile(UpperCAmelCase_ ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(UpperCAmelCase_ ,exist_ok=UpperCAmelCase_ )
_lowercase : Any = os.path.join(UpperCAmelCase_ ,"""qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(UpperCAmelCase_ )
return super().save_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
_lowercase : Optional[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase_ ,subfolder="""qformer_tokenizer""" )
_lowercase : Any = cls._get_arguments_from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
args.append(UpperCAmelCase_ )
return cls(*UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase: Any = logging.getLogger(__name__)
@dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
SCREAMING_SNAKE_CASE_ : bool = field(default=snake_case , metadata={"help": "Whether tp freeze the encoder."} )
SCREAMING_SNAKE_CASE_ : bool = field(default=snake_case , metadata={"help": "Whether to freeze the embeddings."} )
@dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = field(
metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default="summarization" , metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=1_0_2_4 , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=1_2_8 , metadata={
"help": (
"The maximum total sequence length for target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=1_4_2 , metadata={
"help": (
"The maximum total sequence length for validation target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded. "
"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
"during ``evaluate`` and ``predict``."
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=1_4_2 , metadata={
"help": (
"The maximum total sequence length for test target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(default=-1 , metadata={"help": "# training examples. -1 means use all."} )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(default=-1 , metadata={"help": "# validation examples. -1 means use all."} )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(default=-1 , metadata={"help": "# test examples. -1 means use all."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(default=snake_case , metadata={"help": "Source language id for translation."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(default=snake_case , metadata={"help": "Target language id for translation."} )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(default=snake_case , metadata={"help": "# num_beams to use for evaluation."} )
SCREAMING_SNAKE_CASE_ : bool = field(
default=snake_case , metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."} , )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(__UpperCAmelCase , os.path.join(__UpperCAmelCase , F"""{split}_results.json""" ) )
def __SCREAMING_SNAKE_CASE ( ):
# 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.
_lowercase : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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.
_lowercase , _lowercase , _lowercase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowercase , _lowercase , _lowercase : Optional[int] = parser.parse_args_into_dataclasses()
check_output_dir(__UpperCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , __UpperCAmelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowercase : List[str] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowercase : Tuple = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
assert hasattr(__UpperCAmelCase , __UpperCAmelCase ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(__UpperCAmelCase , __UpperCAmelCase , getattr(__UpperCAmelCase , __UpperCAmelCase ) )
_lowercase : Optional[int] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowercase : Dict = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=__UpperCAmelCase , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(__UpperCAmelCase , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowercase : Dict = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(__UpperCAmelCase , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[Any] = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowercase : Dict = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(__UpperCAmelCase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowercase : Optional[int] = SeqaSeqDataset
# Get datasets
_lowercase : int = (
dataset_class(
__UpperCAmelCase , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
_lowercase : int = (
dataset_class(
__UpperCAmelCase , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowercase : Tuple = (
dataset_class(
__UpperCAmelCase , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowercase : List[Any] = (
build_compute_metrics_fn(data_args.task , __UpperCAmelCase ) if training_args.predict_with_generate else None
)
_lowercase : Tuple = SeqaSeqTrainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , data_args=__UpperCAmelCase , train_dataset=__UpperCAmelCase , eval_dataset=__UpperCAmelCase , data_collator=SeqaSeqDataCollator(
__UpperCAmelCase , __UpperCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=__UpperCAmelCase , tokenizer=__UpperCAmelCase , )
_lowercase : int = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
_lowercase : Any = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowercase : Optional[int] = train_result.metrics
_lowercase : Tuple = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , __UpperCAmelCase , training_args.output_dir )
all_metrics.update(__UpperCAmelCase )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_lowercase : List[Any] = trainer.evaluate(metric_key_prefix="""val""" )
_lowercase : Any = data_args.n_val
_lowercase : Any = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , __UpperCAmelCase , training_args.output_dir )
all_metrics.update(__UpperCAmelCase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
_lowercase : Any = trainer.predict(test_dataset=__UpperCAmelCase , metric_key_prefix="""test""" )
_lowercase : int = test_output.metrics
_lowercase : Optional[int] = data_args.n_test
if trainer.is_world_process_zero():
_lowercase : Union[str, Any] = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , __UpperCAmelCase , training_args.output_dir )
all_metrics.update(__UpperCAmelCase )
if training_args.predict_with_generate:
_lowercase : Any = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase )
_lowercase : Any = lmap(str.strip , __UpperCAmelCase )
write_txt_file(__UpperCAmelCase , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(__UpperCAmelCase , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 336 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase: Tuple = logging.get_logger(__name__)
UpperCAmelCase: List[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "trajectory_transformer"
SCREAMING_SNAKE_CASE_ : Optional[int] = ["past_key_values"]
SCREAMING_SNAKE_CASE_ : Tuple = {
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self ,UpperCAmelCase_=1_00 ,UpperCAmelCase_=5 ,UpperCAmelCase_=1 ,UpperCAmelCase_=1 ,UpperCAmelCase_=2_49 ,UpperCAmelCase_=6 ,UpperCAmelCase_=17 ,UpperCAmelCase_=25 ,UpperCAmelCase_=4 ,UpperCAmelCase_=4 ,UpperCAmelCase_=1_28 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.0006 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=1 ,UpperCAmelCase_=True ,UpperCAmelCase_=1 ,UpperCAmelCase_=5_02_56 ,UpperCAmelCase_=5_02_56 ,**UpperCAmelCase_ ,):
_lowercase : Dict = vocab_size
_lowercase : List[str] = action_weight
_lowercase : int = reward_weight
_lowercase : List[Any] = value_weight
_lowercase : List[str] = max_position_embeddings
_lowercase : Any = block_size
_lowercase : Any = action_dim
_lowercase : List[str] = observation_dim
_lowercase : Union[str, Any] = transition_dim
_lowercase : str = learning_rate
_lowercase : Tuple = n_layer
_lowercase : Optional[int] = n_head
_lowercase : List[str] = n_embd
_lowercase : List[str] = embd_pdrop
_lowercase : Optional[Any] = attn_pdrop
_lowercase : List[Any] = resid_pdrop
_lowercase : str = initializer_range
_lowercase : Optional[Any] = layer_norm_eps
_lowercase : List[Any] = kaiming_initializer_range
_lowercase : List[Any] = use_cache
super().__init__(pad_token_id=UpperCAmelCase_ ,bos_token_id=UpperCAmelCase_ ,eos_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Dict = len(__UpperCAmelCase )
# We need to create solution object to save path.
_lowercase : int = [[0 for _ in range(__UpperCAmelCase )] for _ in range(__UpperCAmelCase )]
_lowercase : Optional[Any] = run_maze(__UpperCAmelCase , 0 , 0 , __UpperCAmelCase )
if solved:
print("""\n""".join(str(__UpperCAmelCase ) for row in solutions ) )
else:
print("""No solution exists!""" )
return solved
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : List[Any] = len(__UpperCAmelCase )
# Final check point.
if i == j == (size - 1):
_lowercase : Any = 1
return True
_lowercase : Optional[int] = (not i < 0) and (not j < 0) # Check lower bounds
_lowercase : Union[str, Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
_lowercase : str = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
_lowercase : Optional[int] = 1
# check for directions
if (
run_maze(__UpperCAmelCase , i + 1 , __UpperCAmelCase , __UpperCAmelCase )
or run_maze(__UpperCAmelCase , __UpperCAmelCase , j + 1 , __UpperCAmelCase )
or run_maze(__UpperCAmelCase , i - 1 , __UpperCAmelCase , __UpperCAmelCase )
or run_maze(__UpperCAmelCase , __UpperCAmelCase , j - 1 , __UpperCAmelCase )
):
return True
_lowercase : Optional[Any] = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase: Any = logging.get_logger(__name__)
UpperCAmelCase: List[str] = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = "instructblip_vision_model"
def __init__( self ,UpperCAmelCase_=14_08 ,UpperCAmelCase_=61_44 ,UpperCAmelCase_=39 ,UpperCAmelCase_=16 ,UpperCAmelCase_=2_24 ,UpperCAmelCase_=14 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=1E-6 ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=1E-10 ,UpperCAmelCase_=True ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : Optional[Any] = hidden_size
_lowercase : Tuple = intermediate_size
_lowercase : List[Any] = num_hidden_layers
_lowercase : Tuple = num_attention_heads
_lowercase : Optional[Any] = patch_size
_lowercase : Optional[Any] = image_size
_lowercase : Union[str, Any] = initializer_range
_lowercase : Optional[Any] = attention_dropout
_lowercase : List[Any] = layer_norm_eps
_lowercase : Optional[int] = hidden_act
_lowercase : Tuple = qkv_bias
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : List[Any] = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : int = 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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "instructblip_qformer"
def __init__( self ,UpperCAmelCase_=3_05_22 ,UpperCAmelCase_=7_68 ,UpperCAmelCase_=12 ,UpperCAmelCase_=12 ,UpperCAmelCase_=30_72 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=0 ,UpperCAmelCase_="absolute" ,UpperCAmelCase_=2 ,UpperCAmelCase_=14_08 ,**UpperCAmelCase_ ,):
super().__init__(pad_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : List[Any] = vocab_size
_lowercase : List[Any] = hidden_size
_lowercase : str = num_hidden_layers
_lowercase : List[str] = num_attention_heads
_lowercase : Optional[Any] = hidden_act
_lowercase : int = intermediate_size
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Optional[Any] = attention_probs_dropout_prob
_lowercase : List[Any] = max_position_embeddings
_lowercase : Tuple = initializer_range
_lowercase : Optional[int] = layer_norm_eps
_lowercase : Any = position_embedding_type
_lowercase : Dict = cross_attention_frequency
_lowercase : Optional[Any] = encoder_hidden_size
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : Dict = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : str = config_dict["""qformer_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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "instructblip"
SCREAMING_SNAKE_CASE_ : List[str] = True
def __init__( self ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=32 ,**UpperCAmelCase_ ):
super().__init__(**UpperCAmelCase_ )
if vision_config is None:
_lowercase : str = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
_lowercase : Any = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
_lowercase : Optional[int] = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
_lowercase : int = InstructBlipVisionConfig(**UpperCAmelCase_ )
_lowercase : Optional[int] = InstructBlipQFormerConfig(**UpperCAmelCase_ )
_lowercase : Dict = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
_lowercase : str = CONFIG_MAPPING[text_model_type](**UpperCAmelCase_ )
_lowercase : str = self.text_config.tie_word_embeddings
_lowercase : Union[str, Any] = self.text_config.is_encoder_decoder
_lowercase : List[str] = num_query_tokens
_lowercase : List[str] = self.vision_config.hidden_size
_lowercase : Dict = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowercase : Union[str, Any] = 1.0
_lowercase : Dict = 0.02
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_lowercase : int = self.vision_config.to_dict()
_lowercase : Any = self.qformer_config.to_dict()
_lowercase : Any = self.text_config.to_dict()
_lowercase : Optional[int] = self.__class__.model_type
return output
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if length <= 0 or not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(__UpperCAmelCase )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(length=10))
| 336 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if k in (0.04, 0.06):
_lowercase : Optional[Any] = k
_lowercase : Optional[Any] = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ):
return str(self.k )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = cva.imread(UpperCAmelCase_ ,0 )
_lowercase , _lowercase : Dict = img.shape
_lowercase : list[list[int]] = []
_lowercase : int = img.copy()
_lowercase : List[str] = cva.cvtColor(UpperCAmelCase_ ,cva.COLOR_GRAY2RGB )
_lowercase , _lowercase : Optional[Any] = np.gradient(UpperCAmelCase_ )
_lowercase : Optional[int] = dx**2
_lowercase : Optional[Any] = dy**2
_lowercase : Optional[Any] = dx * dy
_lowercase : List[str] = 0.04
_lowercase : Optional[Any] = self.window_size // 2
for y in range(UpperCAmelCase_ ,h - offset ):
for x in range(UpperCAmelCase_ ,w - offset ):
_lowercase : Optional[Any] = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Dict = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Union[str, Any] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : int = (wxx * wyy) - (wxy**2)
_lowercase : Union[str, Any] = wxx + wyy
_lowercase : Union[str, Any] = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) ,0 )
color_img.itemset((y, x, 1) ,0 )
color_img.itemset((y, x, 2) ,2_55 )
return color_img, corner_list
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = HarrisCorner(0.04, 3)
UpperCAmelCase , UpperCAmelCase: List[Any] = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
UpperCAmelCase: Optional[Any] = """https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCAmelCase: Union[str, Any] = BASE_URL + """/user"""
# https://github.com/settings/tokens
UpperCAmelCase: List[str] = os.environ.get("""USER_TOKEN""", """""")
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : List[Any] = {
"""Authorization""": F"""token {auth_token}""",
"""Accept""": """application/vnd.github.v3+json""",
}
return requests.get(__UpperCAmelCase , headers=__UpperCAmelCase ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(F'{key}: {value}')
else:
raise ValueError("""'USER_TOKEN' field cannot be empty.""")
| 336 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = LEDTokenizer
SCREAMING_SNAKE_CASE_ : List[str] = LEDTokenizerFast
SCREAMING_SNAKE_CASE_ : List[str] = True
def lowerCamelCase__ ( self ):
super().setUp()
_lowercase : Union[str, Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
_lowercase : List[Any] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
_lowercase : Dict = {"""unk_token""": """<unk>"""}
_lowercase : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
_lowercase : Any = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(UpperCAmelCase_ ,max_length=len(UpperCAmelCase_ ) ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
_lowercase : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIn("""input_ids""" ,UpperCAmelCase_ )
self.assertIn("""attention_mask""" ,UpperCAmelCase_ )
self.assertNotIn("""labels""" ,UpperCAmelCase_ )
self.assertNotIn("""decoder_attention_mask""" ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Dict = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(text_target=UpperCAmelCase_ ,max_length=32 ,padding="""max_length""" ,return_tensors="""pt""" )
self.assertEqual(32 ,targets["""input_ids"""].shape[1] )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : List[Any] = tokenizer(
["""I am a small frog""" * 10_24, """I am a small frog"""] ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = ["""A long paragraph for summarization."""]
_lowercase : Dict = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : List[str] = tokenizer(text_target=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : Union[str, Any] = inputs["""input_ids"""]
_lowercase : List[str] = targets["""input_ids"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : str = ["""Summary of the text.""", """Another summary."""]
_lowercase : Optional[int] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
_lowercase : Any = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ )
_lowercase : str = [[0] * len(UpperCAmelCase_ ) for x in encoded_output["""input_ids"""]]
_lowercase : Optional[int] = tokenizer.pad(UpperCAmelCase_ )
self.assertSequenceEqual(outputs["""global_attention_mask"""] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowercase : int = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Optional[int] = self.tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = """A, <mask> AllenNLP sentence."""
_lowercase : List[Any] = tokenizer_r.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
_lowercase : Any = tokenizer_p.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) ,sum(tokens_p["""token_type_ids"""] ) )
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) ,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) ,)
_lowercase : str = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
_lowercase : str = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
self.assertSequenceEqual(tokens_p["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[Any] = 0
_lowercase : Optional[int] = len(__UpperCAmelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
_lowercase : Dict = i + 1
else:
_lowercase : Optional[int] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 336 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Any = f.readlines()
_lowercase : Optional[int] = F"""class {class_name}("""
_lowercase : List[str] = F"""{4 * " "}def {test_name}("""
_lowercase : List[Any] = F"""{8 * " "}{correct_line.split()[0]}"""
_lowercase : int = F"""{16 * " "}{correct_line.split()[0]}"""
_lowercase : str = False
_lowercase : Optional[Any] = False
_lowercase : Union[str, Any] = False
_lowercase : Any = False
_lowercase : int = 0
_lowercase : Tuple = 0
_lowercase : Union[str, Any] = []
for line in lines:
if line.startswith(__UpperCAmelCase ):
_lowercase : List[str] = True
elif in_class and line.startswith(__UpperCAmelCase ):
_lowercase : str = True
elif in_class and in_func and (line.startswith(__UpperCAmelCase ) or line.startswith(__UpperCAmelCase )):
_lowercase : Union[str, Any] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
_lowercase : Optional[int] = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
_lowercase : Optional[Any] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(F"""{spaces * " "}{correct_line}""" )
_lowercase : Union[str, Any] = False
else:
new_lines.append(__UpperCAmelCase )
with open(__UpperCAmelCase , """w""" ) as f:
for line in new_lines:
f.write(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None ):
if fail is not None:
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Dict = {l.strip() for l in f.readlines()}
else:
_lowercase : int = None
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : int = f.readlines()
_lowercase : int = defaultdict(__UpperCAmelCase )
for line in correct_lines:
_lowercase , _lowercase , _lowercase , _lowercase : int = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase: List[Any] = argparse.ArgumentParser()
parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
UpperCAmelCase: Any = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 336 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase: Tuple = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Optional[int] = DPTConfig()
if "large" in checkpoint_url:
_lowercase : str = 1024
_lowercase : List[Any] = 4096
_lowercase : Union[str, Any] = 24
_lowercase : Tuple = 16
_lowercase : List[Any] = [5, 11, 17, 23]
_lowercase : Union[str, Any] = [256, 512, 1024, 1024]
_lowercase : Union[str, Any] = (1, 384, 384)
if "ade" in checkpoint_url:
_lowercase : Optional[int] = True
_lowercase : Union[str, Any] = 150
_lowercase : List[Any] = """huggingface/label-files"""
_lowercase : Tuple = """ade20k-id2label.json"""
_lowercase : Tuple = json.load(open(cached_download(hf_hub_url(__UpperCAmelCase , __UpperCAmelCase , repo_type="""dataset""" ) ) , """r""" ) )
_lowercase : Tuple = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
_lowercase : str = idalabel
_lowercase : Any = {v: k for k, v in idalabel.items()}
_lowercase : Union[str, Any] = [1, 150, 480, 480]
return config, expected_shape
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase , __UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
_lowercase : Optional[Any] = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
_lowercase : List[Any] = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
_lowercase : str = name.replace("""patch_embed""" , """patch_embeddings""" )
if "pos_embed" in name:
_lowercase : Optional[int] = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
_lowercase : Any = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
_lowercase : Dict = name.replace("""proj""" , """projection""" )
if "blocks" in name:
_lowercase : Union[str, Any] = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
_lowercase : Any = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
_lowercase : int = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name:
_lowercase : List[Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
_lowercase : Optional[Any] = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
_lowercase : Union[str, Any] = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
_lowercase : Union[str, Any] = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
_lowercase : str = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
_lowercase : Tuple = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
_lowercase : str = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
_lowercase : List[str] = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
_lowercase : Union[str, Any] = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
_lowercase : int = name.replace(F"""refinenet{layer_idx}""" , F"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
if "out_conv" in name:
_lowercase : int = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
_lowercase : List[str] = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
_lowercase : Dict = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
_lowercase : Dict = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
_lowercase : Any = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
_lowercase : Dict = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
_lowercase : Tuple = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
_lowercase : List[str] = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
_lowercase : str = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
_lowercase : List[str] = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
_lowercase : List[Any] = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
_lowercase : Any = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
_lowercase : str = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
_lowercase : List[Any] = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
_lowercase : List[Any] = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
_lowercase : List[str] = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
_lowercase : Optional[int] = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
_lowercase : str = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
_lowercase : str = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
_lowercase : Tuple = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
_lowercase : str = name.replace("""auxlayer""" , """auxiliary_head.head""" )
return name
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowercase : Dict = state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
_lowercase : str = state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_lowercase : List[str] = in_proj_weight[: config.hidden_size, :]
_lowercase : str = in_proj_bias[: config.hidden_size]
_lowercase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowercase : Optional[int] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowercase : Union[str, Any] = in_proj_weight[
-config.hidden_size :, :
]
_lowercase : Union[str, Any] = in_proj_bias[-config.hidden_size :]
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowercase : str = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase , _lowercase : List[Any] = get_dpt_config(__UpperCAmelCase )
# load original state_dict from URL
_lowercase : List[Any] = torch.hub.load_state_dict_from_url(__UpperCAmelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__UpperCAmelCase )
# rename keys
for key in state_dict.copy().keys():
_lowercase : int = state_dict.pop(__UpperCAmelCase )
_lowercase : List[Any] = val
# read in qkv matrices
read_in_q_k_v(__UpperCAmelCase , __UpperCAmelCase )
# load HuggingFace model
_lowercase : int = DPTForSemanticSegmentation(__UpperCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
# Check outputs on an image
_lowercase : Union[str, Any] = 480 if """ade""" in checkpoint_url else 384
_lowercase : List[str] = DPTImageProcessor(size=__UpperCAmelCase )
_lowercase : Union[str, Any] = prepare_img()
_lowercase : int = image_processor(__UpperCAmelCase , return_tensors="""pt""" )
# forward pass
_lowercase : List[str] = model(**__UpperCAmelCase ).logits if """ade""" in checkpoint_url else model(**__UpperCAmelCase ).predicted_depth
# Assert logits
_lowercase : Tuple = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
if "ade" in checkpoint_url:
_lowercase : int = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
assert outputs.shape == torch.Size(__UpperCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3] , __UpperCAmelCase , atol=1E-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , __UpperCAmelCase )
)
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCAmelCase )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase , __UpperCAmelCase ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=__UpperCAmelCase , )
image_processor.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase , __UpperCAmelCase ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=__UpperCAmelCase , )
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
UpperCAmelCase: Tuple = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 336 |
"""simple docstring"""
UpperCAmelCase: List[str] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = abs(__UpperCAmelCase )
_lowercase : int = 0
while n > 0:
res += n % 10
n //= 10
return res
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : List[str] = abs(__UpperCAmelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return sum(int(__UpperCAmelCase ) for c in str(abs(__UpperCAmelCase ) ) )
def __SCREAMING_SNAKE_CASE ( ):
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(__UpperCAmelCase , __UpperCAmelCase ) -> None:
_lowercase : Any = F"""{func.__name__}({value})"""
_lowercase : List[str] = timeit(F"""__main__.{call}""" , setup="""import __main__""" )
print(F"""{call:56} = {func(__UpperCAmelCase )} -- {timing:.4f} seconds""" )
for value in (262144, 1125899906842624, 1267650600228229401496703205376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(__UpperCAmelCase , __UpperCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 336 |
"""simple docstring"""
UpperCAmelCase: str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCAmelCase: Any = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase: int = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 336 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def lowerCamelCase__ ( self ,UpperCAmelCase_=0 ):
_lowercase : Optional[int] = np.random.RandomState(UpperCAmelCase_ )
_lowercase : Union[str, Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ ( self ):
_lowercase : Any = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : List[Any] = self.get_dummy_inputs()
_lowercase : List[Any] = pipe(**UpperCAmelCase_ ).images
_lowercase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_lowercase : Tuple = np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_lowercase : Tuple = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
_lowercase : Dict = PNDMScheduler.from_config(pipe.scheduler.config ,skip_prk_steps=UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : Union[str, Any] = self.get_dummy_inputs()
_lowercase : Optional[Any] = pipe(**UpperCAmelCase_ ).images
_lowercase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_lowercase : int = np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_lowercase : str = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
_lowercase : Optional[int] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : Dict = self.get_dummy_inputs()
_lowercase : Optional[int] = pipe(**UpperCAmelCase_ ).images
_lowercase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_lowercase : List[Any] = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_lowercase : Tuple = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
_lowercase : Dict = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : str = self.get_dummy_inputs()
_lowercase : str = pipe(**UpperCAmelCase_ ).images
_lowercase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_lowercase : str = np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
_lowercase : Any = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : Optional[Any] = self.get_dummy_inputs()
_lowercase : Union[str, Any] = pipe(**UpperCAmelCase_ ).images
_lowercase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_lowercase : int = np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
_lowercase : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : List[Any] = self.get_dummy_inputs()
_lowercase : int = pipe(**UpperCAmelCase_ ).images
_lowercase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
_lowercase : List[str] = np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : str = self.get_dummy_inputs()
_lowercase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
_lowercase : str = pipe(**UpperCAmelCase_ )
_lowercase : Optional[int] = output.images[0, -3:, -3:, -1]
_lowercase : List[str] = self.get_dummy_inputs()
_lowercase : List[Any] = 3 * [inputs.pop("""prompt""" )]
_lowercase : Optional[int] = pipe.tokenizer(
UpperCAmelCase_ ,padding="""max_length""" ,max_length=pipe.tokenizer.model_max_length ,truncation=UpperCAmelCase_ ,return_tensors="""np""" ,)
_lowercase : Tuple = text_inputs["""input_ids"""]
_lowercase : int = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
_lowercase : str = prompt_embeds
# forward
_lowercase : List[Any] = pipe(**UpperCAmelCase_ )
_lowercase : Tuple = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : Tuple = self.get_dummy_inputs()
_lowercase : List[Any] = 3 * ["""this is a negative prompt"""]
_lowercase : int = negative_prompt
_lowercase : Tuple = 3 * [inputs["""prompt"""]]
# forward
_lowercase : Dict = pipe(**UpperCAmelCase_ )
_lowercase : List[str] = output.images[0, -3:, -3:, -1]
_lowercase : Optional[int] = self.get_dummy_inputs()
_lowercase : Optional[int] = 3 * [inputs.pop("""prompt""" )]
_lowercase : Optional[Any] = []
for p in [prompt, negative_prompt]:
_lowercase : List[Any] = pipe.tokenizer(
UpperCAmelCase_ ,padding="""max_length""" ,max_length=pipe.tokenizer.model_max_length ,truncation=UpperCAmelCase_ ,return_tensors="""np""" ,)
_lowercase : Optional[Any] = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
_lowercase , _lowercase : Dict = embeds
# forward
_lowercase : List[Any] = pipe(**UpperCAmelCase_ )
_lowercase : Any = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@property
def lowerCamelCase__ ( self ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = ort.SessionOptions()
_lowercase : Any = False
return options
def lowerCamelCase__ ( self ):
# using the PNDM scheduler by default
_lowercase : Optional[int] = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" ,revision="""onnx""" ,safety_checker=UpperCAmelCase_ ,feature_extractor=UpperCAmelCase_ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : List[Any] = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
_lowercase : List[Any] = sd_pipe([prompt] ,guidance_scale=6.0 ,num_inference_steps=10 ,output_type="""np""" )
_lowercase : Union[str, Any] = output.images
_lowercase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
_lowercase : Optional[int] = np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCamelCase__ ( self ):
_lowercase : Any = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,subfolder="""scheduler""" ,revision="""onnx""" )
_lowercase : Tuple = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,revision="""onnx""" ,scheduler=UpperCAmelCase_ ,safety_checker=UpperCAmelCase_ ,feature_extractor=UpperCAmelCase_ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : Dict = """open neural network exchange"""
_lowercase : Optional[Any] = np.random.RandomState(0 )
_lowercase : Union[str, Any] = sd_pipe([prompt] ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=UpperCAmelCase_ ,output_type="""np""" )
_lowercase : Optional[Any] = output.images
_lowercase : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
_lowercase : Union[str, Any] = np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCamelCase__ ( self ):
_lowercase : str = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,subfolder="""scheduler""" ,revision="""onnx""" )
_lowercase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,revision="""onnx""" ,scheduler=UpperCAmelCase_ ,safety_checker=UpperCAmelCase_ ,feature_extractor=UpperCAmelCase_ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : Dict = """open neural network exchange"""
_lowercase : Dict = np.random.RandomState(0 )
_lowercase : List[Any] = sd_pipe([prompt] ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=UpperCAmelCase_ ,output_type="""np""" )
_lowercase : Optional[Any] = output.images
_lowercase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_12, 5_12, 3)
_lowercase : Dict = np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def lowerCamelCase__ ( self ):
_lowercase : List[str] = 0
def test_callback_fn(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ) -> None:
_lowercase : int = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
_lowercase : List[str] = latents[0, -3:, -3:, -1]
_lowercase : Union[str, Any] = np.array(
[-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
_lowercase : str = latents[0, -3:, -3:, -1]
_lowercase : List[Any] = np.array(
[-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
_lowercase : Tuple = False
_lowercase : List[str] = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,revision="""onnx""" ,safety_checker=UpperCAmelCase_ ,feature_extractor=UpperCAmelCase_ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : Optional[int] = """Andromeda galaxy in a bottle"""
_lowercase : Dict = np.random.RandomState(0 )
pipe(
prompt=UpperCAmelCase_ ,num_inference_steps=5 ,guidance_scale=7.5 ,generator=UpperCAmelCase_ ,callback=UpperCAmelCase_ ,callback_steps=1 ,)
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def lowerCamelCase__ ( self ):
_lowercase : Dict = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" ,revision="""onnx""" ,safety_checker=UpperCAmelCase_ ,feature_extractor=UpperCAmelCase_ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
assert isinstance(UpperCAmelCase_ ,UpperCAmelCase_ )
assert pipe.safety_checker is None
_lowercase : Optional[int] = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(UpperCAmelCase_ )
_lowercase : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(UpperCAmelCase_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_lowercase : Any = pipe("""example prompt""" ,num_inference_steps=2 ).images[0]
assert image is not None
| 336 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
UpperCAmelCase: List[Any] = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
def constraint_to_multiple_of(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=0 , __UpperCAmelCase=None ):
_lowercase : Union[str, Any] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowercase : str = math.floor(val / multiple ) * multiple
if x < min_val:
_lowercase : Dict = math.ceil(val / multiple ) * multiple
return x
_lowercase : List[str] = (output_size, output_size) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else output_size
_lowercase , _lowercase : List[Any] = get_image_size(__UpperCAmelCase )
_lowercase , _lowercase : Union[str, Any] = output_size
# determine new height and width
_lowercase : str = output_height / input_height
_lowercase : List[Any] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowercase : str = scale_width
else:
# fit height
_lowercase : int = scale_height
_lowercase : List[Any] = constraint_to_multiple_of(scale_height * input_height , multiple=__UpperCAmelCase )
_lowercase : Optional[Any] = constraint_to_multiple_of(scale_width * input_width , multiple=__UpperCAmelCase )
return (new_height, new_width)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["pixel_values"]
def __init__( self ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = PILImageResampling.BILINEAR ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = 1 / 2_55 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : List[Any] = size if size is not None else {"""height""": 3_84, """width""": 3_84}
_lowercase : str = get_size_dict(UpperCAmelCase_ )
_lowercase : Tuple = do_resize
_lowercase : Any = size
_lowercase : List[Any] = keep_aspect_ratio
_lowercase : Any = ensure_multiple_of
_lowercase : str = resample
_lowercase : Optional[Any] = do_rescale
_lowercase : List[Any] = rescale_factor
_lowercase : Union[str, Any] = do_normalize
_lowercase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowercase : Any = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = PILImageResampling.BICUBIC ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
_lowercase : Optional[Any] = get_size_dict(UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_lowercase : Dict = get_resize_output_image_size(
UpperCAmelCase_ ,output_size=(size["""height"""], size["""width"""]) ,keep_aspect_ratio=UpperCAmelCase_ ,multiple=UpperCAmelCase_ ,)
return resize(UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return rescale(UpperCAmelCase_ ,scale=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return normalize(UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = ChannelDimension.FIRST ,**UpperCAmelCase_ ,):
_lowercase : Any = do_resize if do_resize is not None else self.do_resize
_lowercase : List[str] = size if size is not None else self.size
_lowercase : int = get_size_dict(UpperCAmelCase_ )
_lowercase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowercase : List[str] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowercase : List[str] = resample if resample is not None else self.resample
_lowercase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowercase : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase : str = do_normalize if do_normalize is not None else self.do_normalize
_lowercase : Optional[int] = image_mean if image_mean is not None else self.image_mean
_lowercase : int = image_std if image_std is not None else self.image_std
_lowercase : Union[str, Any] = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_lowercase : int = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
_lowercase : Union[str, Any] = [self.resize(image=UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ) for image in images]
if do_rescale:
_lowercase : int = [self.rescale(image=UpperCAmelCase_ ,scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
_lowercase : str = [self.normalize(image=UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ) for image in images]
_lowercase : Tuple = [to_channel_dimension_format(UpperCAmelCase_ ,UpperCAmelCase_ ) for image in images]
_lowercase : int = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase_ ,tensor_type=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ):
_lowercase : Union[str, Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(UpperCAmelCase_ ):
_lowercase : Tuple = target_sizes.numpy()
_lowercase : Optional[Any] = []
for idx in range(len(UpperCAmelCase_ ) ):
_lowercase : Dict = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=UpperCAmelCase_ )
_lowercase : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCAmelCase_ )
else:
_lowercase : Union[str, Any] = logits.argmax(dim=1 )
_lowercase : Optional[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=3 ,UpperCAmelCase_=32 ,UpperCAmelCase_=3 ,UpperCAmelCase_=10 ,UpperCAmelCase_=[10, 20, 30, 40] ,UpperCAmelCase_=[1, 1, 2, 1] ,UpperCAmelCase_=True ,UpperCAmelCase_=True ,UpperCAmelCase_="relu" ,UpperCAmelCase_=3 ,UpperCAmelCase_=None ,):
_lowercase : List[str] = parent
_lowercase : str = batch_size
_lowercase : Tuple = image_size
_lowercase : Dict = num_channels
_lowercase : List[str] = embeddings_size
_lowercase : str = hidden_sizes
_lowercase : List[Any] = depths
_lowercase : Optional[Any] = is_training
_lowercase : int = use_labels
_lowercase : List[Any] = hidden_act
_lowercase : Optional[Any] = num_labels
_lowercase : str = scope
_lowercase : Tuple = len(UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowercase : List[Any] = None
if self.use_labels:
_lowercase : Dict = ids_tensor([self.batch_size] ,self.num_labels )
_lowercase : List[Any] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
return RegNetConfig(
num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Dict = TFRegNetModel(config=UpperCAmelCase_ )
_lowercase : Union[str, Any] = model(UpperCAmelCase_ ,training=UpperCAmelCase_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.num_labels
_lowercase : Optional[int] = TFRegNetForImageClassification(UpperCAmelCase_ )
_lowercase : Dict = model(UpperCAmelCase_ ,labels=UpperCAmelCase_ ,training=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase : Optional[int] = config_and_inputs
_lowercase : Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : List[Any] = (
{"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Any = False
def lowerCamelCase__ ( self ):
_lowercase : Dict = TFRegNetModelTester(self )
_lowercase : List[Any] = ConfigTester(self ,config_class=UpperCAmelCase_ ,has_text_modality=UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
return
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def lowerCamelCase__ ( self ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 ,reason="""TF does not support backprop for grouped convolutions on CPU.""" ,)
@slow
def lowerCamelCase__ ( self ):
super().test_keras_fit()
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : Optional[int] = model_class(UpperCAmelCase_ )
_lowercase : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase : Optional[Any] = [*signature.parameters.keys()]
_lowercase : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
def check_hidden_states_output(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : List[Any] = model_class(UpperCAmelCase_ )
_lowercase : Optional[Any] = model(**self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ) ,training=UpperCAmelCase_ )
_lowercase : Dict = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowercase : Union[str, Any] = self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase_ ) ,expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,)
_lowercase , _lowercase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : Optional[int] = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_lowercase : List[Any] = layer_type
_lowercase : List[str] = True
check_hidden_states_output(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowercase : List[str] = True
check_hidden_states_output(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_={} ):
_lowercase : Dict = model(UpperCAmelCase_ ,return_dict=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Tuple = model(UpperCAmelCase_ ,return_dict=UpperCAmelCase_ ,**UpperCAmelCase_ ).to_tuple()
def recursive_check(UpperCAmelCase_ ,UpperCAmelCase_ ):
if isinstance(UpperCAmelCase_ ,(List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase_ ,UpperCAmelCase_ ):
recursive_check(UpperCAmelCase_ ,UpperCAmelCase_ )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(UpperCAmelCase_ ,UpperCAmelCase_ ) ) ,msg=(
"""Tuple and dict output are not equal. Difference:"""
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) ,)
recursive_check(UpperCAmelCase_ ,UpperCAmelCase_ )
for model_class in self.all_model_classes:
_lowercase : Any = model_class(UpperCAmelCase_ )
_lowercase : int = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Any = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Union[str, Any] = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
_lowercase : Dict = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Union[str, Any] = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Any = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,{"""output_hidden_states""": True} )
_lowercase : Tuple = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
_lowercase : Dict = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ,return_labels=UpperCAmelCase_ )
check_equivalence(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,{"""output_hidden_states""": True} )
def lowerCamelCase__ ( self ):
_lowercase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
@slow
def lowerCamelCase__ ( self ):
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : List[str] = TFRegNetModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCamelCase__ ( self ):
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase__ ( self ):
_lowercase : str = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_lowercase : int = self.default_image_processor
_lowercase : List[str] = prepare_img()
_lowercase : Union[str, Any] = image_processor(images=UpperCAmelCase_ ,return_tensors="""tf""" )
# forward pass
_lowercase : List[str] = model(**UpperCAmelCase_ ,training=UpperCAmelCase_ )
# verify the logits
_lowercase : Tuple = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape ,UpperCAmelCase_ )
_lowercase : Union[str, Any] = tf.constant([-0.4180, -1.5051, -3.4836] )
tf.debugging.assert_near(outputs.logits[0, :3] ,UpperCAmelCase_ ,atol=1E-4 )
| 336 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase: Optional[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
UpperCAmelCase: Tuple = [0, 25, 50]
UpperCAmelCase: List[Any] = [25, 50, 75]
UpperCAmelCase: Optional[int] = fuzz.membership.trimf(X, abca)
UpperCAmelCase: Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
UpperCAmelCase: List[Any] = np.ones(75)
UpperCAmelCase: Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
UpperCAmelCase: str = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
UpperCAmelCase: List[Any] = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
UpperCAmelCase: int = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
UpperCAmelCase: int = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
UpperCAmelCase: List[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
UpperCAmelCase: int = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("""Young""")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("""Middle aged""")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("""union""")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("""intersection""")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("""complement_a""")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("""difference a/b""")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("""alg_sum""")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("""alg_product""")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("""bdd_sum""")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("""bdd_difference""")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 336 | 1 |
"""simple docstring"""
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
UpperCAmelCase: Any = logging.get_logger(__name__)
@dataclass
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__( self ,**UpperCAmelCase_ ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
_lowercase : str = deprecated_arg[3:]
_lowercase : Optional[int] = not kwargs.pop(UpperCAmelCase_ )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
_lowercase : List[Any] = kwargs.pop("""tpu_name""" ,self.tpu_name )
_lowercase : Optional[Any] = kwargs.pop("""device_idx""" ,self.device_idx )
_lowercase : Dict = kwargs.pop("""eager_mode""" ,self.eager_mode )
_lowercase : List[str] = kwargs.pop("""use_xla""" ,self.use_xla )
super().__init__(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ : str = field(
default=snake_case , metadata={"help": "Name of TPU"} , )
SCREAMING_SNAKE_CASE_ : int = field(
default=0 , metadata={"help": "CPU / GPU device index. Defaults to 0."} , )
SCREAMING_SNAKE_CASE_ : bool = field(default=snake_case , metadata={"help": "Benchmark models in eager model."} )
SCREAMING_SNAKE_CASE_ : bool = field(
default=snake_case , metadata={
"help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
} , )
@cached_property
def lowerCamelCase__ ( self ):
requires_backends(self ,["""tf"""] )
_lowercase : Union[str, Any] = None
if self.tpu:
try:
if self.tpu_name:
_lowercase : List[str] = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
_lowercase : int = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
_lowercase : Optional[int] = None
return tpu
@cached_property
def lowerCamelCase__ ( self ):
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 )
_lowercase : Union[str, Any] = 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""" )
_lowercase : str = tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}""" )
else:
tf.config.set_visible_devices([] ,"""GPU""" ) # disable GPU
_lowercase : Union[str, Any] = tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}""" )
return strategy
@property
def lowerCamelCase__ ( self ):
requires_backends(self ,["""tf"""] )
return self._setup_tpu is not None
@property
def lowerCamelCase__ ( self ):
requires_backends(self ,["""tf"""] )
return self._setup_strategy
@property
def lowerCamelCase__ ( self ):
requires_backends(self ,["""tf"""] )
return tf.config.list_physical_devices("""GPU""" )
@property
def lowerCamelCase__ ( self ):
requires_backends(self ,["""tf"""] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def lowerCamelCase__ ( self ):
return self.n_gpu > 0
| 336 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
_lowercase : str = tempfile.mkdtemp()
# fmt: off
_lowercase : List[Any] = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
_lowercase : Optional[int] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
_lowercase : Optional[int] = {"""unk_token""": """<unk>"""}
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
_lowercase : Dict = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48145466, 0.4578275, 0.40821073],
"""image_std""": [0.26862954, 0.26130258, 0.27577711],
}
_lowercase : List[Any] = os.path.join(self.tmpdirname ,UpperCAmelCase_ )
with open(self.image_processor_file ,"""w""" ,encoding="""utf-8""" ) as fp:
json.dump(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPTokenizer.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
_lowercase : Tuple = [Image.fromarray(np.moveaxis(UpperCAmelCase_ ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.get_tokenizer()
_lowercase : List[Any] = self.get_rust_tokenizer()
_lowercase : List[Any] = self.get_image_processor()
_lowercase : List[str] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
_lowercase : Optional[int] = CLIPProcessor.from_pretrained(self.tmpdirname ,use_fast=UpperCAmelCase_ )
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
_lowercase : List[str] = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() ,tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.image_processor ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = CLIPProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_lowercase : Union[str, Any] = self.get_tokenizer(bos_token="""(BOS)""" ,eos_token="""(EOS)""" )
_lowercase : Optional[int] = self.get_image_processor(do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
_lowercase : int = CLIPProcessor.from_pretrained(
self.tmpdirname ,bos_token="""(BOS)""" ,eos_token="""(EOS)""" ,do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : Optional[int] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : int = self.prepare_image_inputs()
_lowercase : str = image_processor(UpperCAmelCase_ ,return_tensors="""np""" )
_lowercase : int = processor(images=UpperCAmelCase_ ,return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : Optional[Any] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : List[Any] = """lower newer"""
_lowercase : Any = processor(text=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer(UpperCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : str = """lower newer"""
_lowercase : List[Any] = self.prepare_image_inputs()
_lowercase : Optional[int] = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase_ ):
processor()
def lowerCamelCase__ ( self ):
_lowercase : Dict = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowercase : int = processor.batch_decode(UpperCAmelCase_ )
_lowercase : Tuple = tokenizer.batch_decode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : Optional[Any] = """lower newer"""
_lowercase : Any = self.prepare_image_inputs()
_lowercase : Optional[int] = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,processor.model_input_names )
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
UpperCAmelCase: str = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[datasets.Features] = None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , ):
import pyspark
def generate_fn():
_lowercase : List[Any] = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
_lowercase : Optional[int] = df_with_partition_id.select("""*""" ).where(F"""part_id = {partition_id}""" ).drop("""part_id""" )
_lowercase : int = partition_df.collect()
_lowercase : Dict = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class UpperCamelCase ( _BaseExamplesIterable ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ,):
_lowercase : Union[str, Any] = df
_lowercase : List[str] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowercase : Tuple = _generate_iterable_examples(self.df ,self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.split_shard_indices_by_worker(UpperCAmelCase_ ,UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
@property
def lowerCamelCase__ ( self ):
return len(self.partition_order )
class UpperCamelCase ( datasets.DatasetBuilder ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = SparkConfig
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
import pyspark
_lowercase : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowercase : List[Any] = df
_lowercase : int = working_dir
super().__init__(
cache_dir=UpperCAmelCase_ ,config_name=str(self.df.semanticHash() ) ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(UpperCAmelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir ,exist_ok=UpperCAmelCase_ )
_lowercase : Union[str, Any] = os.path.join(self._cache_dir ,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(UpperCAmelCase_ ,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" ,"""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowercase : List[str] = (
self._spark.sparkContext.parallelize(range(1 ) ,1 ).mapPartitions(UpperCAmelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase__ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
import pyspark
def get_arrow_batch_size(UpperCAmelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
_lowercase : List[str] = self.df.count()
_lowercase : List[str] = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowercase : Union[str, Any] = (
self.df.limit(UpperCAmelCase_ )
.repartition(1 )
.mapInArrow(UpperCAmelCase_ ,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowercase : List[Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowercase : int = min(UpperCAmelCase_ ,int(approx_total_size / max_shard_size ) )
_lowercase : List[Any] = self.df.repartition(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
import pyspark
_lowercase : Union[str, Any] = ParquetWriter if file_format == """parquet""" else ArrowWriter
_lowercase : List[Any] = os.path.join(self._working_dir ,os.path.basename(UpperCAmelCase_ ) ) if self._working_dir else fpath
_lowercase : Any = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowercase : Union[str, Any] = self.config.features
_lowercase : Optional[int] = self._writer_batch_size
_lowercase : Optional[Any] = self._fs.storage_options
def write_arrow(UpperCAmelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowercase : Any = pyspark.TaskContext().taskAttemptId()
_lowercase : List[str] = next(UpperCAmelCase_ ,UpperCAmelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
_lowercase : List[Any] = 0
_lowercase : int = writer_class(
features=UpperCAmelCase_ ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Optional[int] = pa.Table.from_batches([first_batch] )
writer.write_table(UpperCAmelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowercase , _lowercase : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
shard_id += 1
_lowercase : Union[str, Any] = writer_class(
features=writer._features ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Dict = pa.Table.from_batches([batch] )
writer.write_table(UpperCAmelCase_ )
if writer._num_bytes > 0:
_lowercase , _lowercase : Dict = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(UpperCAmelCase_ ) ):
_lowercase : Dict = os.path.join(os.path.dirname(UpperCAmelCase_ ) ,os.path.basename(UpperCAmelCase_ ) )
shutil.move(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : List[str] = (
self.df.mapInArrow(UpperCAmelCase_ ,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) ,pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) ,pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) ,pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) ,)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = "arrow" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
self._validate_cache_dir()
_lowercase : Tuple = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(UpperCAmelCase_ )
_lowercase : Optional[int] = not is_remote_filesystem(self._fs )
_lowercase : Dict = os.path.join if is_local else posixpath.join
_lowercase : int = """-TTTTT-SSSSS-of-NNNNN"""
_lowercase : Optional[Any] = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
_lowercase : Dict = path_join(self._output_dir ,UpperCAmelCase_ )
_lowercase : List[Any] = 0
_lowercase : Optional[Any] = 0
_lowercase : int = 0
_lowercase : Any = []
_lowercase : Any = []
for task_id, content in self._prepare_split_single(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : Tuple = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(UpperCAmelCase_ )
_lowercase : Optional[int] = total_num_examples
_lowercase : List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
_lowercase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowercase : Union[str, Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
rename(
UpperCAmelCase_ ,fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace("""TTTTT-SSSSS""" ,f"""{global_shard_id:05d}""" ).replace("""NNNNN""" ,f"""{total_shards:05d}""" ) ,)
_lowercase : Optional[Any] = []
_lowercase : List[str] = 0
for i in range(len(UpperCAmelCase_ ) ):
_lowercase , _lowercase : List[str] = task_id_and_num_shards[i]
for shard_id in range(UpperCAmelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(UpperCAmelCase_ ,len(UpperCAmelCase_ ) ).map(lambda UpperCAmelCase_ : _rename_shard(*UpperCAmelCase_ ) ).collect()
else:
# don't use any pattern
_lowercase : Tuple = 0
_lowercase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace(UpperCAmelCase_ ,"""""" ) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,):
return SparkExamplesIterable(self.df )
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=13 ,UpperCAmelCase_=30 ,UpperCAmelCase_=2 ,UpperCAmelCase_=3 ,UpperCAmelCase_=True ,UpperCAmelCase_=True ,UpperCAmelCase_=32 ,UpperCAmelCase_=2 ,UpperCAmelCase_=4 ,UpperCAmelCase_=37 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=10 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=3 ,UpperCAmelCase_=0.6 ,UpperCAmelCase_=None ,):
_lowercase : Dict = parent
_lowercase : List[Any] = batch_size
_lowercase : List[str] = image_size
_lowercase : Any = patch_size
_lowercase : Union[str, Any] = num_channels
_lowercase : Optional[int] = is_training
_lowercase : int = use_labels
_lowercase : List[str] = hidden_size
_lowercase : Optional[Any] = num_hidden_layers
_lowercase : List[Any] = num_attention_heads
_lowercase : List[Any] = intermediate_size
_lowercase : List[Any] = hidden_act
_lowercase : List[Any] = hidden_dropout_prob
_lowercase : int = attention_probs_dropout_prob
_lowercase : Union[str, Any] = type_sequence_label_size
_lowercase : Tuple = initializer_range
_lowercase : Optional[int] = mask_ratio
_lowercase : List[str] = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
_lowercase : str = (image_size // patch_size) ** 2
_lowercase : Union[str, Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCamelCase__ ( self ):
_lowercase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowercase : str = None
if self.use_labels:
_lowercase : List[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_lowercase : Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
return ViTMAEConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,decoder_hidden_size=self.hidden_size ,decoder_num_hidden_layers=self.num_hidden_layers ,decoder_num_attention_heads=self.num_attention_heads ,decoder_intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=UpperCAmelCase_ ,initializer_range=self.initializer_range ,mask_ratio=self.mask_ratio ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Any = TFViTMAEModel(config=UpperCAmelCase_ )
_lowercase : Tuple = model(UpperCAmelCase_ ,training=UpperCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : int = TFViTMAEForPreTraining(UpperCAmelCase_ )
_lowercase : Any = model(UpperCAmelCase_ ,training=UpperCAmelCase_ )
# expected sequence length = num_patches
_lowercase : Optional[int] = (self.image_size // self.patch_size) ** 2
_lowercase : Optional[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
_lowercase : List[Any] = 1
_lowercase : List[Any] = TFViTMAEForPreTraining(UpperCAmelCase_ )
_lowercase : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowercase : Tuple = model(UpperCAmelCase_ ,training=UpperCAmelCase_ )
_lowercase : Optional[Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
def lowerCamelCase__ ( self ):
_lowercase : int = self.prepare_config_and_inputs()
((_lowercase) , (_lowercase) , (_lowercase)) : Union[str, Any] = config_and_inputs
_lowercase : Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : Optional[Any] = {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : List[str] = False
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : int = False
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = TFViTMAEModelTester(self )
_lowercase : Optional[int] = ConfigTester(self ,config_class=UpperCAmelCase_ ,has_text_modality=UpperCAmelCase_ ,hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : str = model_class(UpperCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() ,(tf.keras.layers.Layer) )
_lowercase : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase_ ,tf.keras.layers.Layer ) )
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : List[str] = model_class(UpperCAmelCase_ )
_lowercase : int = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase : Optional[Any] = [*signature.parameters.keys()]
_lowercase : Any = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
# make the mask reproducible
np.random.seed(2 )
_lowercase , _lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
_lowercase : Union[str, Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
_lowercase : int = model_class(UpperCAmelCase_ )
_lowercase : str = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Dict = model(UpperCAmelCase_ ,noise=UpperCAmelCase_ )
_lowercase : str = copy.deepcopy(self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ ) )
_lowercase : str = model(**UpperCAmelCase_ ,noise=UpperCAmelCase_ )
_lowercase : List[str] = outputs_dict[0].numpy()
_lowercase : Optional[int] = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) ,1E-6 )
def lowerCamelCase__ ( self ):
# make the mask reproducible
np.random.seed(2 )
_lowercase , _lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
_lowercase : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(UpperCAmelCase_ ):
_lowercase : List[Any] = {}
for k, v in inputs_dict.items():
if tf.is_tensor(UpperCAmelCase_ ):
_lowercase : Union[str, Any] = v.numpy()
else:
_lowercase : Optional[int] = np.array(UpperCAmelCase_ )
return inputs_np_dict
for model_class in self.all_model_classes:
_lowercase : Optional[int] = model_class(UpperCAmelCase_ )
_lowercase : List[Any] = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : Optional[Any] = prepare_numpy_arrays(UpperCAmelCase_ )
_lowercase : List[Any] = model(UpperCAmelCase_ ,noise=UpperCAmelCase_ )
_lowercase : Optional[int] = model(**UpperCAmelCase_ ,noise=UpperCAmelCase_ )
self.assert_outputs_same(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
# make masks reproducible
np.random.seed(2 )
_lowercase : Union[str, Any] = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
_lowercase : Tuple = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
_lowercase : Optional[int] = tf.constant(UpperCAmelCase_ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
_lowercase : int = tf_noise
super().check_pt_tf_models(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
# make mask reproducible
np.random.seed(2 )
_lowercase , _lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : str = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(UpperCAmelCase_ )
if module_member_name.endswith("""MainLayer""" )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("""MainLayer""" )] == model_class.__name__[: -len("""Model""" )]
for module_member in (getattr(UpperCAmelCase_ ,UpperCAmelCase_ ),)
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(UpperCAmelCase_ ,"""_keras_serializable""" ,UpperCAmelCase_ )
}
_lowercase : List[str] = int((config.image_size // config.patch_size) ** 2 )
_lowercase : List[str] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
_lowercase : Dict = tf.convert_to_tensor(UpperCAmelCase_ )
inputs_dict.update({"""noise""": noise} )
for main_layer_class in tf_main_layer_classes:
_lowercase : Tuple = main_layer_class(UpperCAmelCase_ )
_lowercase : int = {
name: tf.keras.Input(tensor.shape[1:] ,dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
_lowercase : List[str] = tf.keras.Model(UpperCAmelCase_ ,outputs=main_layer(UpperCAmelCase_ ) )
_lowercase : str = model(UpperCAmelCase_ )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowercase : Optional[Any] = os.path.join(UpperCAmelCase_ ,"""keras_model.h5""" )
model.save(UpperCAmelCase_ )
_lowercase : Optional[int] = tf.keras.models.load_model(
UpperCAmelCase_ ,custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(UpperCAmelCase_ ,tf.keras.Model )
_lowercase : Optional[Any] = model(UpperCAmelCase_ )
self.assert_outputs_same(UpperCAmelCase_ ,UpperCAmelCase_ )
@slow
def lowerCamelCase__ ( self ):
# make mask reproducible
np.random.seed(2 )
_lowercase , _lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : Tuple = int((config.image_size // config.patch_size) ** 2 )
_lowercase : Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
_lowercase : Optional[Any] = model_class(UpperCAmelCase_ )
_lowercase : str = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : str = model(UpperCAmelCase_ ,noise=UpperCAmelCase_ )
if model_class.__name__ == "TFViTMAEModel":
_lowercase : Union[str, Any] = outputs.last_hidden_state.numpy()
_lowercase : int = 0
else:
_lowercase : Union[str, Any] = outputs.logits.numpy()
_lowercase : Union[str, Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCAmelCase_ ,saved_model=UpperCAmelCase_ )
_lowercase : Union[str, Any] = model_class.from_pretrained(UpperCAmelCase_ )
_lowercase : Union[str, Any] = model(UpperCAmelCase_ ,noise=UpperCAmelCase_ )
if model_class.__name__ == "TFViTMAEModel":
_lowercase : Any = after_outputs["""last_hidden_state"""].numpy()
_lowercase : Dict = 0
else:
_lowercase : Dict = after_outputs["""logits"""].numpy()
_lowercase : str = 0
_lowercase : int = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(UpperCAmelCase_ ,1E-5 )
def lowerCamelCase__ ( self ):
# make mask reproducible
np.random.seed(2 )
_lowercase , _lowercase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : Any = int((config.image_size // config.patch_size) ** 2 )
_lowercase : Any = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
_lowercase : int = model_class(UpperCAmelCase_ )
_lowercase : Optional[Any] = self._prepare_for_class(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : int = model(UpperCAmelCase_ ,noise=UpperCAmelCase_ )
_lowercase : Optional[int] = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(UpperCAmelCase_ )
_lowercase : Tuple = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
_lowercase : Any = model_class.from_config(model.config )
_lowercase : List[str] = new_model(UpperCAmelCase_ ) # Build model
new_model.set_weights(model.get_weights() )
_lowercase : int = new_model(UpperCAmelCase_ ,noise=UpperCAmelCase_ )
self.assert_outputs_same(UpperCAmelCase_ ,UpperCAmelCase_ )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def lowerCamelCase__ ( self ):
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def lowerCamelCase__ ( self ):
pass
@slow
def lowerCamelCase__ ( self ):
_lowercase : Any = TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(UpperCAmelCase_ )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Any = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCamelCase__ ( self ):
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def lowerCamelCase__ ( self ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
_lowercase : Any = TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" )
_lowercase : Optional[int] = self.default_image_processor
_lowercase : Optional[int] = prepare_img()
_lowercase : List[str] = image_processor(images=UpperCAmelCase_ ,return_tensors="""tf""" )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
_lowercase : Tuple = ViTMAEConfig()
_lowercase : Any = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
_lowercase : Union[str, Any] = np.random.uniform(size=(1, num_patches) )
# forward pass
_lowercase : Union[str, Any] = model(**UpperCAmelCase_ ,noise=UpperCAmelCase_ )
# verify the logits
_lowercase : List[str] = tf.convert_to_tensor([1, 1_96, 7_68] )
self.assertEqual(outputs.logits.shape ,UpperCAmelCase_ )
_lowercase : int = tf.convert_to_tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] ,UpperCAmelCase_ ,atol=1E-4 )
| 336 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Optional[int] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = XLNetTokenizer
SCREAMING_SNAKE_CASE_ : Dict = XLNetTokenizerFast
SCREAMING_SNAKE_CASE_ : int = True
SCREAMING_SNAKE_CASE_ : Union[str, Any] = True
def lowerCamelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowercase : Dict = XLNetTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = """<s>"""
_lowercase : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<unk>""" )
self.assertEqual(vocab_keys[1] ,"""<s>""" )
self.assertEqual(vocab_keys[-1] ,"""<eod>""" )
self.assertEqual(len(UpperCAmelCase_ ) ,10_06 )
def lowerCamelCase__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size ,10_00 )
def lowerCamelCase__ ( self ):
_lowercase : int = XLNetTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCAmelCase_ ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) ,[2_85, 46, 10, 1_70, 3_82] )
_lowercase : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] ,)
_lowercase : List[Any] = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
_lowercase : Optional[Any] = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] ,)
def lowerCamelCase__ ( self ):
_lowercase : Dict = XLNetTokenizer(UpperCAmelCase_ ,do_lower_case=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """""",
"""i""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] ,)
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,["""▁he""", """ll""", """o"""] )
def lowerCamelCase__ ( self ):
_lowercase : int = XLNetTokenizer(UpperCAmelCase_ ,do_lower_case=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] ,)
@slow
def lowerCamelCase__ ( self ):
_lowercase : Dict = XLNetTokenizer.from_pretrained("""xlnet-base-cased""" )
_lowercase : int = tokenizer.encode("""sequence builders""" ,add_special_tokens=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.encode("""multi-sequence build""" ,add_special_tokens=UpperCAmelCase_ )
_lowercase : Optional[int] = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ )
_lowercase : Dict = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ ,UpperCAmelCase_ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def lowerCamelCase__ ( self ):
# fmt: off
_lowercase : Union[str, Any] = {"""input_ids""": [[17, 2_14_42, 2_70, 17, 10, 1_46_45, 3_18, 34, 17, 45_46, 31_45, 7_87, 13, 77_52, 2_20_18, 23, 21, 17, 45_46, 31_45, 7_87, 13, 33_52, 1_44_31, 13, 55_00, 11, 11_76, 5_80, 13, 1_68_19, 47_97, 23, 17, 10, 1_71_35, 6_58, 19, 4_57, 79_32, 13, 1_84, 19, 31_54, 1_71_35, 64_68, 19, 14_04, 1_22_69, 19, 42_29, 53_56, 1_62_64, 46, 19, 17, 2_05_45, 1_03_95, 9, 9, 9, 11, 28, 64_21, 95_31, 2_07_29, 17, 10, 3_53, 1_70_22, 11, 21, 64_21, 95_31, 1_69_49, 17, 10, 1_15_09, 7_53, 11, 33, 95, 24_21, 73_85, 9_56, 1_44_31, 26_26, 25, 8_42, 73_85, 48_36, 21, 14_29, 22_72, 98_55, 31_20, 1_61, 2_47_38, 19, 1_32_03, 6_58, 2_18, 7_87, 21, 4_30, 1_84_82, 8_47, 26_37, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_22, 2_21_78, 27, 10_64, 22, 9_56, 13, 1_11_01, 14_29, 58_54, 2_43_13, 1_89_53, 40, 4_22, 2_43_66, 68, 17_58, 37, 1_04_83, 1_42_57, 31, 2_07, 2_63, 21, 2_03, 37_73, 25, 71, 97_35, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 20_49, 34_42, 17, 1_38_94, 33_80, 23, 95, 18, 1_76_34, 22_88, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ ,model_name="""xlnet-base-cased""" ,revision="""c841166438c31ec7ca9a106dee7bb312b73ae511""" ,)
| 336 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase: Optional[int] = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase: str = ["""ConvNextFeatureExtractor"""]
UpperCAmelCase: Union[str, Any] = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase: List[str] = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase: Optional[Any] = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
UpperCAmelCase: Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
_lowercase : Union[str, Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : int = []
for line in lines:
_lowercase : Dict = re.sub(R"""#.*""" , """""" , __UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(__UpperCAmelCase )
_lowercase : Tuple = """\n""".join(__UpperCAmelCase )
# Make a hash from all this code
_lowercase : Tuple = full_str.encode("""utf-8""" )
return shaaaa(__UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCAmelCase: Tuple = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCAmelCase: List[str] = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase: Any = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
UpperCAmelCase: Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[int] = 0
_lowercase : Tuple = len(__UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
_lowercase : List[str] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__UpperCAmelCase ):
return None
_lowercase : Optional[Any] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
_lowercase : Any = left
_lowercase : Optional[Any] = point
elif point > right:
_lowercase : str = right
_lowercase : List[str] = point
else:
if item < current_item:
_lowercase : Tuple = point - 1
else:
_lowercase : Any = point + 1
return None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
_lowercase : List[Any] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
__UpperCAmelCase , __UpperCAmelCase , point + 1 , __UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if collection != sorted(__UpperCAmelCase ):
raise ValueError("""Collection must be ascending sorted""" )
return True
if __name__ == "__main__":
import sys
UpperCAmelCase: List[Any] = 0
if debug == 1:
UpperCAmelCase: int = [10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("""Sequence must be ascending sorted to apply interpolation search""")
UpperCAmelCase: Optional[Any] = 67
UpperCAmelCase: int = interpolation_search(collection, target)
if result is not None:
print(F'{target} found at positions: {result}')
else:
print("""Not found""")
| 336 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int
SCREAMING_SNAKE_CASE_ : int
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ):
_lowercase : list[list[Edge]] = [[] for _ in range(UpperCAmelCase_ )]
_lowercase : Any = size
def __getitem__( self ,UpperCAmelCase_ ):
return iter(self._graph[vertex] )
@property
def lowerCamelCase__ ( self ):
return self._size
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(UpperCAmelCase_ ,UpperCAmelCase_ ) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = deque([start_vertex] )
_lowercase : list[int | None] = [None] * self.size
_lowercase : Dict = 0
while queue:
_lowercase : Tuple = queue.popleft()
_lowercase : Dict = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
_lowercase : Dict = current_distance + edge.weight
_lowercase : List[Any] = distances[edge.destination_vertex]
if (
isinstance(UpperCAmelCase_ ,UpperCAmelCase_ )
and new_distance >= dest_vertex_distance
):
continue
_lowercase : Optional[int] = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCAmelCase: Any = generate_large_matrix()
UpperCAmelCase: Dict = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
assert all(row == sorted(__UpperCAmelCase , reverse=__UpperCAmelCase ) for row in grid )
assert all(list(__UpperCAmelCase ) == sorted(__UpperCAmelCase , reverse=__UpperCAmelCase ) for col in zip(*__UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = 0
_lowercase : List[Any] = len(__UpperCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
_lowercase : Tuple = (left + right) // 2
_lowercase : List[Any] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
_lowercase : Dict = mid + 1
else:
_lowercase : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Any = 0
_lowercase : Optional[int] = len(grid[0] )
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = find_negative_index(grid[i][:bound] )
total += bound
return (len(__UpperCAmelCase ) * len(grid[0] )) - total
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return len([number for row in grid for number in row if number < 0] )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = 0
for row in grid:
for i, number in enumerate(__UpperCAmelCase ):
if number < 0:
total += len(__UpperCAmelCase ) - i
break
return total
def __SCREAMING_SNAKE_CASE ( ):
from timeit import timeit
print("""Running benchmarks""" )
_lowercase : Tuple = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
_lowercase : Dict = timeit(F"""{func}(grid=grid)""" , setup=__UpperCAmelCase , number=500 )
print(F"""{func}() took {time:0.4f} seconds""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 336 | 1 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
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_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
UpperCAmelCase: str = logging.get_logger(__name__)
@add_end_docstrings(snake_case )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def __init__( self ,**UpperCAmelCase_ ):
super().__init__(**UpperCAmelCase_ )
requires_backends(self ,"""vision""" )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
return super().__call__(UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
_lowercase : Union[str, Any] = {}
if "candidate_labels" in kwargs:
_lowercase : Optional[int] = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
_lowercase : Optional[Any] = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ,UpperCAmelCase_="This is a photo of {}." ):
_lowercase : Dict = load_image(UpperCAmelCase_ )
_lowercase : Union[str, Any] = self.image_processor(images=[image] ,return_tensors=self.framework )
_lowercase : Dict = candidate_labels
_lowercase : List[Any] = [hypothesis_template.format(UpperCAmelCase_ ) for x in candidate_labels]
_lowercase : Optional[int] = self.tokenizer(UpperCAmelCase_ ,return_tensors=self.framework ,padding=UpperCAmelCase_ )
_lowercase : Dict = [text_inputs]
return inputs
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : Optional[int] = model_inputs.pop("""candidate_labels""" )
_lowercase : Tuple = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] ,UpperCAmelCase_ ):
_lowercase : List[Any] = text_inputs[0]
else:
# Batching case.
_lowercase : List[Any] = text_inputs[0][0]
_lowercase : Tuple = self.model(**UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Union[str, Any] = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_image,
}
return model_outputs
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : int = model_outputs.pop("""candidate_labels""" )
_lowercase : Tuple = model_outputs["""logits"""][0]
if self.framework == "pt":
_lowercase : str = logits.softmax(dim=-1 ).squeeze(-1 )
_lowercase : Optional[Any] = probs.tolist()
if not isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[int] = [scores]
elif self.framework == "tf":
_lowercase : List[Any] = stable_softmax(UpperCAmelCase_ ,axis=-1 )
_lowercase : Optional[Any] = probs.numpy().tolist()
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
_lowercase : List[Any] = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(UpperCAmelCase_ ,UpperCAmelCase_ ) ,key=lambda UpperCAmelCase_ : -x[0] )
]
return result
| 336 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase: List[str] = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase: int = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
re.sub("""<n>""" , """""" , __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not postfix_notation:
return 0
_lowercase : Optional[Any] = {"""+""", """-""", """*""", """/"""}
_lowercase : list[Any] = []
for token in postfix_notation:
if token in operations:
_lowercase , _lowercase : Any = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(__UpperCAmelCase ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=0.9_9_9 , __UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_lowercase : str = []
for i in range(__UpperCAmelCase ):
_lowercase : Any = i / num_diffusion_timesteps
_lowercase : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ) , __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
class UpperCamelCase ( snake_case , snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [e.name for e in KarrasDiffusionSchedulers]
SCREAMING_SNAKE_CASE_ : str = 2
@register_to_config
def __init__( self ,UpperCAmelCase_ = 10_00 ,UpperCAmelCase_ = 0.00085 ,UpperCAmelCase_ = 0.012 ,UpperCAmelCase_ = "linear" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = "epsilon" ,UpperCAmelCase_ = "linspace" ,UpperCAmelCase_ = 0 ,):
if trained_betas is not None:
_lowercase : str = torch.tensor(UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
_lowercase : Optional[Any] = torch.linspace(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_lowercase : Any = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,UpperCAmelCase_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_lowercase : Any = betas_for_alpha_bar(UpperCAmelCase_ )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
_lowercase : Tuple = 1.0 - self.betas
_lowercase : Dict = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ):
if schedule_timesteps is None:
_lowercase : Optional[int] = self.timesteps
_lowercase : Union[str, Any] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_lowercase : Optional[Any] = 1 if len(UpperCAmelCase_ ) > 1 else 0
else:
_lowercase : Dict = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
_lowercase : List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCamelCase__ ( self ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
_lowercase : str = self.index_for_timestep(UpperCAmelCase_ )
if self.state_in_first_order:
_lowercase : Optional[Any] = self.sigmas[step_index]
else:
_lowercase : Dict = self.sigmas_interpol[step_index]
_lowercase : Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,):
_lowercase : List[str] = num_inference_steps
_lowercase : Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_lowercase : Dict = np.linspace(0 ,num_train_timesteps - 1 ,UpperCAmelCase_ ,dtype=UpperCAmelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_lowercase : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : str = (np.arange(0 ,UpperCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_lowercase : str = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : Optional[int] = (np.arange(UpperCAmelCase_ ,0 ,-step_ratio )).round().copy().astype(UpperCAmelCase_ )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_lowercase : Union[str, Any] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_lowercase : Optional[Any] = torch.from_numpy(np.log(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_lowercase : List[str] = np.interp(UpperCAmelCase_ ,np.arange(0 ,len(UpperCAmelCase_ ) ) ,UpperCAmelCase_ )
_lowercase : int = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_lowercase : Any = torch.from_numpy(UpperCAmelCase_ ).to(device=UpperCAmelCase_ )
# interpolate sigmas
_lowercase : List[str] = sigmas.log().lerp(sigmas.roll(1 ).log() ,0.5 ).exp()
_lowercase : Union[str, Any] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
_lowercase : Tuple = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(UpperCAmelCase_ ).startswith("""mps""" ):
# mps does not support float64
_lowercase : Tuple = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=torch.floataa )
else:
_lowercase : str = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ )
# interpolate timesteps
_lowercase : int = self.sigma_to_t(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=timesteps.dtype )
_lowercase : Union[str, Any] = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,dim=-1 ).flatten()
_lowercase : str = torch.cat([timesteps[:1], interleaved_timesteps] )
_lowercase : List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_lowercase : Optional[Any] = defaultdict(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# get log sigma
_lowercase : Optional[Any] = sigma.log()
# get distribution
_lowercase : Optional[int] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
_lowercase : Tuple = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
_lowercase : List[Any] = low_idx + 1
_lowercase : int = self.log_sigmas[low_idx]
_lowercase : Any = self.log_sigmas[high_idx]
# interpolate sigmas
_lowercase : Any = (low - log_sigma) / (low - high)
_lowercase : Dict = w.clamp(0 ,1 )
# transform interpolation to time range
_lowercase : List[str] = (1 - w) * low_idx + w * high_idx
_lowercase : Optional[int] = t.view(sigma.shape )
return t
@property
def lowerCamelCase__ ( self ):
return self.sample is None
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = True ,):
_lowercase : Optional[int] = self.index_for_timestep(UpperCAmelCase_ )
# advance index counter by 1
_lowercase : str = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_lowercase : Any = self.sigmas[step_index]
_lowercase : Any = self.sigmas_interpol[step_index + 1]
_lowercase : Tuple = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
_lowercase : Union[str, Any] = self.sigmas[step_index - 1]
_lowercase : int = self.sigmas_interpol[step_index]
_lowercase : Tuple = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_lowercase : Any = 0
_lowercase : int = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_lowercase : Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : Optional[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_lowercase : str = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_lowercase : List[str] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_lowercase : Any = sigma_interpol - sigma_hat
# store for 2nd order step
_lowercase : List[Any] = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
_lowercase : Optional[Any] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
_lowercase : Optional[Any] = sigma_next - sigma_hat
_lowercase : Any = self.sample
_lowercase : Optional[int] = None
_lowercase : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_lowercase : int = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_ ):
# mps does not support float64
_lowercase : str = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
_lowercase : Any = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
_lowercase : List[Any] = self.timesteps.to(original_samples.device )
_lowercase : Union[str, Any] = timesteps.to(original_samples.device )
_lowercase : List[Any] = [self.index_for_timestep(UpperCAmelCase_ ,UpperCAmelCase_ ) for t in timesteps]
_lowercase : Optional[Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_lowercase : List[Any] = sigma.unsqueeze(-1 )
_lowercase : int = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
return self.config.num_train_timesteps
| 336 | 1 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase: List[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : str = os.path.dirname(os.path.realpath(__UpperCAmelCase ) )
_lowercase : Optional[int] = os.path.join(__UpperCAmelCase , """words.txt""" )
_lowercase : Tuple = """"""
with open(__UpperCAmelCase ) as f:
_lowercase : Union[str, Any] = f.readline()
_lowercase : Any = [word.strip("""\"""" ) for word in words.strip("""\r\n""" ).split(""",""" )]
_lowercase : Any = [
word
for word in [sum(ord(__UpperCAmelCase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__UpperCAmelCase )
if __name__ == "__main__":
print(solution())
| 336 |
"""simple docstring"""
import pprint
import requests
UpperCAmelCase: Tuple = """https://zenquotes.io/api"""
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
UpperCAmelCase: int = random_quotes()
pprint.pprint(response)
| 336 | 1 |
"""simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCamelCase :
"""simple docstring"""
@property
def lowerCamelCase__ ( self ):
return self.get_dummy_input()
@property
def lowerCamelCase__ ( self ):
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )
def lowerCamelCase__ ( self ,UpperCAmelCase_=True ,UpperCAmelCase_=False ,UpperCAmelCase_=False ,UpperCAmelCase_=False ,):
_lowercase : Tuple = 4
_lowercase : str = 32
_lowercase : List[str] = (32, 32)
_lowercase : str = torch.manual_seed(0 )
_lowercase : str = torch.device(UpperCAmelCase_ )
_lowercase : int = (batch_size, num_channels) + sizes
_lowercase : Tuple = randn_tensor(UpperCAmelCase_ ,generator=UpperCAmelCase_ ,device=UpperCAmelCase_ )
_lowercase : Any = {"""hidden_states""": hidden_states}
if include_temb:
_lowercase : Optional[Any] = 1_28
_lowercase : List[Any] = randn_tensor((batch_size, temb_channels) ,generator=UpperCAmelCase_ ,device=UpperCAmelCase_ )
if include_res_hidden_states_tuple:
_lowercase : Optional[int] = torch.manual_seed(1 )
_lowercase : Tuple = (randn_tensor(UpperCAmelCase_ ,generator=UpperCAmelCase_ ,device=UpperCAmelCase_ ),)
if include_encoder_hidden_states:
_lowercase : Tuple = floats_tensor((batch_size, 32, 32) ).to(UpperCAmelCase_ )
if include_skip_sample:
_lowercase : Union[str, Any] = randn_tensor(((batch_size, 3) + sizes) ,generator=UpperCAmelCase_ ,device=UpperCAmelCase_ )
return dummy_input
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = {
"""in_channels""": 32,
"""out_channels""": 32,
"""temb_channels""": 1_28,
}
if self.block_type == "up":
_lowercase : List[Any] = 32
if self.block_type == "mid":
init_dict.pop("""out_channels""" )
_lowercase : str = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase , _lowercase : Any = self.prepare_init_args_and_inputs_for_common()
_lowercase : Dict = self.block_class(**UpperCAmelCase_ )
unet_block.to(UpperCAmelCase_ )
unet_block.eval()
with torch.no_grad():
_lowercase : int = unet_block(**UpperCAmelCase_ )
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : int = output[0]
self.assertEqual(output.shape ,self.output_shape )
_lowercase : Optional[Any] = output[0, -1, -3:, -3:]
_lowercase : List[Any] = torch.tensor(UpperCAmelCase_ ).to(UpperCAmelCase_ )
assert torch_all_close(output_slice.flatten() ,UpperCAmelCase_ ,atol=5E-3 )
@unittest.skipIf(torch_device == """mps""" ,"""Training is not supported in mps""" )
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Dict = self.prepare_init_args_and_inputs_for_common()
_lowercase : Optional[int] = self.block_class(**UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.train()
_lowercase : Any = model(**UpperCAmelCase_ )
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[Any] = output[0]
_lowercase : Union[str, Any] = torch.device(UpperCAmelCase_ )
_lowercase : Any = randn_tensor(output.shape ,device=UpperCAmelCase_ )
_lowercase : Optional[Any] = torch.nn.functional.mse_loss(UpperCAmelCase_ ,UpperCAmelCase_ )
loss.backward()
| 336 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : int
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(__UpperCAmelCase ) )]
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
_lowercase : Tuple = all_rotations(__UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_lowercase : BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(__UpperCAmelCase ),
}
return response
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
_lowercase : Optional[Any] = int(__UpperCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(__UpperCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
_lowercase : int = [""""""] * len(__UpperCAmelCase )
for _ in range(len(__UpperCAmelCase ) ):
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = """Provide a string that I will generate its BWT transform: """
UpperCAmelCase: int = input(entry_msg).strip()
UpperCAmelCase: List[str] = bwt_transform(s)
print(
F'Burrows Wheeler transform for string \'{s}\' results '
F'in \'{result["bwt_string"]}\''
)
UpperCAmelCase: Union[str, Any] = reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
print(
F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
F'we get original string \'{original_string}\''
)
| 336 | 1 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 336 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = [randint(-1000 , 1000 ) for i in range(10 )]
_lowercase : Tuple = randint(-5000 , 5000 )
return (arr, r)
UpperCAmelCase: int = make_dataset()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
for triplet in permutations(__UpperCAmelCase , 3 ):
if sum(__UpperCAmelCase ) == target:
return tuple(sorted(__UpperCAmelCase ) )
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
arr.sort()
_lowercase : Optional[Any] = len(__UpperCAmelCase )
for i in range(n - 1 ):
_lowercase , _lowercase : str = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Tuple = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
_lowercase : Union[str, Any] = """
triplet_sum1(*dataset)
"""
_lowercase : Union[str, Any] = """
triplet_sum2(*dataset)
"""
_lowercase : Dict = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
_lowercase : Any = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
return (min(__UpperCAmelCase ), min(__UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase: Any = solution_times()
print(F'The time for naive implementation is {times[0]}.')
print(F'The time for optimized implementation is {times[1]}.')
| 336 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Any = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
UpperCAmelCase: List[Any] = 256_047
UpperCAmelCase: List[Any] = 256_145
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = NllbTokenizer
SCREAMING_SNAKE_CASE_ : Tuple = NllbTokenizerFast
SCREAMING_SNAKE_CASE_ : List[str] = True
SCREAMING_SNAKE_CASE_ : List[str] = True
SCREAMING_SNAKE_CASE_ : List[str] = {}
def lowerCamelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowercase : List[Any] = NllbTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = NllbTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
_lowercase : int = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCAmelCase_ ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) ,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,)
_lowercase : List[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] ,)
_lowercase : Union[str, Any] = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ ,[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] ,)
_lowercase : Union[str, Any] = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] ,)
def lowerCamelCase__ ( self ):
_lowercase : List[str] = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowercase : str = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = self.tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Tuple = tempfile.mkdtemp()
_lowercase : int = tokenizer_r.save_pretrained(UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
_lowercase : Tuple = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
# Checks everything loads correctly in the same way
_lowercase : Dict = tokenizer_r.from_pretrained(UpperCAmelCase_ )
_lowercase : Tuple = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ ,UpperCAmelCase_ ) )
shutil.rmtree(UpperCAmelCase_ )
# Save tokenizer rust, legacy_format=True
_lowercase : Dict = tempfile.mkdtemp()
_lowercase : Any = tokenizer_r.save_pretrained(UpperCAmelCase_ ,legacy_format=UpperCAmelCase_ )
_lowercase : Dict = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
# Checks everything loads correctly in the same way
_lowercase : Tuple = tokenizer_r.from_pretrained(UpperCAmelCase_ )
_lowercase : List[Any] = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ ,UpperCAmelCase_ ) )
shutil.rmtree(UpperCAmelCase_ )
# Save tokenizer rust, legacy_format=False
_lowercase : int = tempfile.mkdtemp()
_lowercase : List[str] = tokenizer_r.save_pretrained(UpperCAmelCase_ ,legacy_format=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowercase : Dict = tokenizer_r.from_pretrained(UpperCAmelCase_ )
_lowercase : int = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ ,UpperCAmelCase_ ) )
shutil.rmtree(UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
if not self.test_seqaseq:
return
_lowercase : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_lowercase : Dict = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
""" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
""" will only worsen the violence and misery for millions of people.""",
]
_lowercase : Dict = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
""" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
""" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
try:
_lowercase : Dict = tokenizer.prepare_seqaseq_batch(
src_texts=UpperCAmelCase_ ,tgt_texts=UpperCAmelCase_ ,max_length=3 ,max_target_length=10 ,return_tensors="""pt""" ,src_lang="""eng_Latn""" ,tgt_lang="""ron_Latn""" ,)
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.labels.shape[1] ,10 )
# max_target_length will default to max_length if not specified
_lowercase : Optional[Any] = tokenizer.prepare_seqaseq_batch(
UpperCAmelCase_ ,tgt_texts=UpperCAmelCase_ ,max_length=3 ,return_tensors="""pt""" )
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.labels.shape[1] ,3 )
_lowercase : List[str] = tokenizer.prepare_seqaseq_batch(
src_texts=UpperCAmelCase_ ,max_length=3 ,max_target_length=10 ,return_tensors="""pt""" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] ,3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] ,3 )
self.assertNotIn("""decoder_input_ids""" ,UpperCAmelCase_ )
@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece.""" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowercase : Any = [AddedToken("""<special>""" ,lstrip=UpperCAmelCase_ )]
_lowercase : Dict = self.rust_tokenizer_class.from_pretrained(
UpperCAmelCase_ ,additional_special_tokens=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Tuple = tokenizer_r.encode("""Hey this is a <special> token""" )
_lowercase : Any = tokenizer_r.encode("""<special>""" ,add_special_tokens=UpperCAmelCase_ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_lowercase : Optional[int] = self.rust_tokenizer_class.from_pretrained(
UpperCAmelCase_ ,additional_special_tokens=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
_lowercase : Tuple = self.tokenizer_class.from_pretrained(
UpperCAmelCase_ ,additional_special_tokens=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = tokenizer_p.encode("""Hey this is a <special> token""" )
_lowercase : Optional[Any] = tokenizer_cr.encode("""Hey this is a <special> token""" )
self.assertEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "facebook/nllb-200-distilled-600M"
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
SCREAMING_SNAKE_CASE_ : int = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
SCREAMING_SNAKE_CASE_ : List[str] = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def lowerCamelCase__ ( cls ):
_lowercase : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name ,src_lang="""eng_Latn""" ,tgt_lang="""ron_Latn""" )
_lowercase : Any = 1
return cls
def lowerCamelCase__ ( self ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""] ,25_60_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""] ,25_60_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""] ,25_60_57 )
def lowerCamelCase__ ( self ):
_lowercase : Tuple = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
self.assertIn(UpperCAmelCase_ ,self.tokenizer.all_special_ids )
# fmt: off
_lowercase : int = [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
# fmt: on
_lowercase : str = self.tokenizer.decode(UpperCAmelCase_ ,skip_special_tokens=UpperCAmelCase_ )
_lowercase : List[Any] = self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertNotIn(self.tokenizer.eos_token ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] ,UpperCAmelCase_ )
_lowercase : List[str] = 10
_lowercase : int = self.tokenizer(UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ).input_ids[0]
self.assertEqual(ids[-1] ,2 )
self.assertEqual(ids[0] ,UpperCAmelCase_ )
self.assertEqual(len(UpperCAmelCase_ ) ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) ,[25_62_03, 3] )
def lowerCamelCase__ ( self ):
_lowercase : Dict = tempfile.mkdtemp()
_lowercase : Tuple = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCAmelCase_ )
_lowercase : str = NllbTokenizer.from_pretrained(UpperCAmelCase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Dict = self.tokenizer(
self.src_text ,text_target=self.tgt_text ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=len(self.expected_src_tokens ) ,return_tensors="""pt""" ,)
_lowercase : str = shift_tokens_right(
batch["""labels"""] ,self.tokenizer.pad_token_id ,self.tokenizer.lang_code_to_id["""ron_Latn"""] )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual((2, 15) ,batch.input_ids.shape )
self.assertEqual((2, 15) ,batch.attention_mask.shape )
_lowercase : int = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens ,UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ ,batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens ,[EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id] )
def lowerCamelCase__ ( self ):
_lowercase : Any = self.tokenizer(self.src_text ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=3 ,return_tensors="""pt""" )
_lowercase : Union[str, Any] = self.tokenizer(
text_target=self.tgt_text ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=10 ,return_tensors="""pt""" )
_lowercase : List[str] = targets["""input_ids"""]
_lowercase : int = shift_tokens_right(
UpperCAmelCase_ ,self.tokenizer.pad_token_id ,decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] ,)
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.decoder_input_ids.shape[1] ,10 )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : int = self.tokenizer._build_translation_inputs(
"""A test""" ,return_tensors="""pt""" ,src_lang="""eng_Latn""" ,tgt_lang="""fra_Latn""" )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) ,{
# A, test, EOS, en_XX
"""input_ids""": [[25_60_47, 70, 73_56, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 25_60_57,
} ,)
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = True
_lowercase : List[Any] = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" ,src_lang="""eng_Latn""" ,tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids ,[1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47] )
_lowercase : Optional[int] = False
_lowercase : str = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" ,src_lang="""eng_Latn""" ,tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids ,[25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2] )
| 336 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE_ : Optional[int] = "BlipImageProcessor"
SCREAMING_SNAKE_CASE_ : Tuple = "AutoTokenizer"
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
super().__init__(UpperCAmelCase_ ,UpperCAmelCase_ )
# add QFormer tokenizer
_lowercase : Optional[int] = qformer_tokenizer
def __call__( self ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = True ,UpperCAmelCase_ = False ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
_lowercase : List[Any] = BatchFeature()
if text is not None:
_lowercase : List[str] = self.tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
encoding.update(UpperCAmelCase_ )
_lowercase : Dict = self.qformer_tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
_lowercase : str = qformer_text_encoding.pop("""input_ids""" )
_lowercase : int = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
_lowercase : Optional[Any] = self.image_processor(UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ )
encoding.update(UpperCAmelCase_ )
return encoding
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.tokenizer.model_input_names
_lowercase : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
if os.path.isfile(UpperCAmelCase_ ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(UpperCAmelCase_ ,exist_ok=UpperCAmelCase_ )
_lowercase : Any = os.path.join(UpperCAmelCase_ ,"""qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(UpperCAmelCase_ )
return super().save_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
_lowercase : Optional[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase_ ,subfolder="""qformer_tokenizer""" )
_lowercase : Any = cls._get_arguments_from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
args.append(UpperCAmelCase_ )
return cls(*UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase: Any = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase: int = [
"""ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EncodecModel""",
"""EncodecPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
UpperCAmelCase: Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 336 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase: Tuple = logging.get_logger(__name__)
UpperCAmelCase: List[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "trajectory_transformer"
SCREAMING_SNAKE_CASE_ : Optional[int] = ["past_key_values"]
SCREAMING_SNAKE_CASE_ : Tuple = {
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self ,UpperCAmelCase_=1_00 ,UpperCAmelCase_=5 ,UpperCAmelCase_=1 ,UpperCAmelCase_=1 ,UpperCAmelCase_=2_49 ,UpperCAmelCase_=6 ,UpperCAmelCase_=17 ,UpperCAmelCase_=25 ,UpperCAmelCase_=4 ,UpperCAmelCase_=4 ,UpperCAmelCase_=1_28 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.0006 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=1 ,UpperCAmelCase_=True ,UpperCAmelCase_=1 ,UpperCAmelCase_=5_02_56 ,UpperCAmelCase_=5_02_56 ,**UpperCAmelCase_ ,):
_lowercase : Dict = vocab_size
_lowercase : List[str] = action_weight
_lowercase : int = reward_weight
_lowercase : List[Any] = value_weight
_lowercase : List[str] = max_position_embeddings
_lowercase : Any = block_size
_lowercase : Any = action_dim
_lowercase : List[str] = observation_dim
_lowercase : Union[str, Any] = transition_dim
_lowercase : str = learning_rate
_lowercase : Tuple = n_layer
_lowercase : Optional[int] = n_head
_lowercase : List[str] = n_embd
_lowercase : List[str] = embd_pdrop
_lowercase : Optional[Any] = attn_pdrop
_lowercase : List[Any] = resid_pdrop
_lowercase : str = initializer_range
_lowercase : Optional[Any] = layer_norm_eps
_lowercase : List[Any] = kaiming_initializer_range
_lowercase : List[Any] = use_cache
super().__init__(pad_token_id=UpperCAmelCase_ ,bos_token_id=UpperCAmelCase_ ,eos_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
UpperCAmelCase: List[str] = """"""
UpperCAmelCase: Optional[int] = """"""
UpperCAmelCase: int = """"""
UpperCAmelCase: Union[str, Any] = """"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
# authorize twitter, initialize tweepy
_lowercase : Dict = tweepy.OAuthHandler(__UpperCAmelCase , __UpperCAmelCase )
auth.set_access_token(__UpperCAmelCase , __UpperCAmelCase )
_lowercase : Union[str, Any] = tweepy.API(__UpperCAmelCase )
# initialize a list to hold all the tweepy Tweets
_lowercase : Union[str, Any] = []
# make initial request for most recent tweets (200 is the maximum allowed count)
_lowercase : Union[str, Any] = api.user_timeline(screen_name=__UpperCAmelCase , count=200 )
# save most recent tweets
alltweets.extend(__UpperCAmelCase )
# save the id of the oldest tweet less one
_lowercase : Tuple = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__UpperCAmelCase ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
_lowercase : Any = api.user_timeline(
screen_name=__UpperCAmelCase , count=200 , max_id=__UpperCAmelCase )
# save most recent tweets
alltweets.extend(__UpperCAmelCase )
# update the id of the oldest tweet less one
_lowercase : List[str] = alltweets[-1].id - 1
print(F"""...{len(__UpperCAmelCase )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
_lowercase : str = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , """w""" ) as f:
_lowercase : Any = csv.writer(__UpperCAmelCase )
writer.writerow(["""id""", """created_at""", """text"""] )
writer.writerows(__UpperCAmelCase )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets("""FirePing32""")
| 336 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase: Any = logging.get_logger(__name__)
UpperCAmelCase: List[str] = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = "instructblip_vision_model"
def __init__( self ,UpperCAmelCase_=14_08 ,UpperCAmelCase_=61_44 ,UpperCAmelCase_=39 ,UpperCAmelCase_=16 ,UpperCAmelCase_=2_24 ,UpperCAmelCase_=14 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=1E-6 ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=1E-10 ,UpperCAmelCase_=True ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : Optional[Any] = hidden_size
_lowercase : Tuple = intermediate_size
_lowercase : List[Any] = num_hidden_layers
_lowercase : Tuple = num_attention_heads
_lowercase : Optional[Any] = patch_size
_lowercase : Optional[Any] = image_size
_lowercase : Union[str, Any] = initializer_range
_lowercase : Optional[Any] = attention_dropout
_lowercase : List[Any] = layer_norm_eps
_lowercase : Optional[int] = hidden_act
_lowercase : Tuple = qkv_bias
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : List[Any] = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : int = 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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "instructblip_qformer"
def __init__( self ,UpperCAmelCase_=3_05_22 ,UpperCAmelCase_=7_68 ,UpperCAmelCase_=12 ,UpperCAmelCase_=12 ,UpperCAmelCase_=30_72 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=0 ,UpperCAmelCase_="absolute" ,UpperCAmelCase_=2 ,UpperCAmelCase_=14_08 ,**UpperCAmelCase_ ,):
super().__init__(pad_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : List[Any] = vocab_size
_lowercase : List[Any] = hidden_size
_lowercase : str = num_hidden_layers
_lowercase : List[str] = num_attention_heads
_lowercase : Optional[Any] = hidden_act
_lowercase : int = intermediate_size
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Optional[Any] = attention_probs_dropout_prob
_lowercase : List[Any] = max_position_embeddings
_lowercase : Tuple = initializer_range
_lowercase : Optional[int] = layer_norm_eps
_lowercase : Any = position_embedding_type
_lowercase : Dict = cross_attention_frequency
_lowercase : Optional[Any] = encoder_hidden_size
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : Dict = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : str = config_dict["""qformer_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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "instructblip"
SCREAMING_SNAKE_CASE_ : List[str] = True
def __init__( self ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=32 ,**UpperCAmelCase_ ):
super().__init__(**UpperCAmelCase_ )
if vision_config is None:
_lowercase : str = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
_lowercase : Any = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
_lowercase : Optional[int] = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
_lowercase : int = InstructBlipVisionConfig(**UpperCAmelCase_ )
_lowercase : Optional[int] = InstructBlipQFormerConfig(**UpperCAmelCase_ )
_lowercase : Dict = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
_lowercase : str = CONFIG_MAPPING[text_model_type](**UpperCAmelCase_ )
_lowercase : str = self.text_config.tie_word_embeddings
_lowercase : Union[str, Any] = self.text_config.is_encoder_decoder
_lowercase : List[str] = num_query_tokens
_lowercase : List[str] = self.vision_config.hidden_size
_lowercase : Dict = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowercase : Union[str, Any] = 1.0
_lowercase : Dict = 0.02
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_lowercase : int = self.vision_config.to_dict()
_lowercase : Any = self.qformer_config.to_dict()
_lowercase : Any = self.text_config.to_dict()
_lowercase : Optional[int] = self.__class__.model_type
return output
| 336 | 1 |
"""simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase: Tuple = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
UpperCAmelCase: Optional[int] = logging.get_logger(__name__)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = "mask2former"
SCREAMING_SNAKE_CASE_ : Tuple = ["swin"]
SCREAMING_SNAKE_CASE_ : str = {"hidden_size": "hidden_dim"}
def __init__( self ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 2_56 ,UpperCAmelCase_ = 2_56 ,UpperCAmelCase_ = 2_56 ,UpperCAmelCase_ = 10_24 ,UpperCAmelCase_ = "relu" ,UpperCAmelCase_ = 6 ,UpperCAmelCase_ = 10 ,UpperCAmelCase_ = 8 ,UpperCAmelCase_ = 0.0 ,UpperCAmelCase_ = 20_48 ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 4 ,UpperCAmelCase_ = 2_55 ,UpperCAmelCase_ = 1_00 ,UpperCAmelCase_ = 0.1 ,UpperCAmelCase_ = 2.0 ,UpperCAmelCase_ = 5.0 ,UpperCAmelCase_ = 5.0 ,UpperCAmelCase_ = 1_25_44 ,UpperCAmelCase_ = 3.0 ,UpperCAmelCase_ = 0.75 ,UpperCAmelCase_ = 0.02 ,UpperCAmelCase_ = 1.0 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = [4, 8, 16, 32] ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.""" )
_lowercase : List[str] = CONFIG_MAPPING["""swin"""](
image_size=2_24 ,in_channels=3 ,patch_size=4 ,embed_dim=96 ,depths=[2, 2, 18, 2] ,num_heads=[3, 6, 12, 24] ,window_size=7 ,drop_path_rate=0.3 ,use_absolute_embeddings=UpperCAmelCase_ ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] ,)
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[int] = backbone_config.pop("""model_type""" )
_lowercase : Any = CONFIG_MAPPING[backbone_model_type]
_lowercase : Any = config_class.from_dict(UpperCAmelCase_ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
_lowercase : int = backbone_config
_lowercase : str = feature_size
_lowercase : Union[str, Any] = mask_feature_size
_lowercase : Any = hidden_dim
_lowercase : Optional[Any] = encoder_feedforward_dim
_lowercase : Union[str, Any] = activation_function
_lowercase : List[Any] = encoder_layers
_lowercase : str = decoder_layers
_lowercase : Any = num_attention_heads
_lowercase : Any = dropout
_lowercase : Optional[int] = dim_feedforward
_lowercase : Union[str, Any] = pre_norm
_lowercase : Optional[int] = enforce_input_projection
_lowercase : Tuple = common_stride
_lowercase : List[Any] = ignore_value
_lowercase : List[Any] = num_queries
_lowercase : Dict = no_object_weight
_lowercase : List[str] = class_weight
_lowercase : int = mask_weight
_lowercase : Any = dice_weight
_lowercase : str = train_num_points
_lowercase : str = oversample_ratio
_lowercase : Tuple = importance_sample_ratio
_lowercase : List[Any] = init_std
_lowercase : Optional[Any] = init_xavier_std
_lowercase : Any = use_auxiliary_loss
_lowercase : List[str] = feature_strides
_lowercase : Any = output_auxiliary_logits
_lowercase : Any = decoder_layers
super().__init__(**UpperCAmelCase_ )
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
return cls(
backbone_config=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
_lowercase : Dict = copy.deepcopy(self.__dict__ )
_lowercase : List[str] = self.backbone_config.to_dict()
_lowercase : Any = self.__class__.model_type
return output
| 336 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if k in (0.04, 0.06):
_lowercase : Optional[Any] = k
_lowercase : Optional[Any] = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ):
return str(self.k )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = cva.imread(UpperCAmelCase_ ,0 )
_lowercase , _lowercase : Dict = img.shape
_lowercase : list[list[int]] = []
_lowercase : int = img.copy()
_lowercase : List[str] = cva.cvtColor(UpperCAmelCase_ ,cva.COLOR_GRAY2RGB )
_lowercase , _lowercase : Optional[Any] = np.gradient(UpperCAmelCase_ )
_lowercase : Optional[int] = dx**2
_lowercase : Optional[Any] = dy**2
_lowercase : Optional[Any] = dx * dy
_lowercase : List[str] = 0.04
_lowercase : Optional[Any] = self.window_size // 2
for y in range(UpperCAmelCase_ ,h - offset ):
for x in range(UpperCAmelCase_ ,w - offset ):
_lowercase : Optional[Any] = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Dict = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Union[str, Any] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : int = (wxx * wyy) - (wxy**2)
_lowercase : Union[str, Any] = wxx + wyy
_lowercase : Union[str, Any] = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) ,0 )
color_img.itemset((y, x, 1) ,0 )
color_img.itemset((y, x, 2) ,2_55 )
return color_img, corner_list
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = HarrisCorner(0.04, 3)
UpperCAmelCase , UpperCAmelCase: List[Any] = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 336 | 1 |
"""simple docstring"""
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
UpperCAmelCase: Union[str, Any] = logging.getLogger(__name__)
UpperCAmelCase: List[Any] = """pytorch_model.bin"""
@dataclasses.dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = dataclasses.field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} , )
@dataclasses.dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = dataclasses.field(metadata={"help": "A csv or a json file containing the training data."} )
SCREAMING_SNAKE_CASE_ : str = dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default=snake_case , metadata={"help": "A csv or a json file containing the validation data."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default=snake_case , metadata={"help": "The name of the task to train on."} , )
SCREAMING_SNAKE_CASE_ : Optional[List[str]] = dataclasses.field(
default=snake_case , metadata={"help": "The list of labels for the task."} )
@dataclasses.dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = dataclasses.field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default="accuracy" , metadata={"help": "The evaluation metric used for the task."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default="no" , metadata={
"help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = dataclasses.field(
default=1_0 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
SCREAMING_SNAKE_CASE_ : Optional[float] = dataclasses.field(
default=0.0 , metadata={
"help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
} , )
SCREAMING_SNAKE_CASE_ : Optional[bool] = dataclasses.field(
default=snake_case , metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} , )
SCREAMING_SNAKE_CASE_ : Optional[bool] = dataclasses.field(
default=snake_case , metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} , )
SCREAMING_SNAKE_CASE_ : Optional[bool] = dataclasses.field(
default=snake_case , metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} , )
SCREAMING_SNAKE_CASE_ : Optional[float] = dataclasses.field(
default=0.0 , metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = dataclasses.field(
default=1_0_0 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = dataclasses.field(
default=snake_case , metadata={"help": "Random seed for initialization."} , )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Any = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_lowercase : Optional[Any] = 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
_lowercase : int = int(eval_result * len(__UpperCAmelCase ) )
print(__UpperCAmelCase )
_lowercase : Optional[Any] = dataset.sort("""probability""" , reverse=__UpperCAmelCase )
_lowercase : Dict = dataset.select(range(__UpperCAmelCase ) )
_lowercase : Any = dataset.remove_columns(["""label""", """probability"""] )
_lowercase : List[str] = dataset.rename_column("""prediction""" , """label""" )
_lowercase : List[str] = dataset.map(lambda __UpperCAmelCase : {"label": idalabel[example["label"]]} )
_lowercase : Optional[int] = dataset.shuffle(seed=args.seed )
_lowercase : List[Any] = 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 __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
_lowercase : Any = 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()
_lowercase : Union[str, Any] = STModelArguments(model_name_or_path=__UpperCAmelCase )
_lowercase : int = STDataArguments(train_file=__UpperCAmelCase , infer_file=__UpperCAmelCase )
_lowercase : Any = STTrainingArguments(output_dir=__UpperCAmelCase )
_lowercase : int = 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
_lowercase : Any = {}
_lowercase : Optional[Any] = 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
_lowercase : Optional[Any] = args.train_file
_lowercase : List[str] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_lowercase : Dict = args.eval_file
for key in data_files:
_lowercase : Tuple = 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:
_lowercase : int = 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...""" )
_lowercase : Any = F"""{args.output_dir}/self-train_iter-{{}}""".format
_lowercase : List[str] = 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()
_lowercase : Optional[int] = None
_lowercase : List[Any] = None
_lowercase : Dict = 0
_lowercase : Union[str, Any] = False
# Show the progress bar
_lowercase : Tuple = 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 ) ):
_lowercase : int = data_dir_format(__UpperCAmelCase )
assert os.path.exists(__UpperCAmelCase )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_lowercase : Any = os.path.join(__UpperCAmelCase , """stage-1""" )
_lowercase : Any = {
"""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} )
_lowercase : Optional[Any] = 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
_lowercase : List[str] = os.path.join(__UpperCAmelCase , """best-checkpoint""" )
_lowercase : Optional[Any] = os.path.join(__UpperCAmelCase , """stage-2""" )
# Update arguments_dict
_lowercase : Union[str, Any] = model_path
_lowercase : Optional[Any] = data_files["""train"""]
_lowercase : str = current_output_dir
_lowercase : int = 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 )
_lowercase : List[str] = iteration
_lowercase : Optional[Any] = data_dir_format(iteration + 1 )
_lowercase : Any = AutoConfig.from_pretrained(os.path.join(__UpperCAmelCase , """best-checkpoint""" ) )
_lowercase : int = config.idalabel
_lowercase : Optional[Any] = os.path.join(__UpperCAmelCase , """eval_results_best-checkpoint.json""" )
_lowercase : Tuple = os.path.join(__UpperCAmelCase , """test_results_best-checkpoint.json""" )
assert os.path.exists(__UpperCAmelCase )
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Tuple = float(json.load(__UpperCAmelCase )[args.eval_metric] )
_lowercase : str = os.path.join(__UpperCAmelCase , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(__UpperCAmelCase )
# Loading the dataset from local csv or json files.
_lowercase : str = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
_lowercase : Optional[Any] = 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()
_lowercase : Optional[int] = os.path.join(__UpperCAmelCase , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_lowercase : Union[str, Any] = eval_result
if best_iteration is None:
_lowercase : str = new_iteration
_lowercase : Any = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_lowercase : str = new_iteration
_lowercase : Union[str, Any] = new_eval_result
_lowercase : Optional[Any] = 0
else:
if new_eval_result == best_eval_result:
_lowercase : Optional[int] = new_iteration
_lowercase : Dict = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_lowercase : List[Any] = 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""" ) , )
| 336 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = LEDTokenizer
SCREAMING_SNAKE_CASE_ : List[str] = LEDTokenizerFast
SCREAMING_SNAKE_CASE_ : List[str] = True
def lowerCamelCase__ ( self ):
super().setUp()
_lowercase : Union[str, Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
_lowercase : List[Any] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
_lowercase : Dict = {"""unk_token""": """<unk>"""}
_lowercase : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
_lowercase : Any = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(UpperCAmelCase_ ,max_length=len(UpperCAmelCase_ ) ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
_lowercase : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIn("""input_ids""" ,UpperCAmelCase_ )
self.assertIn("""attention_mask""" ,UpperCAmelCase_ )
self.assertNotIn("""labels""" ,UpperCAmelCase_ )
self.assertNotIn("""decoder_attention_mask""" ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Dict = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(text_target=UpperCAmelCase_ ,max_length=32 ,padding="""max_length""" ,return_tensors="""pt""" )
self.assertEqual(32 ,targets["""input_ids"""].shape[1] )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : List[Any] = tokenizer(
["""I am a small frog""" * 10_24, """I am a small frog"""] ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = ["""A long paragraph for summarization."""]
_lowercase : Dict = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : List[str] = tokenizer(text_target=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : Union[str, Any] = inputs["""input_ids"""]
_lowercase : List[str] = targets["""input_ids"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : str = ["""Summary of the text.""", """Another summary."""]
_lowercase : Optional[int] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
_lowercase : Any = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ )
_lowercase : str = [[0] * len(UpperCAmelCase_ ) for x in encoded_output["""input_ids"""]]
_lowercase : Optional[int] = tokenizer.pad(UpperCAmelCase_ )
self.assertSequenceEqual(outputs["""global_attention_mask"""] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowercase : int = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Optional[int] = self.tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = """A, <mask> AllenNLP sentence."""
_lowercase : List[Any] = tokenizer_r.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
_lowercase : Any = tokenizer_p.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) ,sum(tokens_p["""token_type_ids"""] ) )
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) ,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) ,)
_lowercase : str = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
_lowercase : str = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
self.assertSequenceEqual(tokens_p["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(__UpperCAmelCase , 2 ) - pow(__UpperCAmelCase , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(__UpperCAmelCase , 2 ) - pow(__UpperCAmelCase , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(__UpperCAmelCase , 2 ) + pow(__UpperCAmelCase , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Any = f.readlines()
_lowercase : Optional[int] = F"""class {class_name}("""
_lowercase : List[str] = F"""{4 * " "}def {test_name}("""
_lowercase : List[Any] = F"""{8 * " "}{correct_line.split()[0]}"""
_lowercase : int = F"""{16 * " "}{correct_line.split()[0]}"""
_lowercase : str = False
_lowercase : Optional[Any] = False
_lowercase : Union[str, Any] = False
_lowercase : Any = False
_lowercase : int = 0
_lowercase : Tuple = 0
_lowercase : Union[str, Any] = []
for line in lines:
if line.startswith(__UpperCAmelCase ):
_lowercase : List[str] = True
elif in_class and line.startswith(__UpperCAmelCase ):
_lowercase : str = True
elif in_class and in_func and (line.startswith(__UpperCAmelCase ) or line.startswith(__UpperCAmelCase )):
_lowercase : Union[str, Any] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
_lowercase : Optional[int] = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
_lowercase : Optional[Any] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(F"""{spaces * " "}{correct_line}""" )
_lowercase : Union[str, Any] = False
else:
new_lines.append(__UpperCAmelCase )
with open(__UpperCAmelCase , """w""" ) as f:
for line in new_lines:
f.write(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None ):
if fail is not None:
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Dict = {l.strip() for l in f.readlines()}
else:
_lowercase : int = None
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : int = f.readlines()
_lowercase : int = defaultdict(__UpperCAmelCase )
for line in correct_lines:
_lowercase , _lowercase , _lowercase , _lowercase : int = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase: List[Any] = argparse.ArgumentParser()
parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
UpperCAmelCase: Any = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 336 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase: List[Any] = logging.get_logger(__name__)
UpperCAmelCase: List[Any] = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
# See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = "donut-swin"
SCREAMING_SNAKE_CASE_ : Any = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self ,UpperCAmelCase_=2_24 ,UpperCAmelCase_=4 ,UpperCAmelCase_=3 ,UpperCAmelCase_=96 ,UpperCAmelCase_=[2, 2, 6, 2] ,UpperCAmelCase_=[3, 6, 12, 24] ,UpperCAmelCase_=7 ,UpperCAmelCase_=4.0 ,UpperCAmelCase_=True ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=False ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-5 ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : List[Any] = image_size
_lowercase : int = patch_size
_lowercase : Optional[int] = num_channels
_lowercase : List[Any] = embed_dim
_lowercase : Dict = depths
_lowercase : Tuple = len(UpperCAmelCase_ )
_lowercase : int = num_heads
_lowercase : Optional[int] = window_size
_lowercase : Optional[Any] = mlp_ratio
_lowercase : str = qkv_bias
_lowercase : str = hidden_dropout_prob
_lowercase : Optional[int] = attention_probs_dropout_prob
_lowercase : Union[str, Any] = drop_path_rate
_lowercase : Tuple = hidden_act
_lowercase : int = use_absolute_embeddings
_lowercase : List[Any] = layer_norm_eps
_lowercase : Any = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_lowercase : Optional[Any] = int(embed_dim * 2 ** (len(UpperCAmelCase_ ) - 1) )
| 336 |
"""simple docstring"""
UpperCAmelCase: List[str] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : List[Any] = list(range(len(__UpperCAmelCase ) ) )
_lowercase : Optional[int] = [v / w for v, w in zip(__UpperCAmelCase , __UpperCAmelCase )]
index.sort(key=lambda __UpperCAmelCase : ratio[i] , reverse=__UpperCAmelCase )
_lowercase : float = 0
_lowercase : list[float] = [0] * len(__UpperCAmelCase )
for i in index:
if weight[i] <= capacity:
_lowercase : Optional[Any] = 1
max_value += value[i]
capacity -= weight[i]
else:
_lowercase : Any = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
UpperCAmelCase: str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCAmelCase: Any = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase: int = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 336 | 1 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
UpperCAmelCase: int = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase: Optional[int] = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase: List[str] = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase: Optional[Any] = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
UpperCAmelCase: Any = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase: List[str] = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase: List[str] = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase: int = re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase: Tuple = re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase: List[Any] = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
UpperCAmelCase: Union[str, Any] = re.compile(r"""^\s*try:""")
# Catches a line with else:
UpperCAmelCase: Union[str, Any] = re.compile(r"""^\s*else:""")
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if _re_test_backend.search(__UpperCAmelCase ) is None:
return None
_lowercase : Any = [b[0] for b in _re_backend.findall(__UpperCAmelCase )]
backends.sort()
return "_and_".join(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
with open(__UpperCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_lowercase : str = f.readlines()
_lowercase : Union[str, Any] = 0
while line_index < len(__UpperCAmelCase ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__UpperCAmelCase ):
return None
# First grab the objects without a specific backend in _import_structure
_lowercase : Optional[Any] = []
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
_lowercase : Tuple = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__UpperCAmelCase ):
_lowercase : Tuple = _re_one_line_import_struct.search(__UpperCAmelCase ).groups()[0]
_lowercase : List[Any] = re.findall(R"""\[([^\]]+)\]""" , __UpperCAmelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
_lowercase : Any = _re_import_struct_key_value.search(__UpperCAmelCase )
if single_line_import_search is not None:
_lowercase : Any = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
_lowercase : List[str] = {"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
_lowercase : List[Any] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_lowercase : Any = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_lowercase : str = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
_lowercase : Optional[int] = lines[line_index]
if _re_import_struct_add_one.search(__UpperCAmelCase ) is not None:
objects.append(_re_import_struct_add_one.search(__UpperCAmelCase ).groups()[0] )
elif _re_import_struct_add_many.search(__UpperCAmelCase ) is not None:
_lowercase : Optional[Any] = _re_import_struct_add_many.search(__UpperCAmelCase ).groups()[0].split(""", """ )
_lowercase : Tuple = [obj[1:-1] for obj in imports if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif _re_between_brackets.search(__UpperCAmelCase ) is not None:
_lowercase : Dict = _re_between_brackets.search(__UpperCAmelCase ).groups()[0].split(""", """ )
_lowercase : int = [obj[1:-1] for obj in imports if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif _re_quote_object.search(__UpperCAmelCase ) is not None:
objects.append(_re_quote_object.search(__UpperCAmelCase ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
_lowercase : Optional[int] = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_lowercase : Optional[Any] = []
while (
line_index < len(__UpperCAmelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
_lowercase : Dict = lines[line_index]
_lowercase : Tuple = _re_import.search(__UpperCAmelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
_lowercase : Optional[int] = {"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(__UpperCAmelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
_lowercase : int = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_lowercase : Union[str, Any] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_lowercase : Union[str, Any] = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
_lowercase : List[Any] = lines[line_index]
_lowercase : Optional[Any] = _re_import.search(__UpperCAmelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
_lowercase : Optional[Any] = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
def find_duplicates(__UpperCAmelCase ):
return [k for k, v in collections.Counter(__UpperCAmelCase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
_lowercase : int = []
for key in import_dict_objects.keys():
_lowercase : Tuple = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
_lowercase : Dict = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}""" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
_lowercase : Optional[int] = """base imports""" if key == """none""" else F"""{key} backend"""
errors.append(F"""Differences for {name}:""" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F""" {a} in TYPE_HINT but not in _import_structure.""" )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F""" {a} in _import_structure but not in TYPE_HINT.""" )
return errors
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = []
for root, _, files in os.walk(__UpperCAmelCase ):
if "__init__.py" in files:
_lowercase : List[str] = os.path.join(__UpperCAmelCase , """__init__.py""" )
_lowercase : Union[str, Any] = parse_init(__UpperCAmelCase )
if objects is not None:
_lowercase : Any = analyze_results(*__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
_lowercase : List[str] = F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append("""\n""".join(__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > 0:
raise ValueError("""\n\n""".join(__UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : str = []
for path, directories, files in os.walk(__UpperCAmelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(__UpperCAmelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__UpperCAmelCase ) / folder).glob("""*.py""" ) ) ) == 0:
continue
_lowercase : int = str((Path(__UpperCAmelCase ) / folder).relative_to(__UpperCAmelCase ) )
_lowercase : int = short_path.replace(os.path.sep , """.""" )
submodules.append(__UpperCAmelCase )
for fname in files:
if fname == "__init__.py":
continue
_lowercase : Union[str, Any] = str((Path(__UpperCAmelCase ) / fname).relative_to(__UpperCAmelCase ) )
_lowercase : Union[str, Any] = short_path.replace(""".py""" , """""" ).replace(os.path.sep , """.""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(__UpperCAmelCase )
return submodules
UpperCAmelCase: int = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
"""models.esm.openfold_utils""",
]
def __SCREAMING_SNAKE_CASE ( ):
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
_lowercase : Dict = direct_transformers_import(__UpperCAmelCase )
_lowercase : Optional[int] = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(__UpperCAmelCase , """__init__.py""" ) , """r""" ) as f:
_lowercase : int = f.read()
import_structure_keys.update(set(re.findall(R"""import_structure\[\"([^\"]*)\"\]""" , __UpperCAmelCase ) ) )
_lowercase : Union[str, Any] = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(__UpperCAmelCase ) > 0:
_lowercase : List[str] = """\n""".join(F"""- {module}""" for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registed in the main init of Transformers:\n"""
F"""{list_of_modules}\n"""
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 336 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
UpperCAmelCase: List[Any] = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
def constraint_to_multiple_of(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=0 , __UpperCAmelCase=None ):
_lowercase : Union[str, Any] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowercase : str = math.floor(val / multiple ) * multiple
if x < min_val:
_lowercase : Dict = math.ceil(val / multiple ) * multiple
return x
_lowercase : List[str] = (output_size, output_size) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else output_size
_lowercase , _lowercase : List[Any] = get_image_size(__UpperCAmelCase )
_lowercase , _lowercase : Union[str, Any] = output_size
# determine new height and width
_lowercase : str = output_height / input_height
_lowercase : List[Any] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowercase : str = scale_width
else:
# fit height
_lowercase : int = scale_height
_lowercase : List[Any] = constraint_to_multiple_of(scale_height * input_height , multiple=__UpperCAmelCase )
_lowercase : Optional[Any] = constraint_to_multiple_of(scale_width * input_width , multiple=__UpperCAmelCase )
return (new_height, new_width)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["pixel_values"]
def __init__( self ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = PILImageResampling.BILINEAR ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = 1 / 2_55 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : List[Any] = size if size is not None else {"""height""": 3_84, """width""": 3_84}
_lowercase : str = get_size_dict(UpperCAmelCase_ )
_lowercase : Tuple = do_resize
_lowercase : Any = size
_lowercase : List[Any] = keep_aspect_ratio
_lowercase : Any = ensure_multiple_of
_lowercase : str = resample
_lowercase : Optional[Any] = do_rescale
_lowercase : List[Any] = rescale_factor
_lowercase : Union[str, Any] = do_normalize
_lowercase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowercase : Any = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = PILImageResampling.BICUBIC ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
_lowercase : Optional[Any] = get_size_dict(UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_lowercase : Dict = get_resize_output_image_size(
UpperCAmelCase_ ,output_size=(size["""height"""], size["""width"""]) ,keep_aspect_ratio=UpperCAmelCase_ ,multiple=UpperCAmelCase_ ,)
return resize(UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return rescale(UpperCAmelCase_ ,scale=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return normalize(UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = ChannelDimension.FIRST ,**UpperCAmelCase_ ,):
_lowercase : Any = do_resize if do_resize is not None else self.do_resize
_lowercase : List[str] = size if size is not None else self.size
_lowercase : int = get_size_dict(UpperCAmelCase_ )
_lowercase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowercase : List[str] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowercase : List[str] = resample if resample is not None else self.resample
_lowercase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowercase : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase : str = do_normalize if do_normalize is not None else self.do_normalize
_lowercase : Optional[int] = image_mean if image_mean is not None else self.image_mean
_lowercase : int = image_std if image_std is not None else self.image_std
_lowercase : Union[str, Any] = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_lowercase : int = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
_lowercase : Union[str, Any] = [self.resize(image=UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ) for image in images]
if do_rescale:
_lowercase : int = [self.rescale(image=UpperCAmelCase_ ,scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
_lowercase : str = [self.normalize(image=UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ) for image in images]
_lowercase : Tuple = [to_channel_dimension_format(UpperCAmelCase_ ,UpperCAmelCase_ ) for image in images]
_lowercase : int = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase_ ,tensor_type=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ):
_lowercase : Union[str, Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(UpperCAmelCase_ ):
_lowercase : Tuple = target_sizes.numpy()
_lowercase : Optional[Any] = []
for idx in range(len(UpperCAmelCase_ ) ):
_lowercase : Dict = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=UpperCAmelCase_ )
_lowercase : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCAmelCase_ )
else:
_lowercase : Union[str, Any] = logits.argmax(dim=1 )
_lowercase : Optional[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 336 | 1 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
_lowercase : Any = cst_fwd.get(__UpperCAmelCase , np.inf )
_lowercase : Optional[int] = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
_lowercase : Optional[int] = new_cost_f
_lowercase : str = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
_lowercase : List[Any] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : int = -1
_lowercase : Tuple = set()
_lowercase : Dict = set()
_lowercase : int = {source: 0}
_lowercase : Optional[int] = {destination: 0}
_lowercase : List[Any] = {source: None}
_lowercase : List[str] = {destination: None}
_lowercase : PriorityQueue[Any] = PriorityQueue()
_lowercase : PriorityQueue[Any] = PriorityQueue()
_lowercase : List[str] = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
_lowercase , _lowercase : Optional[int] = queue_forward.get()
visited_forward.add(__UpperCAmelCase )
_lowercase , _lowercase : Union[str, Any] = queue_backward.get()
visited_backward.add(__UpperCAmelCase )
_lowercase : Tuple = pass_and_relaxation(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , )
_lowercase : Union[str, Any] = pass_and_relaxation(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
_lowercase : Optional[int] = shortest_distance
return shortest_path_distance
UpperCAmelCase: List[Any] = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
UpperCAmelCase: Tuple = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase: Optional[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
UpperCAmelCase: Tuple = [0, 25, 50]
UpperCAmelCase: List[Any] = [25, 50, 75]
UpperCAmelCase: Optional[int] = fuzz.membership.trimf(X, abca)
UpperCAmelCase: Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
UpperCAmelCase: List[Any] = np.ones(75)
UpperCAmelCase: Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
UpperCAmelCase: str = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
UpperCAmelCase: List[Any] = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
UpperCAmelCase: int = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
UpperCAmelCase: int = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
UpperCAmelCase: List[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
UpperCAmelCase: int = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("""Young""")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("""Middle aged""")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("""union""")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("""intersection""")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("""complement_a""")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("""difference a/b""")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("""alg_sum""")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("""alg_product""")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("""bdd_sum""")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("""bdd_difference""")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 336 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCAmelCase: int = logging.get_logger(__name__)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" ,UpperCAmelCase_ ,)
super().__init__(*UpperCAmelCase_ ,**UpperCAmelCase_ )
| 336 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
_lowercase : str = tempfile.mkdtemp()
# fmt: off
_lowercase : List[Any] = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
_lowercase : Optional[int] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
_lowercase : Optional[int] = {"""unk_token""": """<unk>"""}
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
_lowercase : Dict = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48145466, 0.4578275, 0.40821073],
"""image_std""": [0.26862954, 0.26130258, 0.27577711],
}
_lowercase : List[Any] = os.path.join(self.tmpdirname ,UpperCAmelCase_ )
with open(self.image_processor_file ,"""w""" ,encoding="""utf-8""" ) as fp:
json.dump(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPTokenizer.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
_lowercase : Tuple = [Image.fromarray(np.moveaxis(UpperCAmelCase_ ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.get_tokenizer()
_lowercase : List[Any] = self.get_rust_tokenizer()
_lowercase : List[Any] = self.get_image_processor()
_lowercase : List[str] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
_lowercase : Optional[int] = CLIPProcessor.from_pretrained(self.tmpdirname ,use_fast=UpperCAmelCase_ )
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
_lowercase : List[str] = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() ,tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.image_processor ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = CLIPProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_lowercase : Union[str, Any] = self.get_tokenizer(bos_token="""(BOS)""" ,eos_token="""(EOS)""" )
_lowercase : Optional[int] = self.get_image_processor(do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
_lowercase : int = CLIPProcessor.from_pretrained(
self.tmpdirname ,bos_token="""(BOS)""" ,eos_token="""(EOS)""" ,do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : Optional[int] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : int = self.prepare_image_inputs()
_lowercase : str = image_processor(UpperCAmelCase_ ,return_tensors="""np""" )
_lowercase : int = processor(images=UpperCAmelCase_ ,return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : Optional[Any] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : List[Any] = """lower newer"""
_lowercase : Any = processor(text=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer(UpperCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : str = """lower newer"""
_lowercase : List[Any] = self.prepare_image_inputs()
_lowercase : Optional[int] = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase_ ):
processor()
def lowerCamelCase__ ( self ):
_lowercase : Dict = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowercase : int = processor.batch_decode(UpperCAmelCase_ )
_lowercase : Tuple = tokenizer.batch_decode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : Optional[Any] = """lower newer"""
_lowercase : Any = self.prepare_image_inputs()
_lowercase : Optional[int] = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,processor.model_input_names )
| 336 | 1 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase: List[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>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 16000 ):
_lowercase : List[Any] = int(round(sample_rate * max_length ) )
if len(__UpperCAmelCase ) <= sample_length:
return wav
_lowercase : List[Any] = randint(0 , len(__UpperCAmelCase ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[str] = field(default=snake_case , metadata={"help": "Name of a dataset from the datasets package"} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "A file containing the training audio paths and labels."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "A file containing the validation audio paths and labels."} )
SCREAMING_SNAKE_CASE_ : str = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
SCREAMING_SNAKE_CASE_ : str = field(
default="validation" , metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
)
} , )
SCREAMING_SNAKE_CASE_ : str = field(
default="audio" , metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"} , )
SCREAMING_SNAKE_CASE_ : str = field(
default="label" , metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"} )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = field(
default=snake_case , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
SCREAMING_SNAKE_CASE_ : float = field(
default=2_0 , metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."} , )
@dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = field(
default="facebook/wav2vec2-base" , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} , )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} )
SCREAMING_SNAKE_CASE_ : str = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
SCREAMING_SNAKE_CASE_ : Optional[str] = field(
default=snake_case , metadata={"help": "Name or path of preprocessor config."} )
SCREAMING_SNAKE_CASE_ : bool = field(
default=snake_case , metadata={"help": "Whether to freeze the feature encoder layers of the model."} )
SCREAMING_SNAKE_CASE_ : bool = field(
default=snake_case , metadata={"help": "Whether to generate an attention mask in the feature extractor."} )
SCREAMING_SNAKE_CASE_ : bool = field(
default=snake_case , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
SCREAMING_SNAKE_CASE_ : Optional[bool] = field(
default=snake_case , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
SCREAMING_SNAKE_CASE_ : bool = field(
default=snake_case , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def lowerCamelCase__ ( self ):
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""will be removed in a future version. Use `--freeze_feature_encoder`"""
"""instead. Setting `freeze_feature_encoder==True`.""" ,UpperCAmelCase_ ,)
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
"""The argument `--freeze_feature_extractor` is deprecated and """
"""should not be used in combination with `--freeze_feature_encoder`."""
"""Only make use of `--freeze_feature_encoder`.""" )
def __SCREAMING_SNAKE_CASE ( ):
# 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.
_lowercase : List[str] = 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.
_lowercase , _lowercase , _lowercase : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowercase , _lowercase , _lowercase : int = 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_audio_classification""" , __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()
_lowercase : Any = 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}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_lowercase : Optional[Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowercase : List[Any] = 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 train from scratch.""" )
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 and prepare it for the audio classification task.
_lowercase : int = DatasetDict()
_lowercase : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
_lowercase : Dict = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--audio_column_name` to the correct audio column - one of """
F"""{", ".join(raw_datasets["train"].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
"""Make sure to set `--label_column_name` to the correct text column - one of """
F"""{", ".join(raw_datasets["train"].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_lowercase : List[str] = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_lowercase : int = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_lowercase : int = feature_extractor.model_input_names[0]
def train_transforms(__UpperCAmelCase ):
_lowercase : Optional[Any] = []
for audio in batch[data_args.audio_column_name]:
_lowercase : Dict = random_subsample(
audio["""array"""] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(__UpperCAmelCase )
_lowercase : Any = feature_extractor(__UpperCAmelCase , sampling_rate=feature_extractor.sampling_rate )
_lowercase : Optional[int] = {model_input_name: inputs.get(__UpperCAmelCase )}
_lowercase : List[str] = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(__UpperCAmelCase ):
_lowercase : List[str] = [audio["""array"""] for audio in batch[data_args.audio_column_name]]
_lowercase : Dict = feature_extractor(__UpperCAmelCase , sampling_rate=feature_extractor.sampling_rate )
_lowercase : int = {model_input_name: inputs.get(__UpperCAmelCase )}
_lowercase : Optional[int] = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_lowercase : Dict = raw_datasets["""train"""].features[data_args.label_column_name].names
_lowercase , _lowercase : List[Any] = {}, {}
for i, label in enumerate(__UpperCAmelCase ):
_lowercase : Tuple = str(__UpperCAmelCase )
_lowercase : Any = label
# Load the accuracy metric from the datasets package
_lowercase : str = evaluate.load("""accuracy""" )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(__UpperCAmelCase ):
_lowercase : int = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=__UpperCAmelCase , references=eval_pred.label_ids )
_lowercase : Union[str, Any] = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__UpperCAmelCase ) , labelaid=__UpperCAmelCase , idalabel=__UpperCAmelCase , finetuning_task="""audio-classification""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_lowercase : Optional[int] = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_lowercase : Tuple = (
raw_datasets["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(__UpperCAmelCase , output_all_columns=__UpperCAmelCase )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_lowercase : str = (
raw_datasets["""eval"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(__UpperCAmelCase , output_all_columns=__UpperCAmelCase )
# Initialize our trainer
_lowercase : Union[str, Any] = Trainer(
model=__UpperCAmelCase , args=__UpperCAmelCase , train_dataset=raw_datasets["""train"""] if training_args.do_train else None , eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None , compute_metrics=__UpperCAmelCase , tokenizer=__UpperCAmelCase , )
# Training
if training_args.do_train:
_lowercase : Tuple = None
if training_args.resume_from_checkpoint is not None:
_lowercase : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowercase : List[Any] = last_checkpoint
_lowercase : Tuple = 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:
_lowercase : Any = trainer.evaluate()
trainer.log_metrics("""eval""" , __UpperCAmelCase )
trainer.save_metrics("""eval""" , __UpperCAmelCase )
# Write model card and (optionally) push to hub
_lowercase : Any = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """audio-classification""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""audio-classification"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__UpperCAmelCase )
else:
trainer.create_model_card(**__UpperCAmelCase )
if __name__ == "__main__":
main()
| 336 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
UpperCAmelCase: str = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[datasets.Features] = None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , ):
import pyspark
def generate_fn():
_lowercase : List[Any] = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
_lowercase : Optional[int] = df_with_partition_id.select("""*""" ).where(F"""part_id = {partition_id}""" ).drop("""part_id""" )
_lowercase : int = partition_df.collect()
_lowercase : Dict = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class UpperCamelCase ( _BaseExamplesIterable ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ,):
_lowercase : Union[str, Any] = df
_lowercase : List[str] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowercase : Tuple = _generate_iterable_examples(self.df ,self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.split_shard_indices_by_worker(UpperCAmelCase_ ,UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
@property
def lowerCamelCase__ ( self ):
return len(self.partition_order )
class UpperCamelCase ( datasets.DatasetBuilder ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = SparkConfig
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
import pyspark
_lowercase : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowercase : List[Any] = df
_lowercase : int = working_dir
super().__init__(
cache_dir=UpperCAmelCase_ ,config_name=str(self.df.semanticHash() ) ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(UpperCAmelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir ,exist_ok=UpperCAmelCase_ )
_lowercase : Union[str, Any] = os.path.join(self._cache_dir ,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(UpperCAmelCase_ ,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" ,"""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowercase : List[str] = (
self._spark.sparkContext.parallelize(range(1 ) ,1 ).mapPartitions(UpperCAmelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase__ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
import pyspark
def get_arrow_batch_size(UpperCAmelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
_lowercase : List[str] = self.df.count()
_lowercase : List[str] = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowercase : Union[str, Any] = (
self.df.limit(UpperCAmelCase_ )
.repartition(1 )
.mapInArrow(UpperCAmelCase_ ,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowercase : List[Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowercase : int = min(UpperCAmelCase_ ,int(approx_total_size / max_shard_size ) )
_lowercase : List[Any] = self.df.repartition(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
import pyspark
_lowercase : Union[str, Any] = ParquetWriter if file_format == """parquet""" else ArrowWriter
_lowercase : List[Any] = os.path.join(self._working_dir ,os.path.basename(UpperCAmelCase_ ) ) if self._working_dir else fpath
_lowercase : Any = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowercase : Union[str, Any] = self.config.features
_lowercase : Optional[int] = self._writer_batch_size
_lowercase : Optional[Any] = self._fs.storage_options
def write_arrow(UpperCAmelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowercase : Any = pyspark.TaskContext().taskAttemptId()
_lowercase : List[str] = next(UpperCAmelCase_ ,UpperCAmelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
_lowercase : List[Any] = 0
_lowercase : int = writer_class(
features=UpperCAmelCase_ ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Optional[int] = pa.Table.from_batches([first_batch] )
writer.write_table(UpperCAmelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowercase , _lowercase : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
shard_id += 1
_lowercase : Union[str, Any] = writer_class(
features=writer._features ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Dict = pa.Table.from_batches([batch] )
writer.write_table(UpperCAmelCase_ )
if writer._num_bytes > 0:
_lowercase , _lowercase : Dict = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(UpperCAmelCase_ ) ):
_lowercase : Dict = os.path.join(os.path.dirname(UpperCAmelCase_ ) ,os.path.basename(UpperCAmelCase_ ) )
shutil.move(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : List[str] = (
self.df.mapInArrow(UpperCAmelCase_ ,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) ,pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) ,pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) ,pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) ,)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = "arrow" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
self._validate_cache_dir()
_lowercase : Tuple = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(UpperCAmelCase_ )
_lowercase : Optional[int] = not is_remote_filesystem(self._fs )
_lowercase : Dict = os.path.join if is_local else posixpath.join
_lowercase : int = """-TTTTT-SSSSS-of-NNNNN"""
_lowercase : Optional[Any] = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
_lowercase : Dict = path_join(self._output_dir ,UpperCAmelCase_ )
_lowercase : List[Any] = 0
_lowercase : Optional[Any] = 0
_lowercase : int = 0
_lowercase : Any = []
_lowercase : Any = []
for task_id, content in self._prepare_split_single(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : Tuple = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(UpperCAmelCase_ )
_lowercase : Optional[int] = total_num_examples
_lowercase : List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
_lowercase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowercase : Union[str, Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
rename(
UpperCAmelCase_ ,fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace("""TTTTT-SSSSS""" ,f"""{global_shard_id:05d}""" ).replace("""NNNNN""" ,f"""{total_shards:05d}""" ) ,)
_lowercase : Optional[Any] = []
_lowercase : List[str] = 0
for i in range(len(UpperCAmelCase_ ) ):
_lowercase , _lowercase : List[str] = task_id_and_num_shards[i]
for shard_id in range(UpperCAmelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(UpperCAmelCase_ ,len(UpperCAmelCase_ ) ).map(lambda UpperCAmelCase_ : _rename_shard(*UpperCAmelCase_ ) ).collect()
else:
# don't use any pattern
_lowercase : Tuple = 0
_lowercase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace(UpperCAmelCase_ ,"""""" ) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,):
return SparkExamplesIterable(self.df )
| 336 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
UpperCAmelCase: str = logging.get_logger(__name__)
UpperCAmelCase: Dict = {
"""bigscience/bloom""": """https://huggingface.co/bigscience/bloom/resolve/main/config.json""",
"""bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/config.json""",
"""bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json""",
"""bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json""",
"""bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/config.json""",
"""bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json""",
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = "bloom"
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["past_key_values"]
SCREAMING_SNAKE_CASE_ : int = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
}
def __init__( self ,UpperCAmelCase_=25_08_80 ,UpperCAmelCase_=64 ,UpperCAmelCase_=2 ,UpperCAmelCase_=8 ,UpperCAmelCase_=1E-5 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=True ,UpperCAmelCase_=1 ,UpperCAmelCase_=2 ,UpperCAmelCase_=False ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=1 ,UpperCAmelCase_=False ,**UpperCAmelCase_ ,):
_lowercase : List[str] = vocab_size
# Backward compatibility with n_embed kwarg
_lowercase : Tuple = kwargs.pop("""n_embed""" ,UpperCAmelCase_ )
_lowercase : str = hidden_size if n_embed is None else n_embed
_lowercase : List[Any] = n_layer
_lowercase : str = n_head
_lowercase : List[Any] = layer_norm_epsilon
_lowercase : Tuple = initializer_range
_lowercase : Tuple = use_cache
_lowercase : List[str] = pretraining_tp
_lowercase : Union[str, Any] = apply_residual_connection_post_layernorm
_lowercase : int = hidden_dropout
_lowercase : List[str] = attention_dropout
_lowercase : Optional[int] = bos_token_id
_lowercase : List[Any] = eos_token_id
_lowercase : Union[str, Any] = slow_but_exact
super().__init__(bos_token_id=UpperCAmelCase_ ,eos_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = version.parse("1.12" )
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = "default" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = False ,):
super().__init__(UpperCAmelCase_ ,task=UpperCAmelCase_ ,patching_specs=UpperCAmelCase_ ,use_past=UpperCAmelCase_ )
if not getattr(self._config ,"""pad_token_id""" ,UpperCAmelCase_ ):
# TODO: how to do that better?
_lowercase : Tuple = 0
@property
def lowerCamelCase__ ( self ):
_lowercase : str = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(UpperCAmelCase_ ,direction="""inputs""" ,inverted_values_shape=UpperCAmelCase_ )
_lowercase : Any = {0: """batch""", 1: """past_sequence + sequence"""}
else:
_lowercase : Dict = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowerCamelCase__ ( self ):
return self._config.n_layer
@property
def lowerCamelCase__ ( self ):
return self._config.n_head
@property
def lowerCamelCase__ ( self ):
return 1E-3
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = -1 ,UpperCAmelCase_ = -1 ,UpperCAmelCase_ = False ,UpperCAmelCase_ = None ,):
_lowercase : Union[str, Any] = super(UpperCAmelCase_ ,self ).generate_dummy_inputs(
UpperCAmelCase_ ,batch_size=UpperCAmelCase_ ,seq_length=UpperCAmelCase_ ,is_pair=UpperCAmelCase_ ,framework=UpperCAmelCase_ )
# We need to order the input in the way they appears in the forward()
_lowercase : int = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_lowercase , _lowercase : Any = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
_lowercase : Optional[Any] = seqlen + 2
_lowercase : Optional[int] = self._config.hidden_size // self.num_attention_heads
_lowercase : Tuple = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
_lowercase : str = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
_lowercase : List[Any] = [
(torch.zeros(UpperCAmelCase_ ), torch.zeros(UpperCAmelCase_ )) for _ in range(self.num_layers )
]
_lowercase : Optional[Any] = common_inputs["""attention_mask"""]
if self.use_past:
_lowercase : Optional[Any] = ordered_inputs["""attention_mask"""].dtype
_lowercase : Any = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_ ,UpperCAmelCase_ ,dtype=UpperCAmelCase_ )] ,dim=1 )
return ordered_inputs
@property
def lowerCamelCase__ ( self ):
return 13
| 336 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Optional[int] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = XLNetTokenizer
SCREAMING_SNAKE_CASE_ : Dict = XLNetTokenizerFast
SCREAMING_SNAKE_CASE_ : int = True
SCREAMING_SNAKE_CASE_ : Union[str, Any] = True
def lowerCamelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowercase : Dict = XLNetTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = """<s>"""
_lowercase : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<unk>""" )
self.assertEqual(vocab_keys[1] ,"""<s>""" )
self.assertEqual(vocab_keys[-1] ,"""<eod>""" )
self.assertEqual(len(UpperCAmelCase_ ) ,10_06 )
def lowerCamelCase__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size ,10_00 )
def lowerCamelCase__ ( self ):
_lowercase : int = XLNetTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCAmelCase_ ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) ,[2_85, 46, 10, 1_70, 3_82] )
_lowercase : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] ,)
_lowercase : List[Any] = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
_lowercase : Optional[Any] = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] ,)
def lowerCamelCase__ ( self ):
_lowercase : Dict = XLNetTokenizer(UpperCAmelCase_ ,do_lower_case=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """""",
"""i""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] ,)
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,["""▁he""", """ll""", """o"""] )
def lowerCamelCase__ ( self ):
_lowercase : int = XLNetTokenizer(UpperCAmelCase_ ,do_lower_case=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] ,)
@slow
def lowerCamelCase__ ( self ):
_lowercase : Dict = XLNetTokenizer.from_pretrained("""xlnet-base-cased""" )
_lowercase : int = tokenizer.encode("""sequence builders""" ,add_special_tokens=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.encode("""multi-sequence build""" ,add_special_tokens=UpperCAmelCase_ )
_lowercase : Optional[int] = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ )
_lowercase : Dict = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ ,UpperCAmelCase_ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def lowerCamelCase__ ( self ):
# fmt: off
_lowercase : Union[str, Any] = {"""input_ids""": [[17, 2_14_42, 2_70, 17, 10, 1_46_45, 3_18, 34, 17, 45_46, 31_45, 7_87, 13, 77_52, 2_20_18, 23, 21, 17, 45_46, 31_45, 7_87, 13, 33_52, 1_44_31, 13, 55_00, 11, 11_76, 5_80, 13, 1_68_19, 47_97, 23, 17, 10, 1_71_35, 6_58, 19, 4_57, 79_32, 13, 1_84, 19, 31_54, 1_71_35, 64_68, 19, 14_04, 1_22_69, 19, 42_29, 53_56, 1_62_64, 46, 19, 17, 2_05_45, 1_03_95, 9, 9, 9, 11, 28, 64_21, 95_31, 2_07_29, 17, 10, 3_53, 1_70_22, 11, 21, 64_21, 95_31, 1_69_49, 17, 10, 1_15_09, 7_53, 11, 33, 95, 24_21, 73_85, 9_56, 1_44_31, 26_26, 25, 8_42, 73_85, 48_36, 21, 14_29, 22_72, 98_55, 31_20, 1_61, 2_47_38, 19, 1_32_03, 6_58, 2_18, 7_87, 21, 4_30, 1_84_82, 8_47, 26_37, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_22, 2_21_78, 27, 10_64, 22, 9_56, 13, 1_11_01, 14_29, 58_54, 2_43_13, 1_89_53, 40, 4_22, 2_43_66, 68, 17_58, 37, 1_04_83, 1_42_57, 31, 2_07, 2_63, 21, 2_03, 37_73, 25, 71, 97_35, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 20_49, 34_42, 17, 1_38_94, 33_80, 23, 95, 18, 1_76_34, 22_88, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ ,model_name="""xlnet-base-cased""" ,revision="""c841166438c31ec7ca9a106dee7bb312b73ae511""" ,)
| 336 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = DiTPipeline
SCREAMING_SNAKE_CASE_ : Any = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
SCREAMING_SNAKE_CASE_ : List[str] = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
SCREAMING_SNAKE_CASE_ : Dict = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
SCREAMING_SNAKE_CASE_ : List[str] = False
def lowerCamelCase__ ( self ):
torch.manual_seed(0 )
_lowercase : Union[str, Any] = TransformeraDModel(
sample_size=16 ,num_layers=2 ,patch_size=4 ,attention_head_dim=8 ,num_attention_heads=2 ,in_channels=4 ,out_channels=8 ,attention_bias=UpperCAmelCase_ ,activation_fn="""gelu-approximate""" ,num_embeds_ada_norm=10_00 ,norm_type="""ada_norm_zero""" ,norm_elementwise_affine=UpperCAmelCase_ ,)
_lowercase : Optional[int] = AutoencoderKL()
_lowercase : List[Any] = DDIMScheduler()
_lowercase : Dict = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=0 ):
if str(UpperCAmelCase_ ).startswith("""mps""" ):
_lowercase : Tuple = torch.manual_seed(UpperCAmelCase_ )
else:
_lowercase : List[Any] = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
_lowercase : Any = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ ( self ):
_lowercase : List[str] = """cpu"""
_lowercase : Union[str, Any] = self.get_dummy_components()
_lowercase : List[Any] = self.pipeline_class(**UpperCAmelCase_ )
pipe.to(UpperCAmelCase_ )
pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
_lowercase : Any = self.get_dummy_inputs(UpperCAmelCase_ )
_lowercase : Union[str, Any] = pipe(**UpperCAmelCase_ ).images
_lowercase : int = image[0, -3:, -3:, -1]
self.assertEqual(image.shape ,(1, 16, 16, 3) )
_lowercase : Tuple = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowercase : Any = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase_ ,1E-3 )
def lowerCamelCase__ ( self ):
self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase_ ,expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def lowerCamelCase__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = torch.manual_seed(0 )
_lowercase : str = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowercase : Optional[Any] = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowercase : Tuple = pipe.get_label_ids(UpperCAmelCase_ )
_lowercase : Optional[Any] = pipe(UpperCAmelCase_ ,generator=UpperCAmelCase_ ,num_inference_steps=40 ,output_type="""np""" ).images
for word, image in zip(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : str = load_numpy(
f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase__ ( self ):
_lowercase : List[str] = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowercase : Tuple = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowercase : str = ["""vase""", """umbrella"""]
_lowercase : Optional[int] = pipe.get_label_ids(UpperCAmelCase_ )
_lowercase : str = torch.manual_seed(0 )
_lowercase : int = pipe(UpperCAmelCase_ ,generator=UpperCAmelCase_ ,num_inference_steps=25 ,output_type="""np""" ).images
for word, image in zip(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1E-1
| 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() ,encoding="""utf-8""" ,check=UpperCAmelCase_ ,)
assert hasattr(self ,"""env""" )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# configuration for running training on smdistributed Model Parallel
_lowercase : Union[str, Any] = {
"""enabled""": True,
"""processes_per_host""": 8,
}
_lowercase : Optional[Any] = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
_lowercase : List[str] = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
_lowercase : str = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script ,source_dir=self.env.test_path ,role=self.env.role ,image_uri=self.env.image_uri ,base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" ,instance_count=UpperCAmelCase_ ,instance_type=self.instance_type ,debugger_hook_config=UpperCAmelCase_ ,hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 5_00,
} ,metric_definitions=self.env.metric_definitions ,distribution=UpperCAmelCase_ ,py_version="""py36""" ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
TrainingJobAnalytics(UpperCAmelCase_ ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# create estimator
_lowercase : List[Any] = self.create_estimator(UpperCAmelCase_ )
# run training
estimator.fit()
# result dataframe
_lowercase : List[Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
_lowercase : List[str] = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
_lowercase : Any = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
_lowercase : Union[str, Any] = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" ,99_99_99 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" ,"""w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} ,UpperCAmelCase_ )
| 336 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : int = []
for line in lines:
_lowercase : Dict = re.sub(R"""#.*""" , """""" , __UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(__UpperCAmelCase )
_lowercase : Tuple = """\n""".join(__UpperCAmelCase )
# Make a hash from all this code
_lowercase : Tuple = full_str.encode("""utf-8""" )
return shaaaa(__UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCAmelCase: Tuple = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCAmelCase: List[str] = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase: Any = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
UpperCAmelCase: Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 336 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=7 ,UpperCAmelCase_=3 ,UpperCAmelCase_=18 ,UpperCAmelCase_=30 ,UpperCAmelCase_=4_00 ,UpperCAmelCase_=True ,UpperCAmelCase_=None ,UpperCAmelCase_=True ,UpperCAmelCase_=None ,UpperCAmelCase_=True ,UpperCAmelCase_=[0.5, 0.5, 0.5] ,UpperCAmelCase_=[0.5, 0.5, 0.5] ,):
_lowercase : Tuple = size if size is not None else {"""shortest_edge""": 18}
_lowercase : int = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
_lowercase : Optional[Any] = parent
_lowercase : Tuple = batch_size
_lowercase : Optional[int] = num_channels
_lowercase : Tuple = image_size
_lowercase : Dict = min_resolution
_lowercase : int = max_resolution
_lowercase : Optional[int] = do_resize
_lowercase : Any = size
_lowercase : str = do_center_crop
_lowercase : Optional[Any] = crop_size
_lowercase : Union[str, Any] = do_normalize
_lowercase : Union[str, Any] = image_mean
_lowercase : Tuple = image_std
def lowerCamelCase__ ( self ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = LevitImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self ):
_lowercase : Tuple = LevitImageProcessingTester(self )
@property
def lowerCamelCase__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self ):
_lowercase : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""image_mean""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""image_std""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_normalize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_resize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_center_crop""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""size""" ) )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""shortest_edge""": 18} )
self.assertEqual(image_processor.crop_size ,{"""height""": 18, """width""": 18} )
_lowercase : Any = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 ,crop_size=84 )
self.assertEqual(image_processor.size ,{"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size ,{"""height""": 84, """width""": 84} )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
# Initialize image_processing
_lowercase : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowercase : str = prepare_image_inputs(self.image_processor_tester ,equal_resolution=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ ,Image.Image )
# Test not batched input
_lowercase : str = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
_lowercase : Optional[int] = image_processing(UpperCAmelCase_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def lowerCamelCase__ ( self ):
# Initialize image_processing
_lowercase : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowercase : str = prepare_image_inputs(self.image_processor_tester ,equal_resolution=UpperCAmelCase_ ,numpify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ ,np.ndarray )
# Test not batched input
_lowercase : List[Any] = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
_lowercase : Optional[Any] = image_processing(UpperCAmelCase_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def lowerCamelCase__ ( self ):
# Initialize image_processing
_lowercase : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowercase : List[Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=UpperCAmelCase_ ,torchify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ ,torch.Tensor )
# Test not batched input
_lowercase : str = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
_lowercase : int = image_processing(UpperCAmelCase_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
| 336 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 336 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] ,model_result["""ss"""] ):
_lowercase : Union[str, Any] = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = """sshleifer/tiny-gpt2"""
_lowercase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,eager_mode=UpperCAmelCase_ ,multi_process=UpperCAmelCase_ ,)
_lowercase : Any = TensorFlowBenchmark(UpperCAmelCase_ )
_lowercase : str = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__ ( self ):
_lowercase : int = """sgugger/tiny-distilbert-classification"""
_lowercase : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=UpperCAmelCase_ ,only_pretrain_model=UpperCAmelCase_ ,)
_lowercase : List[Any] = TensorFlowBenchmark(UpperCAmelCase_ )
_lowercase : str = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__ ( self ):
_lowercase : List[str] = """sshleifer/tiny-gpt2"""
_lowercase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=UpperCAmelCase_ ,)
_lowercase : List[str] = TensorFlowBenchmark(UpperCAmelCase_ )
_lowercase : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = """sshleifer/tiny-gpt2"""
_lowercase : str = AutoConfig.from_pretrained(UpperCAmelCase_ )
_lowercase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,eager_mode=UpperCAmelCase_ ,multi_process=UpperCAmelCase_ ,)
_lowercase : str = TensorFlowBenchmark(UpperCAmelCase_ ,[config] )
_lowercase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = """sshleifer/tiny-gpt2"""
_lowercase : str = AutoConfig.from_pretrained(UpperCAmelCase_ )
_lowercase : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=UpperCAmelCase_ ,)
_lowercase : Union[str, Any] = TensorFlowBenchmark(UpperCAmelCase_ ,[config] )
_lowercase : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = """sshleifer/tiny-gpt2"""
_lowercase : Union[str, Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=UpperCAmelCase_ ,)
_lowercase : Optional[int] = TensorFlowBenchmark(UpperCAmelCase_ )
_lowercase : int = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowerCamelCase__ ( self ):
_lowercase : Tuple = """sshleifer/tiny-gpt2"""
_lowercase : Tuple = AutoConfig.from_pretrained(UpperCAmelCase_ )
_lowercase : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=UpperCAmelCase_ ,)
_lowercase : Dict = TensorFlowBenchmark(UpperCAmelCase_ ,[config] )
_lowercase : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowerCamelCase__ ( self ):
_lowercase : Tuple = """patrickvonplaten/t5-tiny-random"""
_lowercase : Tuple = AutoConfig.from_pretrained(UpperCAmelCase_ )
_lowercase : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=UpperCAmelCase_ ,)
_lowercase : str = TensorFlowBenchmark(UpperCAmelCase_ ,configs=[config] )
_lowercase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU""" ) ) == 0 ,"""Cannot do xla on CPU.""" )
def lowerCamelCase__ ( self ):
_lowercase : Any = """sshleifer/tiny-gpt2"""
_lowercase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,training=UpperCAmelCase_ ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,use_xla=UpperCAmelCase_ ,multi_process=UpperCAmelCase_ ,)
_lowercase : List[str] = TensorFlowBenchmark(UpperCAmelCase_ )
_lowercase : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
_lowercase : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,inference=UpperCAmelCase_ ,save_to_csv=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,inference_time_csv_file=os.path.join(UpperCAmelCase_ ,"""inf_time.csv""" ) ,inference_memory_csv_file=os.path.join(UpperCAmelCase_ ,"""inf_mem.csv""" ) ,env_info_csv_file=os.path.join(UpperCAmelCase_ ,"""env.csv""" ) ,multi_process=UpperCAmelCase_ ,)
_lowercase : Tuple = TensorFlowBenchmark(UpperCAmelCase_ )
benchmark.run()
self.assertTrue(Path(os.path.join(UpperCAmelCase_ ,"""inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(UpperCAmelCase_ ,"""inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(UpperCAmelCase_ ,"""env.csv""" ) ).exists() )
def lowerCamelCase__ ( self ):
_lowercase : Dict = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(UpperCAmelCase_ ):
self.assertTrue(hasattr(UpperCAmelCase_ ,"""sequential""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""cumulative""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""current""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowercase : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] ,inference=UpperCAmelCase_ ,sequence_lengths=[8] ,batch_sizes=[1] ,log_filename=os.path.join(UpperCAmelCase_ ,"""log.txt""" ) ,log_print=UpperCAmelCase_ ,trace_memory_line_by_line=UpperCAmelCase_ ,eager_mode=UpperCAmelCase_ ,multi_process=UpperCAmelCase_ ,)
_lowercase : Optional[Any] = TensorFlowBenchmark(UpperCAmelCase_ )
_lowercase : int = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(UpperCAmelCase_ ,"""log.txt""" ) ).exists() )
| 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCAmelCase: Any = generate_large_matrix()
UpperCAmelCase: Dict = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
assert all(row == sorted(__UpperCAmelCase , reverse=__UpperCAmelCase ) for row in grid )
assert all(list(__UpperCAmelCase ) == sorted(__UpperCAmelCase , reverse=__UpperCAmelCase ) for col in zip(*__UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = 0
_lowercase : List[Any] = len(__UpperCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
_lowercase : Tuple = (left + right) // 2
_lowercase : List[Any] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
_lowercase : Dict = mid + 1
else:
_lowercase : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Any = 0
_lowercase : Optional[int] = len(grid[0] )
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = find_negative_index(grid[i][:bound] )
total += bound
return (len(__UpperCAmelCase ) * len(grid[0] )) - total
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return len([number for row in grid for number in row if number < 0] )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = 0
for row in grid:
for i, number in enumerate(__UpperCAmelCase ):
if number < 0:
total += len(__UpperCAmelCase ) - i
break
return total
def __SCREAMING_SNAKE_CASE ( ):
from timeit import timeit
print("""Running benchmarks""" )
_lowercase : Tuple = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
_lowercase : Dict = timeit(F"""{func}(grid=grid)""" , setup=__UpperCAmelCase , number=500 )
print(F"""{func}() took {time:0.4f} seconds""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 336 | 1 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCAmelCase: Optional[Any] = """__DUMMY_TRANSFORMERS_USER__"""
UpperCAmelCase: Tuple = """Dummy User"""
UpperCAmelCase: Union[str, Any] = """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"""
UpperCAmelCase: Tuple = """https://hub-ci.huggingface.co"""
UpperCAmelCase: List[Any] = CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
UpperCAmelCase: int = CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
UpperCAmelCase: Dict = Path("""~/.huggingface/hub_ci_token""").expanduser()
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
monkeypatch.setattr(
"""huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE""" , __UpperCAmelCase )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
monkeypatch.setattr("""datasets.config.HF_ENDPOINT""" , __UpperCAmelCase )
monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL""" , __UpperCAmelCase )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token""" , __UpperCAmelCase )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
HfFolder.save_token(__UpperCAmelCase )
yield
HfFolder.delete_token()
@pytest.fixture(scope="""session""" )
def __SCREAMING_SNAKE_CASE ( ):
return HfApi(endpoint=__UpperCAmelCase )
@pytest.fixture(scope="""session""" )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : int = HfFolder.get_token()
HfFolder.save_token(__UpperCAmelCase )
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(__UpperCAmelCase )
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
def _cleanup_repo(__UpperCAmelCase ):
hf_api.delete_repo(__UpperCAmelCase , token=__UpperCAmelCase , repo_type="""dataset""" )
return _cleanup_repo
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
@contextmanager
def _temporary_repo(__UpperCAmelCase ):
try:
yield repo_id
finally:
cleanup_repo(__UpperCAmelCase )
return _temporary_repo
@pytest.fixture(scope="""session""" )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[int] = F"""repo_txt_data-{int(time.time() * 1_0E3 )}"""
_lowercase : Optional[Any] = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__UpperCAmelCase , token=__UpperCAmelCase , repo_type="""dataset""" , private=__UpperCAmelCase )
hf_api.upload_file(
token=__UpperCAmelCase , path_or_fileobj=str(__UpperCAmelCase ) , path_in_repo="""data/text_data.txt""" , repo_id=__UpperCAmelCase , repo_type="""dataset""" , )
yield repo_id
try:
hf_api.delete_repo(__UpperCAmelCase , token=__UpperCAmelCase , repo_type="""dataset""" )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope="""session""" )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[int] = F"""repo_zipped_txt_data-{int(time.time() * 1_0E3 )}"""
_lowercase : str = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__UpperCAmelCase , token=__UpperCAmelCase , repo_type="""dataset""" , private=__UpperCAmelCase )
hf_api.upload_file(
token=__UpperCAmelCase , path_or_fileobj=str(__UpperCAmelCase ) , path_in_repo="""data.zip""" , repo_id=__UpperCAmelCase , repo_type="""dataset""" , )
yield repo_id
try:
hf_api.delete_repo(__UpperCAmelCase , token=__UpperCAmelCase , repo_type="""dataset""" )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope="""session""" )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""repo_zipped_img_data-{int(time.time() * 1_0E3 )}"""
_lowercase : List[Any] = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(__UpperCAmelCase , token=__UpperCAmelCase , repo_type="""dataset""" , private=__UpperCAmelCase )
hf_api.upload_file(
token=__UpperCAmelCase , path_or_fileobj=str(__UpperCAmelCase ) , path_in_repo="""data.zip""" , repo_id=__UpperCAmelCase , repo_type="""dataset""" , )
yield repo_id
try:
hf_api.delete_repo(__UpperCAmelCase , token=__UpperCAmelCase , repo_type="""dataset""" )
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
return hf_private_dataset_repo_zipped_img_data_
| 336 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase: List[str] = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase: int = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
re.sub("""<n>""" , """""" , __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 336 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["flax"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(self ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
@classmethod
def lowerCamelCase__ ( cls ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires_backends(cls ,["""flax"""] )
| 336 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=0.9_9_9 , __UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_lowercase : str = []
for i in range(__UpperCAmelCase ):
_lowercase : Any = i / num_diffusion_timesteps
_lowercase : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ) , __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
class UpperCamelCase ( snake_case , snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [e.name for e in KarrasDiffusionSchedulers]
SCREAMING_SNAKE_CASE_ : str = 2
@register_to_config
def __init__( self ,UpperCAmelCase_ = 10_00 ,UpperCAmelCase_ = 0.00085 ,UpperCAmelCase_ = 0.012 ,UpperCAmelCase_ = "linear" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = "epsilon" ,UpperCAmelCase_ = "linspace" ,UpperCAmelCase_ = 0 ,):
if trained_betas is not None:
_lowercase : str = torch.tensor(UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
_lowercase : Optional[Any] = torch.linspace(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_lowercase : Any = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,UpperCAmelCase_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_lowercase : Any = betas_for_alpha_bar(UpperCAmelCase_ )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
_lowercase : Tuple = 1.0 - self.betas
_lowercase : Dict = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ):
if schedule_timesteps is None:
_lowercase : Optional[int] = self.timesteps
_lowercase : Union[str, Any] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_lowercase : Optional[Any] = 1 if len(UpperCAmelCase_ ) > 1 else 0
else:
_lowercase : Dict = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
_lowercase : List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCamelCase__ ( self ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
_lowercase : str = self.index_for_timestep(UpperCAmelCase_ )
if self.state_in_first_order:
_lowercase : Optional[Any] = self.sigmas[step_index]
else:
_lowercase : Dict = self.sigmas_interpol[step_index]
_lowercase : Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,):
_lowercase : List[str] = num_inference_steps
_lowercase : Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_lowercase : Dict = np.linspace(0 ,num_train_timesteps - 1 ,UpperCAmelCase_ ,dtype=UpperCAmelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_lowercase : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : str = (np.arange(0 ,UpperCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_lowercase : str = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : Optional[int] = (np.arange(UpperCAmelCase_ ,0 ,-step_ratio )).round().copy().astype(UpperCAmelCase_ )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_lowercase : Union[str, Any] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_lowercase : Optional[Any] = torch.from_numpy(np.log(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_lowercase : List[str] = np.interp(UpperCAmelCase_ ,np.arange(0 ,len(UpperCAmelCase_ ) ) ,UpperCAmelCase_ )
_lowercase : int = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_lowercase : Any = torch.from_numpy(UpperCAmelCase_ ).to(device=UpperCAmelCase_ )
# interpolate sigmas
_lowercase : List[str] = sigmas.log().lerp(sigmas.roll(1 ).log() ,0.5 ).exp()
_lowercase : Union[str, Any] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
_lowercase : Tuple = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(UpperCAmelCase_ ).startswith("""mps""" ):
# mps does not support float64
_lowercase : Tuple = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=torch.floataa )
else:
_lowercase : str = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ )
# interpolate timesteps
_lowercase : int = self.sigma_to_t(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=timesteps.dtype )
_lowercase : Union[str, Any] = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,dim=-1 ).flatten()
_lowercase : str = torch.cat([timesteps[:1], interleaved_timesteps] )
_lowercase : List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_lowercase : Optional[Any] = defaultdict(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# get log sigma
_lowercase : Optional[Any] = sigma.log()
# get distribution
_lowercase : Optional[int] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
_lowercase : Tuple = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
_lowercase : List[Any] = low_idx + 1
_lowercase : int = self.log_sigmas[low_idx]
_lowercase : Any = self.log_sigmas[high_idx]
# interpolate sigmas
_lowercase : Any = (low - log_sigma) / (low - high)
_lowercase : Dict = w.clamp(0 ,1 )
# transform interpolation to time range
_lowercase : List[str] = (1 - w) * low_idx + w * high_idx
_lowercase : Optional[int] = t.view(sigma.shape )
return t
@property
def lowerCamelCase__ ( self ):
return self.sample is None
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = True ,):
_lowercase : Optional[int] = self.index_for_timestep(UpperCAmelCase_ )
# advance index counter by 1
_lowercase : str = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_lowercase : Any = self.sigmas[step_index]
_lowercase : Any = self.sigmas_interpol[step_index + 1]
_lowercase : Tuple = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
_lowercase : Union[str, Any] = self.sigmas[step_index - 1]
_lowercase : int = self.sigmas_interpol[step_index]
_lowercase : Tuple = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_lowercase : Any = 0
_lowercase : int = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_lowercase : Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : Optional[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_lowercase : str = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_lowercase : List[str] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_lowercase : Any = sigma_interpol - sigma_hat
# store for 2nd order step
_lowercase : List[Any] = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
_lowercase : Optional[Any] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
_lowercase : Optional[Any] = sigma_next - sigma_hat
_lowercase : Any = self.sample
_lowercase : Optional[int] = None
_lowercase : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_lowercase : int = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_ ):
# mps does not support float64
_lowercase : str = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
_lowercase : Any = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
_lowercase : List[Any] = self.timesteps.to(original_samples.device )
_lowercase : Union[str, Any] = timesteps.to(original_samples.device )
_lowercase : List[Any] = [self.index_for_timestep(UpperCAmelCase_ ,UpperCAmelCase_ ) for t in timesteps]
_lowercase : Optional[Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_lowercase : List[Any] = sigma.unsqueeze(-1 )
_lowercase : int = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
return self.config.num_train_timesteps
| 336 | 1 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ = "▁" ,UpperCAmelCase_ = True ,UpperCAmelCase_ = "<unk>" ,UpperCAmelCase_ = "</s>" ,UpperCAmelCase_ = "<pad>" ,):
_lowercase : List[Any] = {
"""pad""": {"""id""": 0, """token""": pad_token},
"""eos""": {"""id""": 1, """token""": eos_token},
"""unk""": {"""id""": 2, """token""": unk_token},
}
_lowercase : List[str] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
_lowercase : str = token_dict["""token"""]
_lowercase : Optional[int] = Tokenizer(Unigram() )
_lowercase : str = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(""" {2,}""" ) ,""" """ ),
normalizers.Lowercase(),
] )
_lowercase : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ ),
pre_tokenizers.Digits(individual_digits=UpperCAmelCase_ ),
pre_tokenizers.Punctuation(),
] )
_lowercase : Tuple = decoders.Metaspace(replacement=UpperCAmelCase_ ,add_prefix_space=UpperCAmelCase_ )
_lowercase : List[Any] = TemplateProcessing(
single=f"""$A {self.special_tokens["eos"]["token"]}""" ,special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])] ,)
_lowercase : Union[str, Any] = {
"""model""": """SentencePieceUnigram""",
"""replacement""": replacement,
"""add_prefix_space""": add_prefix_space,
}
super().__init__(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = 80_00 ,UpperCAmelCase_ = True ,):
_lowercase : Any = trainers.UnigramTrainer(
vocab_size=UpperCAmelCase_ ,special_tokens=self.special_tokens_list ,show_progress=UpperCAmelCase_ ,)
if isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Any = [files]
self._tokenizer.train(UpperCAmelCase_ ,trainer=UpperCAmelCase_ )
self.add_unk_id()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = 80_00 ,UpperCAmelCase_ = True ,):
_lowercase : Union[str, Any] = trainers.UnigramTrainer(
vocab_size=UpperCAmelCase_ ,special_tokens=self.special_tokens_list ,show_progress=UpperCAmelCase_ ,)
self._tokenizer.train_from_iterator(UpperCAmelCase_ ,trainer=UpperCAmelCase_ )
self.add_unk_id()
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = json.loads(self._tokenizer.to_str() )
_lowercase : Optional[Any] = self.special_tokens["""unk"""]["""id"""]
_lowercase : str = Tokenizer.from_str(json.dumps(UpperCAmelCase_ ) )
| 336 |
"""simple docstring"""
import pprint
import requests
UpperCAmelCase: Tuple = """https://zenquotes.io/api"""
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
UpperCAmelCase: int = random_quotes()
pprint.pprint(response)
| 336 | 1 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase: Any = logging.get_logger(__name__)
UpperCAmelCase: Optional[int] = """T5Config"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : List[str] = jnp.zeros_like(__UpperCAmelCase )
_lowercase : int = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
_lowercase : str = shifted_input_ids.at[:, 0].set(__UpperCAmelCase )
_lowercase : Union[str, Any] = jnp.where(shifted_input_ids == -100 , __UpperCAmelCase , __UpperCAmelCase )
return shifted_input_ids
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = "mt5"
SCREAMING_SNAKE_CASE_ : int = MTaConfig
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = "mt5"
SCREAMING_SNAKE_CASE_ : str = MTaConfig
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = "mt5"
SCREAMING_SNAKE_CASE_ : List[Any] = MTaConfig
| 336 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : int
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(__UpperCAmelCase ) )]
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
_lowercase : Tuple = all_rotations(__UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_lowercase : BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(__UpperCAmelCase ),
}
return response
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
_lowercase : Optional[Any] = int(__UpperCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(__UpperCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
_lowercase : int = [""""""] * len(__UpperCAmelCase )
for _ in range(len(__UpperCAmelCase ) ):
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = """Provide a string that I will generate its BWT transform: """
UpperCAmelCase: int = input(entry_msg).strip()
UpperCAmelCase: List[str] = bwt_transform(s)
print(
F'Burrows Wheeler transform for string \'{s}\' results '
F'in \'{result["bwt_string"]}\''
)
UpperCAmelCase: Union[str, Any] = reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
print(
F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
F'we get original string \'{original_string}\''
)
| 336 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase: List[str] = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase: int = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
re.sub("""<n>""" , """""" , __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 336 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = [randint(-1000 , 1000 ) for i in range(10 )]
_lowercase : Tuple = randint(-5000 , 5000 )
return (arr, r)
UpperCAmelCase: int = make_dataset()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
for triplet in permutations(__UpperCAmelCase , 3 ):
if sum(__UpperCAmelCase ) == target:
return tuple(sorted(__UpperCAmelCase ) )
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
arr.sort()
_lowercase : Optional[Any] = len(__UpperCAmelCase )
for i in range(n - 1 ):
_lowercase , _lowercase : str = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Tuple = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
_lowercase : Union[str, Any] = """
triplet_sum1(*dataset)
"""
_lowercase : Union[str, Any] = """
triplet_sum2(*dataset)
"""
_lowercase : Dict = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
_lowercase : Any = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
return (min(__UpperCAmelCase ), min(__UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase: Any = solution_times()
print(F'The time for naive implementation is {times[0]}.')
print(F'The time for optimized implementation is {times[1]}.')
| 336 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class UpperCamelCase :
"""simple docstring"""
@staticmethod
def lowerCamelCase__ ( *UpperCAmelCase_ ,**UpperCAmelCase_ ):
pass
@is_pipeline_test
@require_torch
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Tuple = pipeline("""visual-question-answering""" ,model="""hf-internal-testing/tiny-vilt-random-vqa""" )
_lowercase : Tuple = [
{
"""image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""question""": """How many cats are there?""",
},
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""question""": """How many cats are there?""",
},
]
return vqa_pipeline, examples
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = vqa_pipeline(UpperCAmelCase_ ,top_k=1 )
self.assertEqual(
UpperCAmelCase_ ,[
[{"""score""": ANY(UpperCAmelCase_ ), """answer""": ANY(UpperCAmelCase_ )}],
[{"""score""": ANY(UpperCAmelCase_ ), """answer""": ANY(UpperCAmelCase_ )}],
] ,)
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : int = pipeline("""visual-question-answering""" ,model="""hf-internal-testing/tiny-vilt-random-vqa""" )
_lowercase : Any = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
_lowercase : Any = """How many cats are there?"""
_lowercase : str = vqa_pipeline(image=UpperCAmelCase_ ,question="""How many cats are there?""" ,top_k=2 )
self.assertEqual(
UpperCAmelCase_ ,[{"""score""": ANY(UpperCAmelCase_ ), """answer""": ANY(UpperCAmelCase_ )}, {"""score""": ANY(UpperCAmelCase_ ), """answer""": ANY(UpperCAmelCase_ )}] )
_lowercase : Optional[int] = vqa_pipeline({"""image""": image, """question""": question} ,top_k=2 )
self.assertEqual(
UpperCAmelCase_ ,[{"""score""": ANY(UpperCAmelCase_ ), """answer""": ANY(UpperCAmelCase_ )}, {"""score""": ANY(UpperCAmelCase_ ), """answer""": ANY(UpperCAmelCase_ )}] )
@slow
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = pipeline("""visual-question-answering""" ,model="""dandelin/vilt-b32-finetuned-vqa""" )
_lowercase : Optional[int] = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
_lowercase : List[Any] = """How many cats are there?"""
_lowercase : Dict = vqa_pipeline(image=UpperCAmelCase_ ,question=UpperCAmelCase_ ,top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}] )
_lowercase : Union[str, Any] = vqa_pipeline({"""image""": image, """question""": question} ,top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}] )
_lowercase : List[Any] = vqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] ,top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ,decimals=4 ) ,[[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2 ,)
@require_tf
@unittest.skip("""Visual question answering not implemented in TF""" )
def lowerCamelCase__ ( self ):
pass
| 336 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE_ : Optional[int] = "BlipImageProcessor"
SCREAMING_SNAKE_CASE_ : Tuple = "AutoTokenizer"
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
super().__init__(UpperCAmelCase_ ,UpperCAmelCase_ )
# add QFormer tokenizer
_lowercase : Optional[int] = qformer_tokenizer
def __call__( self ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = True ,UpperCAmelCase_ = False ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
_lowercase : List[Any] = BatchFeature()
if text is not None:
_lowercase : List[str] = self.tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
encoding.update(UpperCAmelCase_ )
_lowercase : Dict = self.qformer_tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
_lowercase : str = qformer_text_encoding.pop("""input_ids""" )
_lowercase : int = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
_lowercase : Optional[Any] = self.image_processor(UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ )
encoding.update(UpperCAmelCase_ )
return encoding
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.tokenizer.model_input_names
_lowercase : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
if os.path.isfile(UpperCAmelCase_ ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(UpperCAmelCase_ ,exist_ok=UpperCAmelCase_ )
_lowercase : Any = os.path.join(UpperCAmelCase_ ,"""qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(UpperCAmelCase_ )
return super().save_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
_lowercase : Optional[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase_ ,subfolder="""qformer_tokenizer""" )
_lowercase : Any = cls._get_arguments_from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
args.append(UpperCAmelCase_ )
return cls(*UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
UpperCAmelCase: List[Any] = logging.getLogger(__name__)
@dataclass(frozen=snake_case )
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : Optional[str] = None
SCREAMING_SNAKE_CASE_ : Optional[str] = None
SCREAMING_SNAKE_CASE_ : Optional[str] = None
@dataclass(frozen=snake_case )
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[int]
SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None
SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None
SCREAMING_SNAKE_CASE_ : Optional[Union[int, float]] = None
SCREAMING_SNAKE_CASE_ : Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[InputFeatures]
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_=False ,UpperCAmelCase_ = False ,):
_lowercase : List[Any] = hans_processors[task]()
_lowercase : Union[str, Any] = os.path.join(
UpperCAmelCase_ ,"""cached_{}_{}_{}_{}""".format(
"""dev""" if evaluate else """train""" ,tokenizer.__class__.__name__ ,str(UpperCAmelCase_ ) ,UpperCAmelCase_ ,) ,)
_lowercase : str = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
_lowercase , _lowercase : List[Any] = label_list[2], label_list[1]
_lowercase : Optional[int] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_lowercase : int = cached_features_file + """.lock"""
with FileLock(UpperCAmelCase_ ):
if os.path.exists(UpperCAmelCase_ ) and not overwrite_cache:
logger.info(f"""Loading features from cached file {cached_features_file}""" )
_lowercase : str = torch.load(UpperCAmelCase_ )
else:
logger.info(f"""Creating features from dataset file at {data_dir}""" )
_lowercase : int = (
processor.get_dev_examples(UpperCAmelCase_ ) if evaluate else processor.get_train_examples(UpperCAmelCase_ )
)
logger.info("""Training examples: %s""" ,len(UpperCAmelCase_ ) )
_lowercase : Dict = hans_convert_examples_to_features(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
logger.info("""Saving features into cached file %s""" ,UpperCAmelCase_ )
torch.save(self.features ,UpperCAmelCase_ )
def __len__( self ):
return len(self.features )
def __getitem__( self ,UpperCAmelCase_ ):
return self.features[i]
def lowerCamelCase__ ( self ):
return self.label_list
if is_tf_available():
import tensorflow as tf
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[InputFeatures]
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = 1_28 ,UpperCAmelCase_=False ,UpperCAmelCase_ = False ,):
_lowercase : Any = hans_processors[task]()
_lowercase : str = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
_lowercase , _lowercase : str = label_list[2], label_list[1]
_lowercase : Dict = label_list
_lowercase : List[str] = processor.get_dev_examples(UpperCAmelCase_ ) if evaluate else processor.get_train_examples(UpperCAmelCase_ )
_lowercase : Optional[int] = hans_convert_examples_to_features(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) ,desc="""convert examples to features""" ):
if ex_index % 1_00_00 == 0:
logger.info("""Writing example %d of %d""" % (ex_index, len(UpperCAmelCase_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
_lowercase : Tuple = tf.data.Dataset.from_generator(
UpperCAmelCase_ ,(
{
"""example_id""": tf.intaa,
"""input_ids""": tf.intaa,
"""attention_mask""": tf.intaa,
"""token_type_ids""": tf.intaa,
},
tf.intaa,
) ,(
{
"""example_id""": tf.TensorShape([] ),
"""input_ids""": tf.TensorShape([None, None] ),
"""attention_mask""": tf.TensorShape([None, None] ),
"""token_type_ids""": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) ,)
def lowerCamelCase__ ( self ):
return self.dataset
def __len__( self ):
return len(self.features )
def __getitem__( self ,UpperCAmelCase_ ):
return self.features[i]
def lowerCamelCase__ ( self ):
return self.label_list
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_ ,"""heuristics_train_set.txt""" ) ) ,"""train""" )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_ ,"""heuristics_evaluation_set.txt""" ) ) ,"""dev""" )
def lowerCamelCase__ ( self ):
return ["contradiction", "entailment", "neutral"]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[Any] = []
for i, line in enumerate(UpperCAmelCase_ ):
if i == 0:
continue
_lowercase : Union[str, Any] = """%s-%s""" % (set_type, line[0])
_lowercase : int = line[5]
_lowercase : Union[str, Any] = line[6]
_lowercase : str = line[7][2:] if line[7].startswith("""ex""" ) else line[7]
_lowercase : int = line[0]
examples.append(InputExample(guid=UpperCAmelCase_ ,text_a=UpperCAmelCase_ ,text_b=UpperCAmelCase_ ,label=UpperCAmelCase_ ,pairID=UpperCAmelCase_ ) )
return examples
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
_lowercase : List[str] = {label: i for i, label in enumerate(__UpperCAmelCase )}
_lowercase : int = []
for ex_index, example in tqdm.tqdm(enumerate(__UpperCAmelCase ) , desc="""convert examples to features""" ):
if ex_index % 10000 == 0:
logger.info("""Writing example %d""" % (ex_index) )
_lowercase : List[Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=__UpperCAmelCase , max_length=__UpperCAmelCase , padding="""max_length""" , truncation=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , )
_lowercase : List[str] = label_map[example.label] if example.label in label_map else 0
_lowercase : int = int(example.pairID )
features.append(InputFeatures(**__UpperCAmelCase , label=__UpperCAmelCase , pairID=__UpperCAmelCase ) )
for i, example in enumerate(examples[:5] ):
logger.info("""*** Example ***""" )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
UpperCAmelCase: List[Any] = {
"""hans""": 3,
}
UpperCAmelCase: int = {
"""hans""": HansProcessor,
}
| 336 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase: Tuple = logging.get_logger(__name__)
UpperCAmelCase: List[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "trajectory_transformer"
SCREAMING_SNAKE_CASE_ : Optional[int] = ["past_key_values"]
SCREAMING_SNAKE_CASE_ : Tuple = {
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self ,UpperCAmelCase_=1_00 ,UpperCAmelCase_=5 ,UpperCAmelCase_=1 ,UpperCAmelCase_=1 ,UpperCAmelCase_=2_49 ,UpperCAmelCase_=6 ,UpperCAmelCase_=17 ,UpperCAmelCase_=25 ,UpperCAmelCase_=4 ,UpperCAmelCase_=4 ,UpperCAmelCase_=1_28 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.0006 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=1 ,UpperCAmelCase_=True ,UpperCAmelCase_=1 ,UpperCAmelCase_=5_02_56 ,UpperCAmelCase_=5_02_56 ,**UpperCAmelCase_ ,):
_lowercase : Dict = vocab_size
_lowercase : List[str] = action_weight
_lowercase : int = reward_weight
_lowercase : List[Any] = value_weight
_lowercase : List[str] = max_position_embeddings
_lowercase : Any = block_size
_lowercase : Any = action_dim
_lowercase : List[str] = observation_dim
_lowercase : Union[str, Any] = transition_dim
_lowercase : str = learning_rate
_lowercase : Tuple = n_layer
_lowercase : Optional[int] = n_head
_lowercase : List[str] = n_embd
_lowercase : List[str] = embd_pdrop
_lowercase : Optional[Any] = attn_pdrop
_lowercase : List[Any] = resid_pdrop
_lowercase : str = initializer_range
_lowercase : Optional[Any] = layer_norm_eps
_lowercase : List[Any] = kaiming_initializer_range
_lowercase : List[Any] = use_cache
super().__init__(pad_token_id=UpperCAmelCase_ ,bos_token_id=UpperCAmelCase_ ,eos_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
UpperCAmelCase: Optional[int] = logging.get_logger(__name__)
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : str = None
@staticmethod
def lowerCamelCase__ ( ):
raise NotImplementedError
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
raise NotImplementedError
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
raise NotImplementedError
def lowerCamelCase__ ( self ):
if not self.is_available():
raise RuntimeError(
f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )
@classmethod
def lowerCamelCase__ ( cls ):
return f"""`pip install {cls.pip_package or cls.name}`"""
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "optuna"
@staticmethod
def lowerCamelCase__ ( ):
return is_optuna_available()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
return run_hp_search_optuna(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return default_hp_space_optuna(UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = "ray"
SCREAMING_SNAKE_CASE_ : List[Any] = "'ray[tune]'"
@staticmethod
def lowerCamelCase__ ( ):
return is_ray_available()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
return run_hp_search_ray(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return default_hp_space_ray(UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "sigopt"
@staticmethod
def lowerCamelCase__ ( ):
return is_sigopt_available()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
return run_hp_search_sigopt(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return default_hp_space_sigopt(UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = "wandb"
@staticmethod
def lowerCamelCase__ ( ):
return is_wandb_available()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
return run_hp_search_wandb(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return default_hp_space_wandb(UpperCAmelCase_ )
UpperCAmelCase: Optional[int] = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : int = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__UpperCAmelCase ) > 0:
_lowercase : str = available_backends[0].name
if len(__UpperCAmelCase ) > 1:
logger.info(
F"""{len(__UpperCAmelCase )} hyperparameter search backends available. Using {name} as the default.""" )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
F""" - To install {backend.name} run {backend.pip_install()}"""
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 336 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase: Any = logging.get_logger(__name__)
UpperCAmelCase: List[str] = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = "instructblip_vision_model"
def __init__( self ,UpperCAmelCase_=14_08 ,UpperCAmelCase_=61_44 ,UpperCAmelCase_=39 ,UpperCAmelCase_=16 ,UpperCAmelCase_=2_24 ,UpperCAmelCase_=14 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=1E-6 ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=1E-10 ,UpperCAmelCase_=True ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : Optional[Any] = hidden_size
_lowercase : Tuple = intermediate_size
_lowercase : List[Any] = num_hidden_layers
_lowercase : Tuple = num_attention_heads
_lowercase : Optional[Any] = patch_size
_lowercase : Optional[Any] = image_size
_lowercase : Union[str, Any] = initializer_range
_lowercase : Optional[Any] = attention_dropout
_lowercase : List[Any] = layer_norm_eps
_lowercase : Optional[int] = hidden_act
_lowercase : Tuple = qkv_bias
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : List[Any] = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : int = 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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "instructblip_qformer"
def __init__( self ,UpperCAmelCase_=3_05_22 ,UpperCAmelCase_=7_68 ,UpperCAmelCase_=12 ,UpperCAmelCase_=12 ,UpperCAmelCase_=30_72 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=0 ,UpperCAmelCase_="absolute" ,UpperCAmelCase_=2 ,UpperCAmelCase_=14_08 ,**UpperCAmelCase_ ,):
super().__init__(pad_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : List[Any] = vocab_size
_lowercase : List[Any] = hidden_size
_lowercase : str = num_hidden_layers
_lowercase : List[str] = num_attention_heads
_lowercase : Optional[Any] = hidden_act
_lowercase : int = intermediate_size
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Optional[Any] = attention_probs_dropout_prob
_lowercase : List[Any] = max_position_embeddings
_lowercase : Tuple = initializer_range
_lowercase : Optional[int] = layer_norm_eps
_lowercase : Any = position_embedding_type
_lowercase : Dict = cross_attention_frequency
_lowercase : Optional[Any] = encoder_hidden_size
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : Dict = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : str = config_dict["""qformer_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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "instructblip"
SCREAMING_SNAKE_CASE_ : List[str] = True
def __init__( self ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=32 ,**UpperCAmelCase_ ):
super().__init__(**UpperCAmelCase_ )
if vision_config is None:
_lowercase : str = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
_lowercase : Any = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
_lowercase : Optional[int] = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
_lowercase : int = InstructBlipVisionConfig(**UpperCAmelCase_ )
_lowercase : Optional[int] = InstructBlipQFormerConfig(**UpperCAmelCase_ )
_lowercase : Dict = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
_lowercase : str = CONFIG_MAPPING[text_model_type](**UpperCAmelCase_ )
_lowercase : str = self.text_config.tie_word_embeddings
_lowercase : Union[str, Any] = self.text_config.is_encoder_decoder
_lowercase : List[str] = num_query_tokens
_lowercase : List[str] = self.vision_config.hidden_size
_lowercase : Dict = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowercase : Union[str, Any] = 1.0
_lowercase : Dict = 0.02
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_lowercase : int = self.vision_config.to_dict()
_lowercase : Any = self.qformer_config.to_dict()
_lowercase : Any = self.text_config.to_dict()
_lowercase : Optional[int] = self.__class__.model_type
return output
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if k in (0.04, 0.06):
_lowercase : Optional[Any] = k
_lowercase : Optional[Any] = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ):
return str(self.k )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = cva.imread(UpperCAmelCase_ ,0 )
_lowercase , _lowercase : Dict = img.shape
_lowercase : list[list[int]] = []
_lowercase : int = img.copy()
_lowercase : List[str] = cva.cvtColor(UpperCAmelCase_ ,cva.COLOR_GRAY2RGB )
_lowercase , _lowercase : Optional[Any] = np.gradient(UpperCAmelCase_ )
_lowercase : Optional[int] = dx**2
_lowercase : Optional[Any] = dy**2
_lowercase : Optional[Any] = dx * dy
_lowercase : List[str] = 0.04
_lowercase : Optional[Any] = self.window_size // 2
for y in range(UpperCAmelCase_ ,h - offset ):
for x in range(UpperCAmelCase_ ,w - offset ):
_lowercase : Optional[Any] = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Dict = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Union[str, Any] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : int = (wxx * wyy) - (wxy**2)
_lowercase : Union[str, Any] = wxx + wyy
_lowercase : Union[str, Any] = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) ,0 )
color_img.itemset((y, x, 1) ,0 )
color_img.itemset((y, x, 2) ,2_55 )
return color_img, corner_list
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = HarrisCorner(0.04, 3)
UpperCAmelCase , UpperCAmelCase: List[Any] = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 336 | 1 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
UpperCAmelCase: str = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[datasets.Features] = None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , ):
import pyspark
def generate_fn():
_lowercase : List[Any] = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
_lowercase : Optional[int] = df_with_partition_id.select("""*""" ).where(F"""part_id = {partition_id}""" ).drop("""part_id""" )
_lowercase : int = partition_df.collect()
_lowercase : Dict = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class UpperCamelCase ( _BaseExamplesIterable ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ,):
_lowercase : Union[str, Any] = df
_lowercase : List[str] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowercase : Tuple = _generate_iterable_examples(self.df ,self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.split_shard_indices_by_worker(UpperCAmelCase_ ,UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
@property
def lowerCamelCase__ ( self ):
return len(self.partition_order )
class UpperCamelCase ( datasets.DatasetBuilder ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = SparkConfig
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
import pyspark
_lowercase : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowercase : List[Any] = df
_lowercase : int = working_dir
super().__init__(
cache_dir=UpperCAmelCase_ ,config_name=str(self.df.semanticHash() ) ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(UpperCAmelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir ,exist_ok=UpperCAmelCase_ )
_lowercase : Union[str, Any] = os.path.join(self._cache_dir ,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(UpperCAmelCase_ ,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" ,"""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowercase : List[str] = (
self._spark.sparkContext.parallelize(range(1 ) ,1 ).mapPartitions(UpperCAmelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase__ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
import pyspark
def get_arrow_batch_size(UpperCAmelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
_lowercase : List[str] = self.df.count()
_lowercase : List[str] = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowercase : Union[str, Any] = (
self.df.limit(UpperCAmelCase_ )
.repartition(1 )
.mapInArrow(UpperCAmelCase_ ,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowercase : List[Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowercase : int = min(UpperCAmelCase_ ,int(approx_total_size / max_shard_size ) )
_lowercase : List[Any] = self.df.repartition(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
import pyspark
_lowercase : Union[str, Any] = ParquetWriter if file_format == """parquet""" else ArrowWriter
_lowercase : List[Any] = os.path.join(self._working_dir ,os.path.basename(UpperCAmelCase_ ) ) if self._working_dir else fpath
_lowercase : Any = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowercase : Union[str, Any] = self.config.features
_lowercase : Optional[int] = self._writer_batch_size
_lowercase : Optional[Any] = self._fs.storage_options
def write_arrow(UpperCAmelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowercase : Any = pyspark.TaskContext().taskAttemptId()
_lowercase : List[str] = next(UpperCAmelCase_ ,UpperCAmelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
_lowercase : List[Any] = 0
_lowercase : int = writer_class(
features=UpperCAmelCase_ ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Optional[int] = pa.Table.from_batches([first_batch] )
writer.write_table(UpperCAmelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowercase , _lowercase : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
shard_id += 1
_lowercase : Union[str, Any] = writer_class(
features=writer._features ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Dict = pa.Table.from_batches([batch] )
writer.write_table(UpperCAmelCase_ )
if writer._num_bytes > 0:
_lowercase , _lowercase : Dict = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(UpperCAmelCase_ ) ):
_lowercase : Dict = os.path.join(os.path.dirname(UpperCAmelCase_ ) ,os.path.basename(UpperCAmelCase_ ) )
shutil.move(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : List[str] = (
self.df.mapInArrow(UpperCAmelCase_ ,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) ,pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) ,pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) ,pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) ,)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = "arrow" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
self._validate_cache_dir()
_lowercase : Tuple = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(UpperCAmelCase_ )
_lowercase : Optional[int] = not is_remote_filesystem(self._fs )
_lowercase : Dict = os.path.join if is_local else posixpath.join
_lowercase : int = """-TTTTT-SSSSS-of-NNNNN"""
_lowercase : Optional[Any] = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
_lowercase : Dict = path_join(self._output_dir ,UpperCAmelCase_ )
_lowercase : List[Any] = 0
_lowercase : Optional[Any] = 0
_lowercase : int = 0
_lowercase : Any = []
_lowercase : Any = []
for task_id, content in self._prepare_split_single(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : Tuple = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(UpperCAmelCase_ )
_lowercase : Optional[int] = total_num_examples
_lowercase : List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
_lowercase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowercase : Union[str, Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
rename(
UpperCAmelCase_ ,fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace("""TTTTT-SSSSS""" ,f"""{global_shard_id:05d}""" ).replace("""NNNNN""" ,f"""{total_shards:05d}""" ) ,)
_lowercase : Optional[Any] = []
_lowercase : List[str] = 0
for i in range(len(UpperCAmelCase_ ) ):
_lowercase , _lowercase : List[str] = task_id_and_num_shards[i]
for shard_id in range(UpperCAmelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(UpperCAmelCase_ ,len(UpperCAmelCase_ ) ).map(lambda UpperCAmelCase_ : _rename_shard(*UpperCAmelCase_ ) ).collect()
else:
# don't use any pattern
_lowercase : Tuple = 0
_lowercase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace(UpperCAmelCase_ ,"""""" ) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,):
return SparkExamplesIterable(self.df )
| 336 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = LEDTokenizer
SCREAMING_SNAKE_CASE_ : List[str] = LEDTokenizerFast
SCREAMING_SNAKE_CASE_ : List[str] = True
def lowerCamelCase__ ( self ):
super().setUp()
_lowercase : Union[str, Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
_lowercase : List[Any] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
_lowercase : Dict = {"""unk_token""": """<unk>"""}
_lowercase : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
_lowercase : Any = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(UpperCAmelCase_ ,max_length=len(UpperCAmelCase_ ) ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
_lowercase : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIn("""input_ids""" ,UpperCAmelCase_ )
self.assertIn("""attention_mask""" ,UpperCAmelCase_ )
self.assertNotIn("""labels""" ,UpperCAmelCase_ )
self.assertNotIn("""decoder_attention_mask""" ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Dict = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(text_target=UpperCAmelCase_ ,max_length=32 ,padding="""max_length""" ,return_tensors="""pt""" )
self.assertEqual(32 ,targets["""input_ids"""].shape[1] )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : List[Any] = tokenizer(
["""I am a small frog""" * 10_24, """I am a small frog"""] ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = ["""A long paragraph for summarization."""]
_lowercase : Dict = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : List[str] = tokenizer(text_target=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : Union[str, Any] = inputs["""input_ids"""]
_lowercase : List[str] = targets["""input_ids"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : str = ["""Summary of the text.""", """Another summary."""]
_lowercase : Optional[int] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
_lowercase : Any = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ )
_lowercase : str = [[0] * len(UpperCAmelCase_ ) for x in encoded_output["""input_ids"""]]
_lowercase : Optional[int] = tokenizer.pad(UpperCAmelCase_ )
self.assertSequenceEqual(outputs["""global_attention_mask"""] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowercase : int = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Optional[int] = self.tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = """A, <mask> AllenNLP sentence."""
_lowercase : List[Any] = tokenizer_r.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
_lowercase : Any = tokenizer_p.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) ,sum(tokens_p["""token_type_ids"""] ) )
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) ,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) ,)
_lowercase : str = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
_lowercase : str = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
self.assertSequenceEqual(tokens_p["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
| 336 | 1 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
UpperCAmelCase: List[Any] = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
UpperCAmelCase: List[str] = subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()
UpperCAmelCase: Union[str, Any] = """|""".join(sys.argv[1:])
UpperCAmelCase: Tuple = re.compile(rF'^({joined_dirs}).*?\.py$')
UpperCAmelCase: Tuple = [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")
| 336 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Any = f.readlines()
_lowercase : Optional[int] = F"""class {class_name}("""
_lowercase : List[str] = F"""{4 * " "}def {test_name}("""
_lowercase : List[Any] = F"""{8 * " "}{correct_line.split()[0]}"""
_lowercase : int = F"""{16 * " "}{correct_line.split()[0]}"""
_lowercase : str = False
_lowercase : Optional[Any] = False
_lowercase : Union[str, Any] = False
_lowercase : Any = False
_lowercase : int = 0
_lowercase : Tuple = 0
_lowercase : Union[str, Any] = []
for line in lines:
if line.startswith(__UpperCAmelCase ):
_lowercase : List[str] = True
elif in_class and line.startswith(__UpperCAmelCase ):
_lowercase : str = True
elif in_class and in_func and (line.startswith(__UpperCAmelCase ) or line.startswith(__UpperCAmelCase )):
_lowercase : Union[str, Any] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
_lowercase : Optional[int] = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
_lowercase : Optional[Any] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(F"""{spaces * " "}{correct_line}""" )
_lowercase : Union[str, Any] = False
else:
new_lines.append(__UpperCAmelCase )
with open(__UpperCAmelCase , """w""" ) as f:
for line in new_lines:
f.write(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None ):
if fail is not None:
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Dict = {l.strip() for l in f.readlines()}
else:
_lowercase : int = None
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : int = f.readlines()
_lowercase : int = defaultdict(__UpperCAmelCase )
for line in correct_lines:
_lowercase , _lowercase , _lowercase , _lowercase : int = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase: List[Any] = argparse.ArgumentParser()
parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
UpperCAmelCase: Any = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 336 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
UpperCAmelCase: str = """docs/source/en/_toctree.yml"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : List[Any] = defaultdict(__UpperCAmelCase )
for doc in model_doc:
counts[doc["local"]] += 1
_lowercase : Union[str, Any] = [key for key, value in counts.items() if value > 1]
_lowercase : str = []
for duplicate_key in duplicates:
_lowercase : Optional[Any] = list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key} )
if len(__UpperCAmelCase ) > 1:
raise ValueError(
F"""{duplicate_key} is present several times in the documentation table of content at """
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1] )
# Sort
return sorted(__UpperCAmelCase , key=lambda __UpperCAmelCase : s["title"].lower() )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase=False ):
with open(__UpperCAmelCase , encoding="""utf-8""" ) as f:
_lowercase : Any = yaml.safe_load(f.read() )
# Get to the API doc
_lowercase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowercase : Optional[int] = content[api_idx]["""sections"""]
# Then to the model doc
_lowercase : Dict = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
_lowercase : int = api_doc[model_idx]["""sections"""]
_lowercase : Dict = [(idx, section) for idx, section in enumerate(__UpperCAmelCase ) if """sections""" in section]
_lowercase : str = False
for idx, modality_doc in modalities_docs:
_lowercase : Dict = modality_doc["""sections"""]
_lowercase : List[str] = clean_model_doc_toc(__UpperCAmelCase )
if old_modality_doc != new_modality_doc:
_lowercase : Optional[int] = True
if overwrite:
_lowercase : Dict = new_modality_doc
if diff:
if overwrite:
_lowercase : List[Any] = model_doc
_lowercase : Tuple = api_doc
with open(__UpperCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__UpperCAmelCase , allow_unicode=__UpperCAmelCase ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
UpperCAmelCase: Any = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
UpperCAmelCase: List[Any] = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 336 |
"""simple docstring"""
UpperCAmelCase: List[str] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 336 | 1 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 336 |
"""simple docstring"""
UpperCAmelCase: str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCAmelCase: Any = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase: int = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or not number >= 1:
raise ValueError(
"""starting number must be
and integer and be more than 0""" )
if not iterations >= 1:
raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz""" )
_lowercase : List[str] = """"""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__UpperCAmelCase )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
UpperCAmelCase: List[Any] = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
def constraint_to_multiple_of(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=0 , __UpperCAmelCase=None ):
_lowercase : Union[str, Any] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowercase : str = math.floor(val / multiple ) * multiple
if x < min_val:
_lowercase : Dict = math.ceil(val / multiple ) * multiple
return x
_lowercase : List[str] = (output_size, output_size) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else output_size
_lowercase , _lowercase : List[Any] = get_image_size(__UpperCAmelCase )
_lowercase , _lowercase : Union[str, Any] = output_size
# determine new height and width
_lowercase : str = output_height / input_height
_lowercase : List[Any] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowercase : str = scale_width
else:
# fit height
_lowercase : int = scale_height
_lowercase : List[Any] = constraint_to_multiple_of(scale_height * input_height , multiple=__UpperCAmelCase )
_lowercase : Optional[Any] = constraint_to_multiple_of(scale_width * input_width , multiple=__UpperCAmelCase )
return (new_height, new_width)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["pixel_values"]
def __init__( self ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = PILImageResampling.BILINEAR ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = 1 / 2_55 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : List[Any] = size if size is not None else {"""height""": 3_84, """width""": 3_84}
_lowercase : str = get_size_dict(UpperCAmelCase_ )
_lowercase : Tuple = do_resize
_lowercase : Any = size
_lowercase : List[Any] = keep_aspect_ratio
_lowercase : Any = ensure_multiple_of
_lowercase : str = resample
_lowercase : Optional[Any] = do_rescale
_lowercase : List[Any] = rescale_factor
_lowercase : Union[str, Any] = do_normalize
_lowercase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowercase : Any = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = PILImageResampling.BICUBIC ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
_lowercase : Optional[Any] = get_size_dict(UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_lowercase : Dict = get_resize_output_image_size(
UpperCAmelCase_ ,output_size=(size["""height"""], size["""width"""]) ,keep_aspect_ratio=UpperCAmelCase_ ,multiple=UpperCAmelCase_ ,)
return resize(UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return rescale(UpperCAmelCase_ ,scale=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return normalize(UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = ChannelDimension.FIRST ,**UpperCAmelCase_ ,):
_lowercase : Any = do_resize if do_resize is not None else self.do_resize
_lowercase : List[str] = size if size is not None else self.size
_lowercase : int = get_size_dict(UpperCAmelCase_ )
_lowercase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowercase : List[str] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowercase : List[str] = resample if resample is not None else self.resample
_lowercase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowercase : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase : str = do_normalize if do_normalize is not None else self.do_normalize
_lowercase : Optional[int] = image_mean if image_mean is not None else self.image_mean
_lowercase : int = image_std if image_std is not None else self.image_std
_lowercase : Union[str, Any] = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_lowercase : int = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
_lowercase : Union[str, Any] = [self.resize(image=UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ) for image in images]
if do_rescale:
_lowercase : int = [self.rescale(image=UpperCAmelCase_ ,scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
_lowercase : str = [self.normalize(image=UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ) for image in images]
_lowercase : Tuple = [to_channel_dimension_format(UpperCAmelCase_ ,UpperCAmelCase_ ) for image in images]
_lowercase : int = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase_ ,tensor_type=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ):
_lowercase : Union[str, Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(UpperCAmelCase_ ):
_lowercase : Tuple = target_sizes.numpy()
_lowercase : Optional[Any] = []
for idx in range(len(UpperCAmelCase_ ) ):
_lowercase : Dict = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=UpperCAmelCase_ )
_lowercase : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCAmelCase_ )
else:
_lowercase : Union[str, Any] = logits.argmax(dim=1 )
_lowercase : Optional[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 336 | 1 |
"""simple docstring"""
import numpy
# List of input, output pairs
UpperCAmelCase: str = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCAmelCase: Any = (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase: str = [2, 4, 1, 5]
UpperCAmelCase: List[str] = len(train_data)
UpperCAmelCase: str = 0.009
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase="train" ):
return calculate_hypothesis_value(__UpperCAmelCase , __UpperCAmelCase ) - output(
__UpperCAmelCase , __UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : int = 0
for i in range(len(__UpperCAmelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=m ):
_lowercase : Tuple = 0
for i in range(__UpperCAmelCase ):
if index == -1:
summation_value += _error(__UpperCAmelCase )
else:
summation_value += _error(__UpperCAmelCase ) * train_data[i][0][index]
return summation_value
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Any = summation_of_cost_derivative(__UpperCAmelCase , __UpperCAmelCase ) / m
return cost_derivative_value
def __SCREAMING_SNAKE_CASE ( ):
global parameter_vector
# Tune these values to set a tolerance value for predicted output
_lowercase : Optional[Any] = 0.0_0_0_0_0_2
_lowercase : List[Any] = 0
_lowercase : Tuple = 0
while True:
j += 1
_lowercase : Any = [0, 0, 0, 0]
for i in range(0 , len(__UpperCAmelCase ) ):
_lowercase : Tuple = get_cost_derivative(i - 1 )
_lowercase : Any = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
__UpperCAmelCase , __UpperCAmelCase , atol=__UpperCAmelCase , rtol=__UpperCAmelCase , ):
break
_lowercase : str = temp_parameter_vector
print(("""Number of iterations:""", j) )
def __SCREAMING_SNAKE_CASE ( ):
for i in range(len(__UpperCAmelCase ) ):
print(("""Actual output value:""", output(__UpperCAmelCase , """test""" )) )
print(("""Hypothesis output:""", calculate_hypothesis_value(__UpperCAmelCase , """test""" )) )
if __name__ == "__main__":
run_gradient_descent()
print("""\nTesting gradient descent for a linear hypothesis function.\n""")
test_gradient_descent()
| 336 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase: Optional[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
UpperCAmelCase: Tuple = [0, 25, 50]
UpperCAmelCase: List[Any] = [25, 50, 75]
UpperCAmelCase: Optional[int] = fuzz.membership.trimf(X, abca)
UpperCAmelCase: Any = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
UpperCAmelCase: List[Any] = np.ones(75)
UpperCAmelCase: Any = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
UpperCAmelCase: str = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
UpperCAmelCase: List[Any] = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
UpperCAmelCase: Optional[int] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
UpperCAmelCase: int = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
UpperCAmelCase: int = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
UpperCAmelCase: List[Any] = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
UpperCAmelCase: int = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("""Young""")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("""Middle aged""")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("""union""")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("""intersection""")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("""complement_a""")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("""difference a/b""")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("""alg_sum""")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("""alg_product""")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("""bdd_sum""")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("""bdd_difference""")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 336 | 1 |
"""simple docstring"""
# Copyright 2022 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
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
UpperCAmelCase: List[Any] = """Run commands across TPU VMs for initial setup before running `accelerate launch`."""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase=None ):
if subparsers is not None:
_lowercase : int = subparsers.add_parser("""tpu-config""" , description=_description )
else:
_lowercase : Optional[int] = argparse.ArgumentParser("""Accelerate tpu-config command""" , description=_description )
# Core arguments
_lowercase : List[str] = parser.add_argument_group(
"""Config Arguments""" , """Arguments that can be configured through `accelerate config`.""" )
config_args.add_argument(
"""--config_file""" , type=__UpperCAmelCase , default=__UpperCAmelCase , help="""Path to the config file to use for accelerate.""" , )
config_args.add_argument(
"""--tpu_name""" , default=__UpperCAmelCase , help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file.""" , )
config_args.add_argument(
"""--tpu_zone""" , default=__UpperCAmelCase , help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file.""" , )
_lowercase : int = parser.add_argument_group("""TPU Arguments""" , """Arguments for options ran inside the TPU.""" )
pod_args.add_argument(
"""--use_alpha""" , action="""store_true""" , help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.""" , )
pod_args.add_argument(
"""--command_file""" , default=__UpperCAmelCase , help="""The path to the file containing the commands to run on the pod on startup.""" , )
pod_args.add_argument(
"""--command""" , action="""append""" , nargs="""+""" , help="""A command to run on the pod. Can be passed multiple times.""" , )
pod_args.add_argument(
"""--install_accelerate""" , action="""store_true""" , help="""Whether to install accelerate on the pod. Defaults to False.""" , )
pod_args.add_argument(
"""--accelerate_version""" , default="""latest""" , help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.""" , )
pod_args.add_argument(
"""--debug""" , action="""store_true""" , help="""If set, will print the command that would be run instead of running it.""" )
if subparsers is not None:
parser.set_defaults(func=__UpperCAmelCase )
return parser
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(__UpperCAmelCase ):
_lowercase : Tuple = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
_lowercase : List[str] = defaults.command_file
if not args.command and defaults.commands is not None:
_lowercase : int = defaults.commands
if not args.tpu_name:
_lowercase : Optional[int] = defaults.tpu_name
if not args.tpu_zone:
_lowercase : Dict = defaults.tpu_zone
if args.accelerate_version == "dev":
_lowercase : int = """git+https://github.com/huggingface/accelerate.git"""
elif args.accelerate_version == "latest":
_lowercase : List[Any] = """accelerate -U"""
elif isinstance(parse(args.accelerate_version ) , __UpperCAmelCase ):
_lowercase : List[str] = F"""accelerate=={args.accelerate_version}"""
if not args.command_file and not args.command:
raise ValueError("""You must specify either a command file or a command to run on the pod.""" )
if args.command_file:
with open(args.command_file , """r""" ) as f:
_lowercase : Dict = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , __UpperCAmelCase ):
_lowercase : Optional[Any] = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
_lowercase : int = ["""cd /usr/share"""]
if args.install_accelerate:
new_cmd += [F"""pip install {args.accelerate_version}"""]
new_cmd += args.command
_lowercase : Optional[int] = """; """.join(__UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
_lowercase : Any = ["""gcloud"""]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F"""Running {" ".join(__UpperCAmelCase )}""" )
return
subprocess.run(__UpperCAmelCase )
print("""Successfully setup pod.""" )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Union[str, Any] = tpu_command_parser()
_lowercase : Optional[int] = parser.parse_args()
tpu_command_launcher(__UpperCAmelCase )
| 336 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ):
_lowercase : str = tempfile.mkdtemp()
# fmt: off
_lowercase : List[Any] = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
_lowercase : Optional[int] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
_lowercase : Optional[int] = {"""unk_token""": """<unk>"""}
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
_lowercase : Dict = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48145466, 0.4578275, 0.40821073],
"""image_std""": [0.26862954, 0.26130258, 0.27577711],
}
_lowercase : List[Any] = os.path.join(self.tmpdirname ,UpperCAmelCase_ )
with open(self.image_processor_file ,"""w""" ,encoding="""utf-8""" ) as fp:
json.dump(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPTokenizer.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
_lowercase : Tuple = [Image.fromarray(np.moveaxis(UpperCAmelCase_ ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.get_tokenizer()
_lowercase : List[Any] = self.get_rust_tokenizer()
_lowercase : List[Any] = self.get_image_processor()
_lowercase : List[str] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
_lowercase : Optional[int] = CLIPProcessor.from_pretrained(self.tmpdirname ,use_fast=UpperCAmelCase_ )
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
_lowercase : List[str] = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() ,tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.image_processor ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = CLIPProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_lowercase : Union[str, Any] = self.get_tokenizer(bos_token="""(BOS)""" ,eos_token="""(EOS)""" )
_lowercase : Optional[int] = self.get_image_processor(do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
_lowercase : int = CLIPProcessor.from_pretrained(
self.tmpdirname ,bos_token="""(BOS)""" ,eos_token="""(EOS)""" ,do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : Optional[int] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : int = self.prepare_image_inputs()
_lowercase : str = image_processor(UpperCAmelCase_ ,return_tensors="""np""" )
_lowercase : int = processor(images=UpperCAmelCase_ ,return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : Optional[Any] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : List[Any] = """lower newer"""
_lowercase : Any = processor(text=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer(UpperCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : str = """lower newer"""
_lowercase : List[Any] = self.prepare_image_inputs()
_lowercase : Optional[int] = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase_ ):
processor()
def lowerCamelCase__ ( self ):
_lowercase : Dict = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowercase : int = processor.batch_decode(UpperCAmelCase_ )
_lowercase : Tuple = tokenizer.batch_decode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.get_image_processor()
_lowercase : List[Any] = self.get_tokenizer()
_lowercase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
_lowercase : Optional[Any] = """lower newer"""
_lowercase : Any = self.prepare_image_inputs()
_lowercase : Optional[int] = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,processor.model_input_names )
| 336 | 1 |
"""simple docstring"""
from math import factorial
UpperCAmelCase: dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter number must be greater than or equal to 0""" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(__UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 60 , __UpperCAmelCase = 1000000 ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""Parameters chain_length and number_limit must be int""" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"""Parameters chain_length and number_limit must be greater than 0""" )
# the counter for the chains with the exact desired length
_lowercase : str = 0
# the cached sizes of the previous chains
_lowercase : dict[int, int] = {}
for start_chain_element in range(1 , __UpperCAmelCase ):
# The temporary set will contain the elements of the chain
_lowercase : Optional[int] = set()
_lowercase : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
_lowercase : Optional[int] = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(__UpperCAmelCase )
chain_set_length += 1
_lowercase : Optional[Any] = digit_factorial_sum(__UpperCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
_lowercase : List[str] = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{solution()}')
| 336 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
UpperCAmelCase: str = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[datasets.Features] = None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , ):
import pyspark
def generate_fn():
_lowercase : List[Any] = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
_lowercase : Optional[int] = df_with_partition_id.select("""*""" ).where(F"""part_id = {partition_id}""" ).drop("""part_id""" )
_lowercase : int = partition_df.collect()
_lowercase : Dict = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class UpperCamelCase ( _BaseExamplesIterable ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ,):
_lowercase : Union[str, Any] = df
_lowercase : List[str] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowercase : Tuple = _generate_iterable_examples(self.df ,self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.split_shard_indices_by_worker(UpperCAmelCase_ ,UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
@property
def lowerCamelCase__ ( self ):
return len(self.partition_order )
class UpperCamelCase ( datasets.DatasetBuilder ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = SparkConfig
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
import pyspark
_lowercase : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowercase : List[Any] = df
_lowercase : int = working_dir
super().__init__(
cache_dir=UpperCAmelCase_ ,config_name=str(self.df.semanticHash() ) ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(UpperCAmelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir ,exist_ok=UpperCAmelCase_ )
_lowercase : Union[str, Any] = os.path.join(self._cache_dir ,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(UpperCAmelCase_ ,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" ,"""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowercase : List[str] = (
self._spark.sparkContext.parallelize(range(1 ) ,1 ).mapPartitions(UpperCAmelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase__ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
import pyspark
def get_arrow_batch_size(UpperCAmelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
_lowercase : List[str] = self.df.count()
_lowercase : List[str] = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowercase : Union[str, Any] = (
self.df.limit(UpperCAmelCase_ )
.repartition(1 )
.mapInArrow(UpperCAmelCase_ ,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowercase : List[Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowercase : int = min(UpperCAmelCase_ ,int(approx_total_size / max_shard_size ) )
_lowercase : List[Any] = self.df.repartition(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
import pyspark
_lowercase : Union[str, Any] = ParquetWriter if file_format == """parquet""" else ArrowWriter
_lowercase : List[Any] = os.path.join(self._working_dir ,os.path.basename(UpperCAmelCase_ ) ) if self._working_dir else fpath
_lowercase : Any = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowercase : Union[str, Any] = self.config.features
_lowercase : Optional[int] = self._writer_batch_size
_lowercase : Optional[Any] = self._fs.storage_options
def write_arrow(UpperCAmelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowercase : Any = pyspark.TaskContext().taskAttemptId()
_lowercase : List[str] = next(UpperCAmelCase_ ,UpperCAmelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
_lowercase : List[Any] = 0
_lowercase : int = writer_class(
features=UpperCAmelCase_ ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Optional[int] = pa.Table.from_batches([first_batch] )
writer.write_table(UpperCAmelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowercase , _lowercase : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
shard_id += 1
_lowercase : Union[str, Any] = writer_class(
features=writer._features ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Dict = pa.Table.from_batches([batch] )
writer.write_table(UpperCAmelCase_ )
if writer._num_bytes > 0:
_lowercase , _lowercase : Dict = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(UpperCAmelCase_ ) ):
_lowercase : Dict = os.path.join(os.path.dirname(UpperCAmelCase_ ) ,os.path.basename(UpperCAmelCase_ ) )
shutil.move(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : List[str] = (
self.df.mapInArrow(UpperCAmelCase_ ,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) ,pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) ,pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) ,pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) ,)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = "arrow" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
self._validate_cache_dir()
_lowercase : Tuple = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(UpperCAmelCase_ )
_lowercase : Optional[int] = not is_remote_filesystem(self._fs )
_lowercase : Dict = os.path.join if is_local else posixpath.join
_lowercase : int = """-TTTTT-SSSSS-of-NNNNN"""
_lowercase : Optional[Any] = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
_lowercase : Dict = path_join(self._output_dir ,UpperCAmelCase_ )
_lowercase : List[Any] = 0
_lowercase : Optional[Any] = 0
_lowercase : int = 0
_lowercase : Any = []
_lowercase : Any = []
for task_id, content in self._prepare_split_single(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : Tuple = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(UpperCAmelCase_ )
_lowercase : Optional[int] = total_num_examples
_lowercase : List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
_lowercase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowercase : Union[str, Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
rename(
UpperCAmelCase_ ,fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace("""TTTTT-SSSSS""" ,f"""{global_shard_id:05d}""" ).replace("""NNNNN""" ,f"""{total_shards:05d}""" ) ,)
_lowercase : Optional[Any] = []
_lowercase : List[str] = 0
for i in range(len(UpperCAmelCase_ ) ):
_lowercase , _lowercase : List[str] = task_id_and_num_shards[i]
for shard_id in range(UpperCAmelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(UpperCAmelCase_ ,len(UpperCAmelCase_ ) ).map(lambda UpperCAmelCase_ : _rename_shard(*UpperCAmelCase_ ) ).collect()
else:
# don't use any pattern
_lowercase : Tuple = 0
_lowercase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace(UpperCAmelCase_ ,"""""" ) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,):
return SparkExamplesIterable(self.df )
| 336 | 1 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __SCREAMING_SNAKE_CASE ( *__UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Tuple = list(__UpperCAmelCase )
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Tuple = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : List[Any] = [
"""CUDA out of memory.""", # CUDA OOM
"""cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""", # CUDNN SNAFU
"""DefaultCPUAllocator: can't allocate memory""", # CPU OOM
]
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase = None , __UpperCAmelCase = 128 ):
if function is None:
return functools.partial(__UpperCAmelCase , starting_batch_size=__UpperCAmelCase )
_lowercase : Any = starting_batch_size
def decorator(*__UpperCAmelCase , **__UpperCAmelCase ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
_lowercase : Tuple = list(inspect.signature(__UpperCAmelCase ).parameters.keys() )
# Guard against user error
if len(__UpperCAmelCase ) < (len(__UpperCAmelCase ) + 1):
_lowercase : Union[str, Any] = """, """.join([F"""{arg}={value}""" for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
F"""Batch size was passed into `{function.__name__}` as the first argument when called."""
F"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`""" )
while True:
if batch_size == 0:
raise RuntimeError("""No executable batch size found, reached zero.""" )
try:
return function(__UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase )
except Exception as e:
if should_reduce_batch_size(__UpperCAmelCase ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 336 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Optional[int] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = XLNetTokenizer
SCREAMING_SNAKE_CASE_ : Dict = XLNetTokenizerFast
SCREAMING_SNAKE_CASE_ : int = True
SCREAMING_SNAKE_CASE_ : Union[str, Any] = True
def lowerCamelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowercase : Dict = XLNetTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = """<s>"""
_lowercase : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_ ) ,UpperCAmelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_ ) ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""<unk>""" )
self.assertEqual(vocab_keys[1] ,"""<s>""" )
self.assertEqual(vocab_keys[-1] ,"""<eod>""" )
self.assertEqual(len(UpperCAmelCase_ ) ,10_06 )
def lowerCamelCase__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size ,10_00 )
def lowerCamelCase__ ( self ):
_lowercase : int = XLNetTokenizer(UpperCAmelCase_ ,keep_accents=UpperCAmelCase_ )
_lowercase : Union[str, Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCAmelCase_ ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) ,[2_85, 46, 10, 1_70, 3_82] )
_lowercase : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] ,)
_lowercase : List[Any] = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
_lowercase : Optional[Any] = tokenizer.convert_ids_to_tokens(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] ,)
def lowerCamelCase__ ( self ):
_lowercase : Dict = XLNetTokenizer(UpperCAmelCase_ ,do_lower_case=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """""",
"""i""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] ,)
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,["""▁he""", """ll""", """o"""] )
def lowerCamelCase__ ( self ):
_lowercase : int = XLNetTokenizer(UpperCAmelCase_ ,do_lower_case=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCAmelCase_ ,[
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""se""",
""".""",
] ,)
@slow
def lowerCamelCase__ ( self ):
_lowercase : Dict = XLNetTokenizer.from_pretrained("""xlnet-base-cased""" )
_lowercase : int = tokenizer.encode("""sequence builders""" ,add_special_tokens=UpperCAmelCase_ )
_lowercase : List[str] = tokenizer.encode("""multi-sequence build""" ,add_special_tokens=UpperCAmelCase_ )
_lowercase : Optional[int] = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ )
_lowercase : Dict = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_ ,UpperCAmelCase_ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def lowerCamelCase__ ( self ):
# fmt: off
_lowercase : Union[str, Any] = {"""input_ids""": [[17, 2_14_42, 2_70, 17, 10, 1_46_45, 3_18, 34, 17, 45_46, 31_45, 7_87, 13, 77_52, 2_20_18, 23, 21, 17, 45_46, 31_45, 7_87, 13, 33_52, 1_44_31, 13, 55_00, 11, 11_76, 5_80, 13, 1_68_19, 47_97, 23, 17, 10, 1_71_35, 6_58, 19, 4_57, 79_32, 13, 1_84, 19, 31_54, 1_71_35, 64_68, 19, 14_04, 1_22_69, 19, 42_29, 53_56, 1_62_64, 46, 19, 17, 2_05_45, 1_03_95, 9, 9, 9, 11, 28, 64_21, 95_31, 2_07_29, 17, 10, 3_53, 1_70_22, 11, 21, 64_21, 95_31, 1_69_49, 17, 10, 1_15_09, 7_53, 11, 33, 95, 24_21, 73_85, 9_56, 1_44_31, 26_26, 25, 8_42, 73_85, 48_36, 21, 14_29, 22_72, 98_55, 31_20, 1_61, 2_47_38, 19, 1_32_03, 6_58, 2_18, 7_87, 21, 4_30, 1_84_82, 8_47, 26_37, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_22, 2_21_78, 27, 10_64, 22, 9_56, 13, 1_11_01, 14_29, 58_54, 2_43_13, 1_89_53, 40, 4_22, 2_43_66, 68, 17_58, 37, 1_04_83, 1_42_57, 31, 2_07, 2_63, 21, 2_03, 37_73, 25, 71, 97_35, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 20_49, 34_42, 17, 1_38_94, 33_80, 23, 95, 18, 1_76_34, 22_88, 9, 4, 3]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase_ ,model_name="""xlnet-base-cased""" ,revision="""c841166438c31ec7ca9a106dee7bb312b73ae511""" ,)
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
raise ValueError("""String lengths must match!""" )
_lowercase : int = 0
for chara, chara in zip(__UpperCAmelCase , __UpperCAmelCase ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,):
_lowercase : Union[str, Any] = parent
_lowercase : int = 13
_lowercase : Optional[int] = 7
_lowercase : str = 30
_lowercase : Optional[Any] = self.seq_length + self.mem_len
_lowercase : Dict = 15
_lowercase : Union[str, Any] = True
_lowercase : Optional[int] = True
_lowercase : Optional[int] = 99
_lowercase : int = [10, 50, 80]
_lowercase : List[str] = 32
_lowercase : str = 32
_lowercase : str = 4
_lowercase : Tuple = 8
_lowercase : List[str] = 1_28
_lowercase : List[Any] = 2
_lowercase : Union[str, Any] = 2
_lowercase : Optional[Any] = None
_lowercase : Any = 1
_lowercase : List[str] = 0
_lowercase : Optional[int] = 3
_lowercase : Tuple = self.vocab_size - 1
_lowercase : str = 0.01
def lowerCamelCase__ ( self ):
_lowercase : int = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowercase : int = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowercase : Any = None
if self.use_labels:
_lowercase : Any = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowercase : Optional[int] = TransfoXLConfig(
vocab_size=self.vocab_size ,mem_len=self.mem_len ,clamp_len=self.clamp_len ,cutoffs=self.cutoffs ,d_model=self.hidden_size ,d_embed=self.d_embed ,n_head=self.num_attention_heads ,d_head=self.d_head ,d_inner=self.d_inner ,div_val=self.div_val ,n_layer=self.num_hidden_layers ,eos_token_id=self.eos_token_id ,pad_token_id=self.vocab_size - 1 ,init_range=self.init_range ,num_labels=self.num_labels ,)
return (config, input_ids_a, input_ids_a, lm_labels)
def lowerCamelCase__ ( self ):
random.seed(self.seed )
tf.random.set_seed(self.seed )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = TFTransfoXLModel(UpperCAmelCase_ )
_lowercase , _lowercase : List[Any] = model(UpperCAmelCase_ ).to_tuple()
_lowercase : List[Any] = {"""input_ids""": input_ids_a, """mems""": mems_a}
_lowercase , _lowercase : List[Any] = model(UpperCAmelCase_ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,)
self.parent.assertListEqual(
[mem.shape for mem in mems_a] ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : List[Any] = TFTransfoXLLMHeadModel(UpperCAmelCase_ )
_lowercase , _lowercase : str = model(UpperCAmelCase_ ).to_tuple()
_lowercase : Any = {"""input_ids""": input_ids_a, """labels""": lm_labels}
_lowercase , _lowercase : Optional[Any] = model(UpperCAmelCase_ ).to_tuple()
_lowercase , _lowercase : str = model([input_ids_a, mems_a] ).to_tuple()
_lowercase : List[str] = {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}
_lowercase , _lowercase : Dict = model(UpperCAmelCase_ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,)
self.parent.assertEqual(lm_logits_a.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[Any] = TFTransfoXLForSequenceClassification(UpperCAmelCase_ )
_lowercase : List[str] = model(UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def lowerCamelCase__ ( self ):
_lowercase : str = self.prepare_config_and_inputs()
((_lowercase) , (_lowercase) , (_lowercase) , (_lowercase)) : List[str] = config_and_inputs
_lowercase : Union[str, Any] = {"""input_ids""": input_ids_a}
return config, inputs_dict
@require_tf
class UpperCamelCase ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
SCREAMING_SNAKE_CASE_ : int = () if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : Any = (
{
"feature-extraction": TFTransfoXLModel,
"text-classification": TFTransfoXLForSequenceClassification,
"text-generation": TFTransfoXLLMHeadModel,
"zero-shot": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : List[str] = False
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : Tuple = False
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def lowerCamelCase__ ( self ):
_lowercase : Tuple = TFTransfoXLModelTester(self )
_lowercase : Optional[Any] = ConfigTester(self ,config_class=UpperCAmelCase_ ,d_embed=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
self.model_tester.set_seed()
_lowercase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
self.model_tester.set_seed()
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : str = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
_lowercase : Dict = model_class(UpperCAmelCase_ )
assert isinstance(model.get_input_embeddings() ,tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
_lowercase : Tuple = model.get_output_embeddings()
assert isinstance(UpperCAmelCase_ ,tf.keras.layers.Layer )
_lowercase : Any = model.get_bias()
assert name is None
else:
_lowercase : Any = model.get_output_embeddings()
assert x is None
_lowercase : Tuple = model.get_bias()
assert name is None
def lowerCamelCase__ ( self ):
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def lowerCamelCase__ ( self ):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : List[str] = TFTransfoXLModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss.""" )
def lowerCamelCase__ ( self ):
pass
@require_tf
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip("""Skip test until #12651 is resolved.""" )
@slow
def lowerCamelCase__ ( self ):
_lowercase : List[str] = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""" )
# fmt: off
_lowercase : Any = tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]] ,dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
_lowercase : Any = [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
_lowercase : List[str] = model.generate(UpperCAmelCase_ ,max_length=2_00 ,do_sample=UpperCAmelCase_ )
self.assertListEqual(output_ids[0].numpy().tolist() ,UpperCAmelCase_ )
| 336 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : int = []
for line in lines:
_lowercase : Dict = re.sub(R"""#.*""" , """""" , __UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(__UpperCAmelCase )
_lowercase : Tuple = """\n""".join(__UpperCAmelCase )
# Make a hash from all this code
_lowercase : Tuple = full_str.encode("""utf-8""" )
return shaaaa(__UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCAmelCase: Tuple = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCAmelCase: List[str] = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase: Any = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
UpperCAmelCase: Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 336 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=13 ,UpperCAmelCase_=64 ,UpperCAmelCase_=2 ,UpperCAmelCase_=3 ,UpperCAmelCase_=True ,UpperCAmelCase_=True ,UpperCAmelCase_=32 ,UpperCAmelCase_=5 ,UpperCAmelCase_=4 ,UpperCAmelCase_=37 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=10 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=[1, 16, 4, 4] ,UpperCAmelCase_=None ,):
_lowercase : Union[str, Any] = parent
_lowercase : Optional[Any] = batch_size
_lowercase : Dict = image_size
_lowercase : Union[str, Any] = patch_size
_lowercase : str = num_channels
_lowercase : Any = is_training
_lowercase : Optional[Any] = use_labels
_lowercase : List[Any] = hidden_size
_lowercase : Union[str, Any] = num_hidden_layers
_lowercase : Optional[int] = num_attention_heads
_lowercase : List[Any] = intermediate_size
_lowercase : Optional[int] = hidden_act
_lowercase : Optional[Any] = hidden_dropout_prob
_lowercase : Union[str, Any] = attention_probs_dropout_prob
_lowercase : int = type_sequence_label_size
_lowercase : Any = initializer_range
_lowercase : Tuple = scope
_lowercase : Optional[Any] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
_lowercase : Tuple = (self.image_size // 32) ** 2
_lowercase : Union[str, Any] = num_patches + 1
def lowerCamelCase__ ( self ):
_lowercase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowercase : Tuple = None
if self.use_labels:
_lowercase : Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_lowercase : Dict = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
"""hidden_sizes""": [4, 8, 16, 32],
"""num_groups""": 2,
}
return ViTHybridConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=UpperCAmelCase_ ,initializer_range=self.initializer_range ,backbone_featmap_shape=self.backbone_featmap_shape ,backbone_config=UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : int = ViTHybridModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
_lowercase : List[str] = model(UpperCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Dict = self.type_sequence_label_size
_lowercase : Optional[int] = ViTHybridForImageClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
_lowercase : Optional[Any] = model(UpperCAmelCase_ ,labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = self.prepare_config_and_inputs()
_lowercase , _lowercase , _lowercase : Optional[int] = config_and_inputs
_lowercase : Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
SCREAMING_SNAKE_CASE_ : Optional[int] = (
{"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : List[Any] = False
SCREAMING_SNAKE_CASE_ : Tuple = False
SCREAMING_SNAKE_CASE_ : Any = False
def lowerCamelCase__ ( self ):
_lowercase : Dict = ViTHybridModelTester(self )
_lowercase : str = ConfigTester(self ,config_class=UpperCAmelCase_ ,has_text_modality=UpperCAmelCase_ ,hidden_size=37 )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : int = model_class(UpperCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
_lowercase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase_ ,nn.Linear ) )
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : List[str] = model_class(UpperCAmelCase_ )
_lowercase : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase : Optional[Any] = [*signature.parameters.keys()]
_lowercase : Dict = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
_lowercase , _lowercase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : str = _config_zero_init(UpperCAmelCase_ )
for model_class in self.all_model_classes:
_lowercase : Optional[Any] = model_class(config=UpperCAmelCase_ )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
_lowercase : Any = [f"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() ,[0.0, 1.0] ,msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,)
@slow
def lowerCamelCase__ ( self ):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : Optional[Any] = ViTHybridModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCamelCase__ ( self ):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
UpperCAmelCase_ )
_lowercase : Union[str, Any] = self.default_image_processor
_lowercase : Dict = prepare_img()
_lowercase : Dict = image_processor(images=UpperCAmelCase_ ,return_tensors="""pt""" ).to(UpperCAmelCase_ )
# forward pass
with torch.no_grad():
_lowercase : List[str] = model(**UpperCAmelCase_ )
# verify the logits
_lowercase : Optional[int] = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape ,UpperCAmelCase_ )
_lowercase : str = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,UpperCAmelCase_ ,atol=1E-4 ) )
@slow
@require_accelerate
def lowerCamelCase__ ( self ):
_lowercase : Dict = ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384""" )
_lowercase : Union[str, Any] = ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384""" ,device_map="""auto""" )
_lowercase : Optional[Any] = prepare_img()
_lowercase : Optional[Any] = image_processor(images=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : List[Any] = model(**UpperCAmelCase_ )
_lowercase : str = outputs.logits
# model predicts one of the 1000 ImageNet classes
_lowercase : Union[str, Any] = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] ,"""tabby, tabby cat""" )
| 336 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 336 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=7 ,UpperCAmelCase_=3 ,UpperCAmelCase_=18 ,UpperCAmelCase_=30 ,UpperCAmelCase_=4_00 ,UpperCAmelCase_=True ,UpperCAmelCase_=None ,UpperCAmelCase_=True ,UpperCAmelCase_=None ,UpperCAmelCase_=True ,UpperCAmelCase_=[0.48145466, 0.4578275, 0.40821073] ,UpperCAmelCase_=[0.26862954, 0.26130258, 0.27577711] ,UpperCAmelCase_=True ,):
_lowercase : Optional[Any] = size if size is not None else {"""height""": 2_24, """width""": 2_24}
_lowercase : Any = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
_lowercase : int = parent
_lowercase : Tuple = batch_size
_lowercase : int = num_channels
_lowercase : Dict = image_size
_lowercase : Any = min_resolution
_lowercase : List[str] = max_resolution
_lowercase : Any = do_resize
_lowercase : Optional[int] = size
_lowercase : Dict = do_center_crop
_lowercase : Optional[Any] = crop_size
_lowercase : Tuple = do_normalize
_lowercase : List[str] = image_mean
_lowercase : str = image_std
_lowercase : Any = do_convert_rgb
def lowerCamelCase__ ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def lowerCamelCase__ ( self ,UpperCAmelCase_=False ,UpperCAmelCase_=False ,UpperCAmelCase_=False ):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
_lowercase : Union[str, Any] = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
2_55 ,size=(self.num_channels, self.max_resolution, self.max_resolution) ,dtype=np.uinta ) )
else:
_lowercase : Optional[int] = []
for i in range(self.batch_size ):
_lowercase , _lowercase : Optional[Any] = np.random.choice(np.arange(self.min_resolution ,self.max_resolution ) ,2 )
image_inputs.append(np.random.randint(2_55 ,size=(self.num_channels, width, height) ,dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
_lowercase : Optional[int] = [Image.fromarray(np.moveaxis(UpperCAmelCase_ ,0 ,-1 ) ) for x in image_inputs]
if torchify:
_lowercase : Union[str, Any] = [torch.from_numpy(UpperCAmelCase_ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = ChineseCLIPImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self ):
_lowercase : int = ChineseCLIPImageProcessingTester(self ,do_center_crop=UpperCAmelCase_ )
@property
def lowerCamelCase__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self ):
_lowercase : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_resize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""size""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_center_crop""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""center_crop""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_normalize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""image_mean""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""image_std""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_convert_rgb""" ) )
def lowerCamelCase__ ( self ):
_lowercase : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 2_24, """width""": 2_24} )
self.assertEqual(image_processor.crop_size ,{"""height""": 18, """width""": 18} )
_lowercase : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 ,crop_size=84 )
self.assertEqual(image_processor.size ,{"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size ,{"""height""": 84, """width""": 84} )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
# Initialize image_processing
_lowercase : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowercase : List[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ ,Image.Image )
# Test not batched input
_lowercase : List[Any] = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
_lowercase : Tuple = image_processing(UpperCAmelCase_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def lowerCamelCase__ ( self ):
# Initialize image_processing
_lowercase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowercase : Any = self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_ ,numpify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ ,np.ndarray )
# Test not batched input
_lowercase : Optional[int] = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
_lowercase : str = image_processing(UpperCAmelCase_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def lowerCamelCase__ ( self ):
# Initialize image_processing
_lowercase : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowercase : Tuple = self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_ ,torchify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ ,torch.Tensor )
# Test not batched input
_lowercase : int = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
_lowercase : Any = image_processing(UpperCAmelCase_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
@require_torch
@require_vision
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ChineseCLIPImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self ):
_lowercase : Any = ChineseCLIPImageProcessingTester(self ,num_channels=4 ,do_center_crop=UpperCAmelCase_ )
_lowercase : List[Any] = 3
@property
def lowerCamelCase__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self ):
_lowercase : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_resize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""size""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_center_crop""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""center_crop""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_normalize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""image_mean""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""image_std""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ ,"""do_convert_rgb""" ) )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
# Initialize image_processing
_lowercase : int = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowercase : Dict = self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ ,Image.Image )
# Test not batched input
_lowercase : str = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
_lowercase : List[Any] = image_processing(UpperCAmelCase_ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
| 336 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCAmelCase: Any = generate_large_matrix()
UpperCAmelCase: Dict = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
assert all(row == sorted(__UpperCAmelCase , reverse=__UpperCAmelCase ) for row in grid )
assert all(list(__UpperCAmelCase ) == sorted(__UpperCAmelCase , reverse=__UpperCAmelCase ) for col in zip(*__UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = 0
_lowercase : List[Any] = len(__UpperCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
_lowercase : Tuple = (left + right) // 2
_lowercase : List[Any] = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
_lowercase : Dict = mid + 1
else:
_lowercase : Dict = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Any = 0
_lowercase : Optional[int] = len(grid[0] )
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = find_negative_index(grid[i][:bound] )
total += bound
return (len(__UpperCAmelCase ) * len(grid[0] )) - total
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return len([number for row in grid for number in row if number < 0] )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = 0
for row in grid:
for i, number in enumerate(__UpperCAmelCase ):
if number < 0:
total += len(__UpperCAmelCase ) - i
break
return total
def __SCREAMING_SNAKE_CASE ( ):
from timeit import timeit
print("""Running benchmarks""" )
_lowercase : Tuple = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
_lowercase : Dict = timeit(F"""{func}(grid=grid)""" , setup=__UpperCAmelCase , number=500 )
print(F"""{func}() took {time:0.4f} seconds""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 336 | 1 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if k in (0.04, 0.06):
_lowercase : Optional[Any] = k
_lowercase : Optional[Any] = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ):
return str(self.k )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = cva.imread(UpperCAmelCase_ ,0 )
_lowercase , _lowercase : Dict = img.shape
_lowercase : list[list[int]] = []
_lowercase : int = img.copy()
_lowercase : List[str] = cva.cvtColor(UpperCAmelCase_ ,cva.COLOR_GRAY2RGB )
_lowercase , _lowercase : Optional[Any] = np.gradient(UpperCAmelCase_ )
_lowercase : Optional[int] = dx**2
_lowercase : Optional[Any] = dy**2
_lowercase : Optional[Any] = dx * dy
_lowercase : List[str] = 0.04
_lowercase : Optional[Any] = self.window_size // 2
for y in range(UpperCAmelCase_ ,h - offset ):
for x in range(UpperCAmelCase_ ,w - offset ):
_lowercase : Optional[Any] = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Dict = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Union[str, Any] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : int = (wxx * wyy) - (wxy**2)
_lowercase : Union[str, Any] = wxx + wyy
_lowercase : Union[str, Any] = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) ,0 )
color_img.itemset((y, x, 1) ,0 )
color_img.itemset((y, x, 2) ,2_55 )
return color_img, corner_list
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = HarrisCorner(0.04, 3)
UpperCAmelCase , UpperCAmelCase: List[Any] = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 336 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase: List[str] = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase: int = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
re.sub("""<n>""" , """""" , __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 336 | 1 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __SCREAMING_SNAKE_CASE ( *__UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase=True , __UpperCAmelCase=2 ):
from .. import __version__
_lowercase : Tuple = take_from
_lowercase : Union[str, Any] = ()
if not isinstance(args[0] , __UpperCAmelCase ):
_lowercase : Optional[int] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__UpperCAmelCase ).base_version ) >= version.parse(__UpperCAmelCase ):
raise ValueError(
F"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
F""" version {__version__} is >= {version_name}""" )
_lowercase : List[str] = None
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__UpperCAmelCase ),)
_lowercase : Union[str, Any] = F"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__UpperCAmelCase , __UpperCAmelCase ):
values += (getattr(__UpperCAmelCase , __UpperCAmelCase ),)
_lowercase : List[str] = F"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_lowercase : int = F"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_lowercase : Dict = warning + """ """ if standard_warn else """"""
warnings.warn(warning + message , __UpperCAmelCase , stacklevel=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) > 0:
_lowercase : Tuple = inspect.getouterframes(inspect.currentframe() )[1]
_lowercase : List[Any] = call_frame.filename
_lowercase : Tuple = call_frame.lineno
_lowercase : Optional[Any] = call_frame.function
_lowercase , _lowercase : Union[str, Any] = next(iter(deprecated_kwargs.items() ) )
raise TypeError(F"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__UpperCAmelCase ) == 0:
return
elif len(__UpperCAmelCase ) == 1:
return values[0]
return values
| 336 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=0.9_9_9 , __UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
_lowercase : str = []
for i in range(__UpperCAmelCase ):
_lowercase : Any = i / num_diffusion_timesteps
_lowercase : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ) , __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
class UpperCamelCase ( snake_case , snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [e.name for e in KarrasDiffusionSchedulers]
SCREAMING_SNAKE_CASE_ : str = 2
@register_to_config
def __init__( self ,UpperCAmelCase_ = 10_00 ,UpperCAmelCase_ = 0.00085 ,UpperCAmelCase_ = 0.012 ,UpperCAmelCase_ = "linear" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = "epsilon" ,UpperCAmelCase_ = "linspace" ,UpperCAmelCase_ = 0 ,):
if trained_betas is not None:
_lowercase : str = torch.tensor(UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
_lowercase : Optional[Any] = torch.linspace(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_lowercase : Any = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,UpperCAmelCase_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_lowercase : Any = betas_for_alpha_bar(UpperCAmelCase_ )
else:
raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )
_lowercase : Tuple = 1.0 - self.betas
_lowercase : Dict = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ):
if schedule_timesteps is None:
_lowercase : Optional[int] = self.timesteps
_lowercase : Union[str, Any] = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_lowercase : Optional[Any] = 1 if len(UpperCAmelCase_ ) > 1 else 0
else:
_lowercase : Dict = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
_lowercase : List[str] = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCamelCase__ ( self ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
_lowercase : str = self.index_for_timestep(UpperCAmelCase_ )
if self.state_in_first_order:
_lowercase : Optional[Any] = self.sigmas[step_index]
else:
_lowercase : Dict = self.sigmas_interpol[step_index]
_lowercase : Optional[int] = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,):
_lowercase : List[str] = num_inference_steps
_lowercase : Dict = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_lowercase : Dict = np.linspace(0 ,num_train_timesteps - 1 ,UpperCAmelCase_ ,dtype=UpperCAmelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_lowercase : Union[str, Any] = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : str = (np.arange(0 ,UpperCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_lowercase : str = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowercase : Optional[int] = (np.arange(UpperCAmelCase_ ,0 ,-step_ratio )).round().copy().astype(UpperCAmelCase_ )
timesteps -= 1
else:
raise ValueError(
f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""" )
_lowercase : Union[str, Any] = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_lowercase : Optional[Any] = torch.from_numpy(np.log(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
_lowercase : List[str] = np.interp(UpperCAmelCase_ ,np.arange(0 ,len(UpperCAmelCase_ ) ) ,UpperCAmelCase_ )
_lowercase : int = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_lowercase : Any = torch.from_numpy(UpperCAmelCase_ ).to(device=UpperCAmelCase_ )
# interpolate sigmas
_lowercase : List[str] = sigmas.log().lerp(sigmas.roll(1 ).log() ,0.5 ).exp()
_lowercase : Union[str, Any] = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
_lowercase : Tuple = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(UpperCAmelCase_ ).startswith("""mps""" ):
# mps does not support float64
_lowercase : Tuple = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=torch.floataa )
else:
_lowercase : str = torch.from_numpy(UpperCAmelCase_ ).to(UpperCAmelCase_ )
# interpolate timesteps
_lowercase : int = self.sigma_to_t(UpperCAmelCase_ ).to(UpperCAmelCase_ ,dtype=timesteps.dtype )
_lowercase : Union[str, Any] = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,dim=-1 ).flatten()
_lowercase : str = torch.cat([timesteps[:1], interleaved_timesteps] )
_lowercase : List[Any] = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_lowercase : Optional[Any] = defaultdict(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
# get log sigma
_lowercase : Optional[Any] = sigma.log()
# get distribution
_lowercase : Optional[int] = log_sigma - self.log_sigmas[:, None]
# get sigmas range
_lowercase : Tuple = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
_lowercase : List[Any] = low_idx + 1
_lowercase : int = self.log_sigmas[low_idx]
_lowercase : Any = self.log_sigmas[high_idx]
# interpolate sigmas
_lowercase : Any = (low - log_sigma) / (low - high)
_lowercase : Dict = w.clamp(0 ,1 )
# transform interpolation to time range
_lowercase : List[str] = (1 - w) * low_idx + w * high_idx
_lowercase : Optional[int] = t.view(sigma.shape )
return t
@property
def lowerCamelCase__ ( self ):
return self.sample is None
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = True ,):
_lowercase : Optional[int] = self.index_for_timestep(UpperCAmelCase_ )
# advance index counter by 1
_lowercase : str = timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_lowercase : Any = self.sigmas[step_index]
_lowercase : Any = self.sigmas_interpol[step_index + 1]
_lowercase : Tuple = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
_lowercase : Union[str, Any] = self.sigmas[step_index - 1]
_lowercase : int = self.sigmas_interpol[step_index]
_lowercase : Tuple = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_lowercase : Any = 0
_lowercase : int = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_lowercase : Union[str, Any] = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : Optional[Any] = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_lowercase : str = sigma_hat if self.state_in_first_order else sigma_interpol
_lowercase : List[str] = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_lowercase : List[str] = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_lowercase : Any = sigma_interpol - sigma_hat
# store for 2nd order step
_lowercase : List[Any] = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
_lowercase : Optional[Any] = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
_lowercase : Optional[Any] = sigma_next - sigma_hat
_lowercase : Any = self.sample
_lowercase : Optional[int] = None
_lowercase : str = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_lowercase : int = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_ ):
# mps does not support float64
_lowercase : str = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
_lowercase : Any = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
_lowercase : List[Any] = self.timesteps.to(original_samples.device )
_lowercase : Union[str, Any] = timesteps.to(original_samples.device )
_lowercase : List[Any] = [self.index_for_timestep(UpperCAmelCase_ ,UpperCAmelCase_ ) for t in timesteps]
_lowercase : Optional[Any] = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_lowercase : List[Any] = sigma.unsqueeze(-1 )
_lowercase : int = original_samples + noise * sigma
return noisy_samples
def __len__( self ):
return self.config.num_train_timesteps
| 336 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
UpperCAmelCase: List[str] = logging.get_logger(__name__)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = "upernet"
def __init__( self ,UpperCAmelCase_=None ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=[1, 2, 3, 6] ,UpperCAmelCase_=True ,UpperCAmelCase_=0.4 ,UpperCAmelCase_=3_84 ,UpperCAmelCase_=2_56 ,UpperCAmelCase_=1 ,UpperCAmelCase_=False ,UpperCAmelCase_=2_55 ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
_lowercase : Union[str, Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
elif isinstance(UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[Any] = backbone_config.get("""model_type""" )
_lowercase : str = CONFIG_MAPPING[backbone_model_type]
_lowercase : Dict = config_class.from_dict(UpperCAmelCase_ )
_lowercase : int = backbone_config
_lowercase : Optional[Any] = hidden_size
_lowercase : Any = initializer_range
_lowercase : Union[str, Any] = pool_scales
_lowercase : Tuple = use_auxiliary_head
_lowercase : Dict = auxiliary_loss_weight
_lowercase : str = auxiliary_in_channels
_lowercase : Any = auxiliary_channels
_lowercase : Optional[int] = auxiliary_num_convs
_lowercase : Optional[int] = auxiliary_concat_input
_lowercase : int = loss_ignore_index
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = copy.deepcopy(self.__dict__ )
_lowercase : Tuple = self.backbone_config.to_dict()
_lowercase : Any = self.__class__.model_type
return output
| 336 |
"""simple docstring"""
import pprint
import requests
UpperCAmelCase: Tuple = """https://zenquotes.io/api"""
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
UpperCAmelCase: int = random_quotes()
pprint.pprint(response)
| 336 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase: Optional[int] = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase: int = [
"""GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GitForCausalLM""",
"""GitModel""",
"""GitPreTrainedModel""",
"""GitVisionModel""",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
UpperCAmelCase: int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 336 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str
SCREAMING_SNAKE_CASE_ : int
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(__UpperCAmelCase ) )]
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
_lowercase : Tuple = all_rotations(__UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_lowercase : BWTTransformDict = {
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(__UpperCAmelCase ),
}
return response
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
_lowercase : Optional[Any] = int(__UpperCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(__UpperCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
_lowercase : int = [""""""] * len(__UpperCAmelCase )
for _ in range(len(__UpperCAmelCase ) ):
for i in range(len(__UpperCAmelCase ) ):
_lowercase : Union[str, Any] = bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = """Provide a string that I will generate its BWT transform: """
UpperCAmelCase: int = input(entry_msg).strip()
UpperCAmelCase: List[str] = bwt_transform(s)
print(
F'Burrows Wheeler transform for string \'{s}\' results '
F'in \'{result["bwt_string"]}\''
)
UpperCAmelCase: Union[str, Any] = reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
print(
F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
F'we get original string \'{original_string}\''
)
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase: List[str] = 1.6021e-19 # units = C
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ):
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = [randint(-1000 , 1000 ) for i in range(10 )]
_lowercase : Tuple = randint(-5000 , 5000 )
return (arr, r)
UpperCAmelCase: int = make_dataset()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
for triplet in permutations(__UpperCAmelCase , 3 ):
if sum(__UpperCAmelCase ) == target:
return tuple(sorted(__UpperCAmelCase ) )
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
arr.sort()
_lowercase : Optional[Any] = len(__UpperCAmelCase )
for i in range(n - 1 ):
_lowercase , _lowercase : str = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Tuple = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
_lowercase : Union[str, Any] = """
triplet_sum1(*dataset)
"""
_lowercase : Union[str, Any] = """
triplet_sum2(*dataset)
"""
_lowercase : Dict = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
_lowercase : Any = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
return (min(__UpperCAmelCase ), min(__UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase: Any = solution_times()
print(F'The time for naive implementation is {times[0]}.')
print(F'The time for optimized implementation is {times[1]}.')
| 336 | 1 |
"""simple docstring"""
import requests
UpperCAmelCase: Dict = """YOUR API KEY"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase = giphy_api_key ):
_lowercase : Optional[Any] = """+""".join(query.split() )
_lowercase : str = F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
_lowercase : Any = requests.get(__UpperCAmelCase ).json()["""data"""]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("""\n""".join(get_gifs("""space ship""")))
| 336 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE_ : Optional[int] = "BlipImageProcessor"
SCREAMING_SNAKE_CASE_ : Tuple = "AutoTokenizer"
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
super().__init__(UpperCAmelCase_ ,UpperCAmelCase_ )
# add QFormer tokenizer
_lowercase : Optional[int] = qformer_tokenizer
def __call__( self ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = True ,UpperCAmelCase_ = False ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = 0 ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = False ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
_lowercase : List[Any] = BatchFeature()
if text is not None:
_lowercase : List[str] = self.tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
encoding.update(UpperCAmelCase_ )
_lowercase : Dict = self.qformer_tokenizer(
text=UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,max_length=UpperCAmelCase_ ,stride=UpperCAmelCase_ ,pad_to_multiple_of=UpperCAmelCase_ ,return_attention_mask=UpperCAmelCase_ ,return_overflowing_tokens=UpperCAmelCase_ ,return_special_tokens_mask=UpperCAmelCase_ ,return_offsets_mapping=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ ,return_length=UpperCAmelCase_ ,verbose=UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ ,**UpperCAmelCase_ ,)
_lowercase : str = qformer_text_encoding.pop("""input_ids""" )
_lowercase : int = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
_lowercase : Optional[Any] = self.image_processor(UpperCAmelCase_ ,return_tensors=UpperCAmelCase_ )
encoding.update(UpperCAmelCase_ )
return encoding
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
return self.tokenizer.decode(*UpperCAmelCase_ ,**UpperCAmelCase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = self.tokenizer.model_input_names
_lowercase : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
if os.path.isfile(UpperCAmelCase_ ):
raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(UpperCAmelCase_ ,exist_ok=UpperCAmelCase_ )
_lowercase : Any = os.path.join(UpperCAmelCase_ ,"""qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(UpperCAmelCase_ )
return super().save_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
_lowercase : Optional[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase_ ,subfolder="""qformer_tokenizer""" )
_lowercase : Any = cls._get_arguments_from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
args.append(UpperCAmelCase_ )
return cls(*UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCAmelCase: Tuple = """3"""
print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
print("""Torch version:""", None)
try:
import deepspeed
print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
print("""DeepSpeed version:""", None)
try:
import tensorflow as tf
print("""TensorFlow version:""", tf.__version__)
print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
print("""TensorFlow version:""", None)
| 336 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase: Tuple = logging.get_logger(__name__)
UpperCAmelCase: List[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "trajectory_transformer"
SCREAMING_SNAKE_CASE_ : Optional[int] = ["past_key_values"]
SCREAMING_SNAKE_CASE_ : Tuple = {
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self ,UpperCAmelCase_=1_00 ,UpperCAmelCase_=5 ,UpperCAmelCase_=1 ,UpperCAmelCase_=1 ,UpperCAmelCase_=2_49 ,UpperCAmelCase_=6 ,UpperCAmelCase_=17 ,UpperCAmelCase_=25 ,UpperCAmelCase_=4 ,UpperCAmelCase_=4 ,UpperCAmelCase_=1_28 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.0006 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=1 ,UpperCAmelCase_=True ,UpperCAmelCase_=1 ,UpperCAmelCase_=5_02_56 ,UpperCAmelCase_=5_02_56 ,**UpperCAmelCase_ ,):
_lowercase : Dict = vocab_size
_lowercase : List[str] = action_weight
_lowercase : int = reward_weight
_lowercase : List[Any] = value_weight
_lowercase : List[str] = max_position_embeddings
_lowercase : Any = block_size
_lowercase : Any = action_dim
_lowercase : List[str] = observation_dim
_lowercase : Union[str, Any] = transition_dim
_lowercase : str = learning_rate
_lowercase : Tuple = n_layer
_lowercase : Optional[int] = n_head
_lowercase : List[str] = n_embd
_lowercase : List[str] = embd_pdrop
_lowercase : Optional[Any] = attn_pdrop
_lowercase : List[Any] = resid_pdrop
_lowercase : str = initializer_range
_lowercase : Optional[Any] = layer_norm_eps
_lowercase : List[Any] = kaiming_initializer_range
_lowercase : List[Any] = use_cache
super().__init__(pad_token_id=UpperCAmelCase_ ,bos_token_id=UpperCAmelCase_ ,eos_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
| 336 | 1 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=False ):
try:
_lowercase : Union[str, Any] = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_lowercase : Tuple = default
else:
# KEY is set, convert it to True or False.
try:
_lowercase : Optional[int] = strtobool(__UpperCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"""If set, {key} must be yes or no.""" )
return _value
UpperCAmelCase: Union[str, Any] = parse_flag_from_env("""RUN_SLOW""", default=False)
UpperCAmelCase: Dict = parse_flag_from_env("""RUN_REMOTE""", default=False)
UpperCAmelCase: Tuple = parse_flag_from_env("""RUN_LOCAL""", default=True)
UpperCAmelCase: Tuple = parse_flag_from_env("""RUN_PACKAGED""", default=True)
# Compression
UpperCAmelCase: Union[str, Any] = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="""test requires lz4""")
UpperCAmelCase: List[Any] = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="""test requires py7zr""")
UpperCAmelCase: Optional[int] = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="""test requires zstandard""")
# Audio
UpperCAmelCase: Union[str, Any] = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec("""soundfile""") is None or version.parse(importlib_metadata.version("""soundfile""")) < version.parse("""0.12.0"""),
reason="""test requires sndfile>=0.12.1: 'pip install \"soundfile>=0.12.1\"'; """,
)
# Beam
UpperCAmelCase: str = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse("""0.3.2"""),
reason="""test requires apache-beam and a compatible dill version""",
)
# Dill-cloudpickle compatibility
UpperCAmelCase: Tuple = pytest.mark.skipif(
config.DILL_VERSION <= version.parse("""0.3.2"""),
reason="""test requires dill>0.3.2 for cloudpickle compatibility""",
)
# Windows
UpperCAmelCase: Optional[int] = pytest.mark.skipif(
sys.platform == """win32""",
reason="""test should not be run on Windows""",
)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import faiss # noqa
except ImportError:
_lowercase : List[Any] = unittest.skip("""test requires faiss""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import regex # noqa
except ImportError:
_lowercase : int = unittest.skip("""test requires regex""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import elasticsearch # noqa
except ImportError:
_lowercase : int = unittest.skip("""test requires elasticsearch""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import sqlalchemy # noqa
except ImportError:
_lowercase : Dict = unittest.skip("""test requires sqlalchemy""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not config.TORCH_AVAILABLE:
_lowercase : Optional[int] = unittest.skip("""test requires PyTorch""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not config.TF_AVAILABLE:
_lowercase : Any = unittest.skip("""test requires TensorFlow""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not config.JAX_AVAILABLE:
_lowercase : str = unittest.skip("""test requires JAX""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not config.PIL_AVAILABLE:
_lowercase : str = unittest.skip("""test requires Pillow""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import transformers # noqa F401
except ImportError:
return unittest.skip("""test requires transformers""" )(__UpperCAmelCase )
else:
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip("""test requires tiktoken""" )(__UpperCAmelCase )
else:
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import spacy # noqa F401
except ImportError:
return unittest.skip("""test requires spacy""" )(__UpperCAmelCase )
else:
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
def _require_spacy_model(__UpperCAmelCase ):
try:
import spacy # noqa F401
spacy.load(__UpperCAmelCase )
except ImportError:
return unittest.skip("""test requires spacy""" )(__UpperCAmelCase )
except OSError:
return unittest.skip("""test requires spacy model '{}'""".format(__UpperCAmelCase ) )(__UpperCAmelCase )
else:
return test_case
return _require_spacy_model
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip("""test requires pyspark""" )(__UpperCAmelCase )
else:
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip("""test requires joblibspark""" )(__UpperCAmelCase )
else:
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not _run_slow_tests or _run_slow_tests == 0:
_lowercase : Any = unittest.skip("""test is slow""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not _run_local_tests or _run_local_tests == 0:
_lowercase : Optional[int] = unittest.skip("""test is local""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not _run_packaged_tests or _run_packaged_tests == 0:
_lowercase : List[Any] = unittest.skip("""test is packaged""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not _run_remote_tests or _run_remote_tests == 0:
_lowercase : Optional[Any] = unittest.skip("""test requires remote""" )(__UpperCAmelCase )
return test_case
def __SCREAMING_SNAKE_CASE ( *__UpperCAmelCase ):
def decorate(cls ):
for name, fn in cls.__dict__.items():
if callable(__UpperCAmelCase ) and name.startswith("""test""" ):
for decorator in decorators:
_lowercase : Optional[int] = decorator(__UpperCAmelCase )
setattr(cls , __UpperCAmelCase , __UpperCAmelCase )
return cls
return decorate
class UpperCamelCase ( snake_case ):
"""simple docstring"""
pass
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
SCREAMING_SNAKE_CASE_ : List[str] = 1
SCREAMING_SNAKE_CASE_ : Optional[int] = 2
@contextmanager
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase=OfflineSimulationMode.CONNECTION_FAILS , __UpperCAmelCase=1E-1_6 ):
_lowercase : List[str] = requests.Session().request
def timeout_request(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
# Change the url to an invalid url so that the connection hangs
_lowercase : str = """https://10.255.255.1"""
if kwargs.get("""timeout""" ) is None:
raise RequestWouldHangIndefinitelyError(
F"""Tried a call to {url} in offline mode with no timeout set. Please set a timeout.""" )
_lowercase : Dict = timeout
try:
return online_request(__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
_lowercase : Optional[Any] = url
_lowercase : Dict = e.args[0]
_lowercase : Optional[Any] = (max_retry_error.args[0].replace("""10.255.255.1""" , F"""OfflineMock[{url}]""" ),)
_lowercase : Union[str, Any] = (max_retry_error,)
raise
def raise_connection_error(__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
raise requests.ConnectionError("""Offline mode is enabled.""" , request=__UpperCAmelCase )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch("""requests.Session.send""" , __UpperCAmelCase ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch("""requests.Session.request""" , __UpperCAmelCase ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch("""datasets.config.HF_DATASETS_OFFLINE""" , __UpperCAmelCase ):
yield
else:
raise ValueError("""Please use a value from the OfflineSimulationMode enum.""" )
@contextmanager
def __SCREAMING_SNAKE_CASE ( *__UpperCAmelCase , **__UpperCAmelCase ):
_lowercase : Optional[int] = str(Path().resolve() )
with tempfile.TemporaryDirectory(*__UpperCAmelCase , **__UpperCAmelCase ) as tmp_dir:
try:
os.chdir(__UpperCAmelCase )
yield
finally:
os.chdir(__UpperCAmelCase )
@contextmanager
def __SCREAMING_SNAKE_CASE ( ):
import gc
gc.collect()
_lowercase : str = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def __SCREAMING_SNAKE_CASE ( ):
import gc
gc.collect()
_lowercase : Tuple = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
return deepcopy(__UpperCAmelCase ).integers(0 , 100 , 10 ).tolist() == deepcopy(__UpperCAmelCase ).integers(0 , 100 , 10 ).tolist()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
import decorator
from requests.exceptions import HTTPError
def _wrapper(__UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase ):
try:
return func(*__UpperCAmelCase , **__UpperCAmelCase )
except HTTPError as err:
if str(__UpperCAmelCase ).startswith("""500""" ) or str(__UpperCAmelCase ).startswith("""502""" ):
pytest.xfail(str(__UpperCAmelCase ) )
raise err
return decorator.decorator(_wrapper , __UpperCAmelCase )
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Optional[Any] = returncode
_lowercase : int = stdout
_lowercase : Tuple = stderr
async def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
while True:
_lowercase : Tuple = await stream.readline()
if line:
callback(__UpperCAmelCase )
else:
break
async def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=False , __UpperCAmelCase=False ):
if echo:
print("""\nRunning: """ , """ """.join(__UpperCAmelCase ) )
_lowercase : Dict = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=__UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=__UpperCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_lowercase : str = []
_lowercase : str = []
def tee(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase="" ):
_lowercase : List[Any] = line.decode("""utf-8""" ).rstrip()
sink.append(__UpperCAmelCase )
if not quiet:
print(__UpperCAmelCase , __UpperCAmelCase , file=__UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda __UpperCAmelCase : tee(__UpperCAmelCase , __UpperCAmelCase , sys.stdout , label="""stdout:""" ) ),
_read_stream(p.stderr , lambda __UpperCAmelCase : tee(__UpperCAmelCase , __UpperCAmelCase , sys.stderr , label="""stderr:""" ) ),
] , timeout=__UpperCAmelCase , )
return _RunOutput(await p.wait() , __UpperCAmelCase , __UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=180 , __UpperCAmelCase=False , __UpperCAmelCase=True ):
_lowercase : List[Any] = asyncio.get_event_loop()
_lowercase : int = loop.run_until_complete(
_stream_subprocess(__UpperCAmelCase , env=__UpperCAmelCase , stdin=__UpperCAmelCase , timeout=__UpperCAmelCase , quiet=__UpperCAmelCase , echo=__UpperCAmelCase ) )
_lowercase : str = """ """.join(__UpperCAmelCase )
if result.returncode > 0:
_lowercase : Optional[int] = """\n""".join(result.stderr )
raise RuntimeError(
F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
F"""The combined stderr from workers follows:\n{stderr}""" )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(F"""'{cmd_str}' produced no output.""" )
return result
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : List[str] = os.environ.get("""PYTEST_XDIST_WORKER""" , """gw0""" )
_lowercase : Any = re.sub(R"""^gw""" , """""" , __UpperCAmelCase , 0 , re.M )
return int(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : int = 29500
_lowercase : Optional[int] = pytest_xdist_worker_id()
return port + uniq_delta
| 336 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase: Any = logging.get_logger(__name__)
UpperCAmelCase: List[str] = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = "instructblip_vision_model"
def __init__( self ,UpperCAmelCase_=14_08 ,UpperCAmelCase_=61_44 ,UpperCAmelCase_=39 ,UpperCAmelCase_=16 ,UpperCAmelCase_=2_24 ,UpperCAmelCase_=14 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=1E-6 ,UpperCAmelCase_=0.0 ,UpperCAmelCase_=1E-10 ,UpperCAmelCase_=True ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : Optional[Any] = hidden_size
_lowercase : Tuple = intermediate_size
_lowercase : List[Any] = num_hidden_layers
_lowercase : Tuple = num_attention_heads
_lowercase : Optional[Any] = patch_size
_lowercase : Optional[Any] = image_size
_lowercase : Union[str, Any] = initializer_range
_lowercase : Optional[Any] = attention_dropout
_lowercase : List[Any] = layer_norm_eps
_lowercase : Optional[int] = hidden_act
_lowercase : Tuple = qkv_bias
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : List[Any] = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : int = 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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "instructblip_qformer"
def __init__( self ,UpperCAmelCase_=3_05_22 ,UpperCAmelCase_=7_68 ,UpperCAmelCase_=12 ,UpperCAmelCase_=12 ,UpperCAmelCase_=30_72 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=0 ,UpperCAmelCase_="absolute" ,UpperCAmelCase_=2 ,UpperCAmelCase_=14_08 ,**UpperCAmelCase_ ,):
super().__init__(pad_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : List[Any] = vocab_size
_lowercase : List[Any] = hidden_size
_lowercase : str = num_hidden_layers
_lowercase : List[str] = num_attention_heads
_lowercase : Optional[Any] = hidden_act
_lowercase : int = intermediate_size
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Optional[Any] = attention_probs_dropout_prob
_lowercase : List[Any] = max_position_embeddings
_lowercase : Tuple = initializer_range
_lowercase : Optional[int] = layer_norm_eps
_lowercase : Any = position_embedding_type
_lowercase : Dict = cross_attention_frequency
_lowercase : Optional[Any] = encoder_hidden_size
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,**UpperCAmelCase_ ):
cls._set_token_in_kwargs(UpperCAmelCase_ )
_lowercase , _lowercase : Dict = cls.get_config_dict(UpperCAmelCase_ ,**UpperCAmelCase_ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
_lowercase : str = config_dict["""qformer_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(UpperCAmelCase_ ,**UpperCAmelCase_ )
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "instructblip"
SCREAMING_SNAKE_CASE_ : List[str] = True
def __init__( self ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=None ,UpperCAmelCase_=32 ,**UpperCAmelCase_ ):
super().__init__(**UpperCAmelCase_ )
if vision_config is None:
_lowercase : str = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
_lowercase : Any = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
_lowercase : Optional[int] = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
_lowercase : int = InstructBlipVisionConfig(**UpperCAmelCase_ )
_lowercase : Optional[int] = InstructBlipQFormerConfig(**UpperCAmelCase_ )
_lowercase : Dict = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
_lowercase : str = CONFIG_MAPPING[text_model_type](**UpperCAmelCase_ )
_lowercase : str = self.text_config.tie_word_embeddings
_lowercase : Union[str, Any] = self.text_config.is_encoder_decoder
_lowercase : List[str] = num_query_tokens
_lowercase : List[str] = self.vision_config.hidden_size
_lowercase : Dict = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowercase : Union[str, Any] = 1.0
_lowercase : Dict = 0.02
@classmethod
def lowerCamelCase__ ( cls ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,**UpperCAmelCase_ ,):
return cls(
vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
_lowercase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_lowercase : int = self.vision_config.to_dict()
_lowercase : Any = self.qformer_config.to_dict()
_lowercase : Any = self.text_config.to_dict()
_lowercase : Optional[int] = self.__class__.model_type
return output
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = BlenderbotConfig
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {}
SCREAMING_SNAKE_CASE_ : Tuple = "gelu"
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=13 ,UpperCAmelCase_=7 ,UpperCAmelCase_=True ,UpperCAmelCase_=False ,UpperCAmelCase_=99 ,UpperCAmelCase_=32 ,UpperCAmelCase_=2 ,UpperCAmelCase_=4 ,UpperCAmelCase_=37 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=20 ,UpperCAmelCase_=2 ,UpperCAmelCase_=1 ,UpperCAmelCase_=0 ,):
_lowercase : Optional[int] = parent
_lowercase : Union[str, Any] = batch_size
_lowercase : List[str] = seq_length
_lowercase : Dict = is_training
_lowercase : Optional[int] = use_labels
_lowercase : int = vocab_size
_lowercase : Union[str, Any] = hidden_size
_lowercase : Optional[Any] = num_hidden_layers
_lowercase : Union[str, Any] = num_attention_heads
_lowercase : Tuple = intermediate_size
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Tuple = attention_probs_dropout_prob
_lowercase : Optional[int] = max_position_embeddings
_lowercase : List[str] = eos_token_id
_lowercase : Dict = pad_token_id
_lowercase : Any = bos_token_id
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
_lowercase : Union[str, Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
_lowercase : Tuple = tf.concat([input_ids, eos_tensor] ,axis=1 )
_lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowercase : Union[str, Any] = self.config_cls(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,)
_lowercase : int = prepare_blenderbot_inputs_dict(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ )
return config, inputs_dict
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Any = TFBlenderbotModel(config=UpperCAmelCase_ ).get_decoder()
_lowercase : Union[str, Any] = inputs_dict["""input_ids"""]
_lowercase : Optional[Any] = input_ids[:1, :]
_lowercase : Union[str, Any] = inputs_dict["""attention_mask"""][:1, :]
_lowercase : List[Any] = inputs_dict["""head_mask"""]
_lowercase : Dict = 1
# first forward pass
_lowercase : List[str] = model(UpperCAmelCase_ ,attention_mask=UpperCAmelCase_ ,head_mask=UpperCAmelCase_ ,use_cache=UpperCAmelCase_ )
_lowercase , _lowercase : Optional[Any] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_lowercase : Any = ids_tensor((self.batch_size, 3) ,config.vocab_size )
_lowercase : str = tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )
# append to next input_ids and
_lowercase : Optional[Any] = tf.concat([input_ids, next_tokens] ,axis=-1 )
_lowercase : Tuple = tf.concat([attention_mask, next_attn_mask] ,axis=-1 )
_lowercase : str = model(UpperCAmelCase_ ,attention_mask=UpperCAmelCase_ )[0]
_lowercase : Dict = model(UpperCAmelCase_ ,attention_mask=UpperCAmelCase_ ,past_key_values=UpperCAmelCase_ )[0]
self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )
# select random slice
_lowercase : int = int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
_lowercase : List[str] = output_from_no_past[:, -3:, random_slice_idx]
_lowercase : str = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(UpperCAmelCase_ ,UpperCAmelCase_ ,rtol=1E-3 )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , ):
if attention_mask is None:
_lowercase : Dict = tf.cast(tf.math.not_equal(__UpperCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_lowercase : Optional[int] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_lowercase : Optional[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_lowercase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_lowercase : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase ( snake_case , snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : List[Any] = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : Optional[Any] = (
{
"conversational": TFBlenderbotForConditionalGeneration,
"feature-extraction": TFBlenderbotModel,
"summarization": TFBlenderbotForConditionalGeneration,
"text2text-generation": TFBlenderbotForConditionalGeneration,
"translation": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE_ : List[str] = True
SCREAMING_SNAKE_CASE_ : Any = False
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
def lowerCamelCase__ ( self ):
_lowercase : List[str] = TFBlenderbotModelTester(self )
_lowercase : Dict = ConfigTester(self ,config_class=UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
_lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_ )
@require_tokenizers
@require_tf
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = ["My friends are cool but they eat too many carbs."]
SCREAMING_SNAKE_CASE_ : str = "facebook/blenderbot-400M-distill"
@cached_property
def lowerCamelCase__ ( self ):
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def lowerCamelCase__ ( self ):
_lowercase : int = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def lowerCamelCase__ ( self ):
_lowercase : Optional[Any] = self.tokenizer(self.src_text ,return_tensors="""tf""" )
_lowercase : Dict = self.model.generate(
model_inputs.input_ids ,)
_lowercase : Dict = self.tokenizer.batch_decode(generated_ids.numpy() ,skip_special_tokens=UpperCAmelCase_ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 336 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
if k in (0.04, 0.06):
_lowercase : Optional[Any] = k
_lowercase : Optional[Any] = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ):
return str(self.k )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = cva.imread(UpperCAmelCase_ ,0 )
_lowercase , _lowercase : Dict = img.shape
_lowercase : list[list[int]] = []
_lowercase : int = img.copy()
_lowercase : List[str] = cva.cvtColor(UpperCAmelCase_ ,cva.COLOR_GRAY2RGB )
_lowercase , _lowercase : Optional[Any] = np.gradient(UpperCAmelCase_ )
_lowercase : Optional[int] = dx**2
_lowercase : Optional[Any] = dy**2
_lowercase : Optional[Any] = dx * dy
_lowercase : List[str] = 0.04
_lowercase : Optional[Any] = self.window_size // 2
for y in range(UpperCAmelCase_ ,h - offset ):
for x in range(UpperCAmelCase_ ,w - offset ):
_lowercase : Optional[Any] = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Dict = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : Union[str, Any] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowercase : int = (wxx * wyy) - (wxy**2)
_lowercase : Union[str, Any] = wxx + wyy
_lowercase : Union[str, Any] = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) ,0 )
color_img.itemset((y, x, 1) ,0 )
color_img.itemset((y, x, 2) ,2_55 )
return color_img, corner_list
if __name__ == "__main__":
UpperCAmelCase: Optional[int] = HarrisCorner(0.04, 3)
UpperCAmelCase , UpperCAmelCase: List[Any] = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 336 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCAmelCase ) )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
# Base Case
if index == len(__UpperCAmelCase ):
return True
# Recursive Step
for i in range(__UpperCAmelCase ):
if valid_coloring(graph[index] , __UpperCAmelCase , __UpperCAmelCase ):
# Color current vertex
_lowercase : Dict = i
# Validate coloring
if util_color(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , index + 1 ):
return True
# Backtrack
_lowercase : Dict = -1
return False
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Union[str, Any] = [-1] * len(__UpperCAmelCase )
if util_color(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , 0 ):
return colored_vertices
return []
| 336 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = LEDTokenizer
SCREAMING_SNAKE_CASE_ : List[str] = LEDTokenizerFast
SCREAMING_SNAKE_CASE_ : List[str] = True
def lowerCamelCase__ ( self ):
super().setUp()
_lowercase : Union[str, Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
_lowercase : List[Any] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
_lowercase : Dict = {"""unk_token""": """<unk>"""}
_lowercase : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
_lowercase : Any = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(UpperCAmelCase_ ,max_length=len(UpperCAmelCase_ ) ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
_lowercase : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIn("""input_ids""" ,UpperCAmelCase_ )
self.assertIn("""attention_mask""" ,UpperCAmelCase_ )
self.assertNotIn("""labels""" ,UpperCAmelCase_ )
self.assertNotIn("""decoder_attention_mask""" ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Dict = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(text_target=UpperCAmelCase_ ,max_length=32 ,padding="""max_length""" ,return_tensors="""pt""" )
self.assertEqual(32 ,targets["""input_ids"""].shape[1] )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : List[Any] = tokenizer(
["""I am a small frog""" * 10_24, """I am a small frog"""] ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = ["""A long paragraph for summarization."""]
_lowercase : Dict = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : List[str] = tokenizer(text_target=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : Union[str, Any] = inputs["""input_ids"""]
_lowercase : List[str] = targets["""input_ids"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : str = ["""Summary of the text.""", """Another summary."""]
_lowercase : Optional[int] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
_lowercase : Any = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ )
_lowercase : str = [[0] * len(UpperCAmelCase_ ) for x in encoded_output["""input_ids"""]]
_lowercase : Optional[int] = tokenizer.pad(UpperCAmelCase_ )
self.assertSequenceEqual(outputs["""global_attention_mask"""] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowercase : int = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Optional[int] = self.tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = """A, <mask> AllenNLP sentence."""
_lowercase : List[Any] = tokenizer_r.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
_lowercase : Any = tokenizer_p.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) ,sum(tokens_p["""token_type_ids"""] ) )
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) ,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) ,)
_lowercase : str = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
_lowercase : str = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
self.assertSequenceEqual(tokens_p["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
| 336 | 1 |
"""simple docstring"""
UpperCAmelCase: List[str] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 336 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Any = f.readlines()
_lowercase : Optional[int] = F"""class {class_name}("""
_lowercase : List[str] = F"""{4 * " "}def {test_name}("""
_lowercase : List[Any] = F"""{8 * " "}{correct_line.split()[0]}"""
_lowercase : int = F"""{16 * " "}{correct_line.split()[0]}"""
_lowercase : str = False
_lowercase : Optional[Any] = False
_lowercase : Union[str, Any] = False
_lowercase : Any = False
_lowercase : int = 0
_lowercase : Tuple = 0
_lowercase : Union[str, Any] = []
for line in lines:
if line.startswith(__UpperCAmelCase ):
_lowercase : List[str] = True
elif in_class and line.startswith(__UpperCAmelCase ):
_lowercase : str = True
elif in_class and in_func and (line.startswith(__UpperCAmelCase ) or line.startswith(__UpperCAmelCase )):
_lowercase : Union[str, Any] = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
_lowercase : Optional[int] = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
_lowercase : Optional[Any] = True
if in_class and in_func and in_line and insert_line:
new_lines.append(F"""{spaces * " "}{correct_line}""" )
_lowercase : Union[str, Any] = False
else:
new_lines.append(__UpperCAmelCase )
with open(__UpperCAmelCase , """w""" ) as f:
for line in new_lines:
f.write(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None ):
if fail is not None:
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Dict = {l.strip() for l in f.readlines()}
else:
_lowercase : int = None
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : int = f.readlines()
_lowercase : int = defaultdict(__UpperCAmelCase )
for line in correct_lines:
_lowercase , _lowercase , _lowercase , _lowercase : int = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase: List[Any] = argparse.ArgumentParser()
parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
UpperCAmelCase: Any = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 336 | 1 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Dict = [randint(-1000 , 1000 ) for i in range(10 )]
_lowercase : Tuple = randint(-5000 , 5000 )
return (arr, r)
UpperCAmelCase: int = make_dataset()
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
for triplet in permutations(__UpperCAmelCase , 3 ):
if sum(__UpperCAmelCase ) == target:
return tuple(sorted(__UpperCAmelCase ) )
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
arr.sort()
_lowercase : Optional[Any] = len(__UpperCAmelCase )
for i in range(n - 1 ):
_lowercase , _lowercase : str = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : Tuple = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
_lowercase : Union[str, Any] = """
triplet_sum1(*dataset)
"""
_lowercase : Union[str, Any] = """
triplet_sum2(*dataset)
"""
_lowercase : Dict = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
_lowercase : Any = repeat(setup=__UpperCAmelCase , stmt=__UpperCAmelCase , repeat=5 , number=10000 )
return (min(__UpperCAmelCase ), min(__UpperCAmelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase: Any = solution_times()
print(F'The time for naive implementation is {times[0]}.')
print(F'The time for optimized implementation is {times[1]}.')
| 336 |
"""simple docstring"""
UpperCAmelCase: List[str] = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 336 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCAmelCase: List[Any] = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if isinstance(__UpperCAmelCase , np.ndarray ):
return list(tensor.shape )
_lowercase : Union[str, Any] = tf.shape(__UpperCAmelCase )
if tensor.shape == tf.TensorShape(__UpperCAmelCase ):
return dynamic
_lowercase : int = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCAmelCase )]
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None ):
return tf.nn.softmax(logits=logits + 1E-9 , axis=__UpperCAmelCase , name=__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __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
_lowercase , _lowercase : str = 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
_lowercase : int = [1] * inputs.shape.rank
_lowercase : Union[str, Any] = shape_list(__UpperCAmelCase )[axis]
_lowercase : Any = tf.reshape(__UpperCAmelCase , __UpperCAmelCase )
_lowercase : Any = tf.reshape(__UpperCAmelCase , __UpperCAmelCase )
# Compute layer normalization using the batch_normalization
# function.
_lowercase : Union[str, Any] = tf.nn.batch_normalization(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , offset=__UpperCAmelCase , scale=__UpperCAmelCase , variance_epsilon=__UpperCAmelCase , )
return outputs
def __SCREAMING_SNAKE_CASE ( __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
_lowercase : Optional[int] = tf.shape(__UpperCAmelCase )
_lowercase : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
_lowercase : List[str] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(__UpperCAmelCase , __UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , tf.Tensor ):
_lowercase : Tuple = tf.convert_to_tensor(__UpperCAmelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
_lowercase : Dict = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
_lowercase : Union[str, Any] = 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))
_lowercase : Union[str, Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __SCREAMING_SNAKE_CASE ( __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 __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Optional[Any] = 64512
# 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.
_lowercase : str = [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}""" )
_lowercase : str = np.asarray(__UpperCAmelCase )
_lowercase : Any = 1
_lowercase : Union[str, Any] = 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
_lowercase : Any = np.array_split(__UpperCAmelCase , __UpperCAmelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(__UpperCAmelCase ):
_lowercase : Any = chunk_data
else:
_lowercase : List[str] = data
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
if name in group.attrs:
_lowercase : str = [n.decode("""utf8""" ) if hasattr(__UpperCAmelCase , """decode""" ) else n for n in group.attrs[name]]
else:
_lowercase : Union[str, Any] = []
_lowercase : Any = 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 __SCREAMING_SNAKE_CASE ( __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 )
| 336 |
"""simple docstring"""
UpperCAmelCase: str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCAmelCase: Any = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase: int = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 336 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase: str = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase: Tuple = [
"""PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PegasusXForConditionalGeneration""",
"""PegasusXModel""",
"""PegasusXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
UpperCAmelCase: str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 336 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
UpperCAmelCase: List[Any] = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
def constraint_to_multiple_of(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=0 , __UpperCAmelCase=None ):
_lowercase : Union[str, Any] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowercase : str = math.floor(val / multiple ) * multiple
if x < min_val:
_lowercase : Dict = math.ceil(val / multiple ) * multiple
return x
_lowercase : List[str] = (output_size, output_size) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else output_size
_lowercase , _lowercase : List[Any] = get_image_size(__UpperCAmelCase )
_lowercase , _lowercase : Union[str, Any] = output_size
# determine new height and width
_lowercase : str = output_height / input_height
_lowercase : List[Any] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowercase : str = scale_width
else:
# fit height
_lowercase : int = scale_height
_lowercase : List[Any] = constraint_to_multiple_of(scale_height * input_height , multiple=__UpperCAmelCase )
_lowercase : Optional[Any] = constraint_to_multiple_of(scale_width * input_width , multiple=__UpperCAmelCase )
return (new_height, new_width)
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["pixel_values"]
def __init__( self ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = PILImageResampling.BILINEAR ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = 1 / 2_55 ,UpperCAmelCase_ = True ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
super().__init__(**UpperCAmelCase_ )
_lowercase : List[Any] = size if size is not None else {"""height""": 3_84, """width""": 3_84}
_lowercase : str = get_size_dict(UpperCAmelCase_ )
_lowercase : Tuple = do_resize
_lowercase : Any = size
_lowercase : List[Any] = keep_aspect_ratio
_lowercase : Any = ensure_multiple_of
_lowercase : str = resample
_lowercase : Optional[Any] = do_rescale
_lowercase : List[Any] = rescale_factor
_lowercase : Union[str, Any] = do_normalize
_lowercase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowercase : Any = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = False ,UpperCAmelCase_ = 1 ,UpperCAmelCase_ = PILImageResampling.BICUBIC ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
_lowercase : Optional[Any] = get_size_dict(UpperCAmelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_lowercase : Dict = get_resize_output_image_size(
UpperCAmelCase_ ,output_size=(size["""height"""], size["""width"""]) ,keep_aspect_ratio=UpperCAmelCase_ ,multiple=UpperCAmelCase_ ,)
return resize(UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return rescale(UpperCAmelCase_ ,scale=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
return normalize(UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ,data_format=UpperCAmelCase_ ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,UpperCAmelCase_ = ChannelDimension.FIRST ,**UpperCAmelCase_ ,):
_lowercase : Any = do_resize if do_resize is not None else self.do_resize
_lowercase : List[str] = size if size is not None else self.size
_lowercase : int = get_size_dict(UpperCAmelCase_ )
_lowercase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowercase : List[str] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowercase : List[str] = resample if resample is not None else self.resample
_lowercase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowercase : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase : str = do_normalize if do_normalize is not None else self.do_normalize
_lowercase : Optional[int] = image_mean if image_mean is not None else self.image_mean
_lowercase : int = image_std if image_std is not None else self.image_std
_lowercase : Union[str, Any] = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
_lowercase : int = [to_numpy_array(UpperCAmelCase_ ) for image in images]
if do_resize:
_lowercase : Union[str, Any] = [self.resize(image=UpperCAmelCase_ ,size=UpperCAmelCase_ ,resample=UpperCAmelCase_ ) for image in images]
if do_rescale:
_lowercase : int = [self.rescale(image=UpperCAmelCase_ ,scale=UpperCAmelCase_ ) for image in images]
if do_normalize:
_lowercase : str = [self.normalize(image=UpperCAmelCase_ ,mean=UpperCAmelCase_ ,std=UpperCAmelCase_ ) for image in images]
_lowercase : Tuple = [to_channel_dimension_format(UpperCAmelCase_ ,UpperCAmelCase_ ) for image in images]
_lowercase : int = {"""pixel_values""": images}
return BatchFeature(data=UpperCAmelCase_ ,tensor_type=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ):
_lowercase : Union[str, Any] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(UpperCAmelCase_ ):
_lowercase : Tuple = target_sizes.numpy()
_lowercase : Optional[Any] = []
for idx in range(len(UpperCAmelCase_ ) ):
_lowercase : Dict = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="""bilinear""" ,align_corners=UpperCAmelCase_ )
_lowercase : Optional[int] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(UpperCAmelCase_ )
else:
_lowercase : Union[str, Any] = logits.argmax(dim=1 )
_lowercase : Optional[Any] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 336 | 1 |
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