code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Dict =logging.get_logger(__name__)
class lowerCAmelCase__ ( _lowerCamelCase ):
A_ : List[str] = 'timm_backbone'
def __init__( self : Optional[Any] , __UpperCamelCase : Any=None , __UpperCamelCase : List[Any]=3 , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : Optional[Any]=True , __UpperCamelCase : Dict=None , **__UpperCamelCase : Union[str, Any] , ) -> Optional[Any]:
super().__init__(**__UpperCamelCase )
A = backbone
A = num_channels
A = features_only
A = use_pretrained_backbone
A = True
A = out_indices if out_indices is not None else (-1,) | 106 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__snake_case :Any =None
try:
import msvcrt
except ImportError:
__snake_case :Union[str, Any] =None
try:
import fcntl
except ImportError:
__snake_case :str =None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
__snake_case :str =OSError
# Data
# ------------------------------------------------
__snake_case :Any =[
'Timeout',
'BaseFileLock',
'WindowsFileLock',
'UnixFileLock',
'SoftFileLock',
'FileLock',
]
__snake_case :str ='3.0.12'
__snake_case :str =None
def lowerCamelCase_ ( ) -> List[str]:
'''simple docstring'''
global _logger
A = _logger or logging.getLogger(__name__ )
return _logger
class lowerCAmelCase__ ( _lowerCamelCase ):
def __init__( self : Tuple , __UpperCamelCase : Union[str, Any] ) -> List[Any]:
A = lock_file
return None
def __str__( self : List[Any] ) -> int:
A = f'''The file lock \'{self.lock_file}\' could not be acquired.'''
return temp
class lowerCAmelCase__ :
def __init__( self : int , __UpperCamelCase : Union[str, Any] ) -> List[str]:
A = lock
return None
def __enter__( self : Dict ) -> Dict:
return self.lock
def __exit__( self : int , __UpperCamelCase : str , __UpperCamelCase : Optional[int] , __UpperCamelCase : Any ) -> Optional[int]:
self.lock.release()
return None
class lowerCAmelCase__ :
def __init__( self : List[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : List[Any]=-1 , __UpperCamelCase : Optional[Any]=None ) -> Dict:
A = max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
A = self.hash_filename_if_too_long(__UpperCamelCase , __UpperCamelCase )
# The path to the lock file.
A = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
A = None
# The default timeout value.
A = timeout
# We use this lock primarily for the lock counter.
A = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
A = 0
return None
@property
def __UpperCamelCase ( self : str ) -> Union[str, Any]:
return self._lock_file
@property
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
return self._timeout
@timeout.setter
def __UpperCamelCase ( self : Union[str, Any] , __UpperCamelCase : Any ) -> Tuple:
A = float(__UpperCamelCase )
return None
def __UpperCamelCase ( self : Optional[Any] ) -> Any:
raise NotImplementedError()
def __UpperCamelCase ( self : int ) -> str:
raise NotImplementedError()
@property
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
return self._lock_file_fd is not None
def __UpperCamelCase ( self : int , __UpperCamelCase : List[str]=None , __UpperCamelCase : Any=0.0_5 ) -> Any:
# Use the default timeout, if no timeout is provided.
if timeout is None:
A = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
A = id(self )
A = self._lock_file
A = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(f'''Attempting to acquire lock {lock_id} on {lock_filename}''' )
self._acquire()
if self.is_locked:
logger().debug(f'''Lock {lock_id} acquired on {lock_filename}''' )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(f'''Timeout on acquiring lock {lock_id} on {lock_filename}''' )
raise Timeout(self._lock_file )
else:
logger().debug(
f'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...''' )
time.sleep(__UpperCamelCase )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
A = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def __UpperCamelCase ( self : Optional[int] , __UpperCamelCase : Tuple=False ) -> Tuple:
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
A = id(self )
A = self._lock_file
logger().debug(f'''Attempting to release lock {lock_id} on {lock_filename}''' )
self._release()
A = 0
logger().debug(f'''Lock {lock_id} released on {lock_filename}''' )
return None
def __enter__( self : int ) -> Dict:
self.acquire()
return self
def __exit__( self : List[str] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] , __UpperCamelCase : str ) -> Dict:
self.release()
return None
def __del__( self : Union[str, Any] ) -> Optional[int]:
self.release(force=__UpperCamelCase )
return None
def __UpperCamelCase ( self : Union[str, Any] , __UpperCamelCase : str , __UpperCamelCase : int ) -> str:
A = os.path.basename(__UpperCamelCase )
if len(__UpperCamelCase ) > max_length and max_length > 0:
A = os.path.dirname(__UpperCamelCase )
A = str(hash(__UpperCamelCase ) )
A = filename[: max_length - len(__UpperCamelCase ) - 8] + '...' + hashed_filename + '.lock'
return os.path.join(__UpperCamelCase , __UpperCamelCase )
else:
return path
class lowerCAmelCase__ ( _lowerCamelCase ):
def __init__( self : Tuple , __UpperCamelCase : List[str] , __UpperCamelCase : Tuple=-1 , __UpperCamelCase : Optional[Any]=None ) -> Union[str, Any]:
from .file_utils import relative_to_absolute_path
super().__init__(__UpperCamelCase , timeout=__UpperCamelCase , max_filename_length=__UpperCamelCase )
A = '\\\\?\\' + relative_to_absolute_path(self.lock_file )
def __UpperCamelCase ( self : Any ) -> Any:
A = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
A = os.open(self._lock_file , __UpperCamelCase )
except OSError:
pass
else:
try:
msvcrt.locking(__UpperCamelCase , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(__UpperCamelCase )
else:
A = fd
return None
def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
A = self._lock_file_fd
A = None
msvcrt.locking(__UpperCamelCase , msvcrt.LK_UNLCK , 1 )
os.close(__UpperCamelCase )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class lowerCAmelCase__ ( _lowerCamelCase ):
def __init__( self : List[Any] , __UpperCamelCase : Any , __UpperCamelCase : List[Any]=-1 , __UpperCamelCase : Dict=None ) -> Dict:
A = os.statvfs(os.path.dirname(__UpperCamelCase ) ).f_namemax
super().__init__(__UpperCamelCase , timeout=__UpperCamelCase , max_filename_length=__UpperCamelCase )
def __UpperCamelCase ( self : Any ) -> int:
A = os.O_RDWR | os.O_CREAT | os.O_TRUNC
A = os.open(self._lock_file , __UpperCamelCase )
try:
fcntl.flock(__UpperCamelCase , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(__UpperCamelCase )
else:
A = fd
return None
def __UpperCamelCase ( self : Optional[int] ) -> int:
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
A = self._lock_file_fd
A = None
fcntl.flock(__UpperCamelCase , fcntl.LOCK_UN )
os.close(__UpperCamelCase )
return None
class lowerCAmelCase__ ( _lowerCamelCase ):
def __UpperCamelCase ( self : int ) -> Optional[int]:
A = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
A = os.open(self._lock_file , __UpperCamelCase )
except OSError:
pass
else:
A = fd
return None
def __UpperCamelCase ( self : Optional[Any] ) -> List[str]:
os.close(self._lock_file_fd )
A = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
__snake_case :List[str] =None
if msvcrt:
__snake_case :List[Any] =WindowsFileLock
elif fcntl:
__snake_case :Any =UnixFileLock
else:
__snake_case :Tuple =SoftFileLock
if warnings is not None:
warnings.warn('only soft file lock is available') | 106 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( UpperCamelCase_ , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase : Optional[Any] = FunnelTokenizer
__lowerCAmelCase : Optional[Any] = FunnelTokenizerFast
__lowerCAmelCase : int = True
__lowerCAmelCase : List[str] = True
def __UpperCamelCase ( self ) -> List[Any]:
super().setUp()
_a : Optional[Any] = [
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_a : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def __UpperCamelCase ( self , **lowerCamelCase_ ) -> Any:
return FunnelTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def __UpperCamelCase ( self , **lowerCamelCase_ ) -> Optional[int]:
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def __UpperCamelCase ( self , lowerCamelCase_ ) -> str:
_a : Optional[Any] = '''UNwant\u00E9d,running'''
_a : int = '''unwanted, running'''
return input_text, output_text
def __UpperCamelCase ( self ) -> Union[str, Any]:
_a : List[Any] = self.tokenizer_class(self.vocab_file )
_a : int = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(UpperCamelCase__ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [7, 4, 5, 1_0, 8, 9] )
def __UpperCamelCase ( self ) -> List[str]:
_a : Dict = self.get_tokenizers(do_lower_case=UpperCamelCase__ )
for tokenizer in tokenizers:
_a : int = tokenizer('UNwant\u00E9d,running' )
_a : Optional[Any] = len(inputs['input_ids'] ) - 1
self.assertListEqual(inputs['token_type_ids'] , [2] + [0] * sentence_len )
_a : List[Any] = tokenizer('UNwant\u00E9d,running' , 'UNwant\u00E9d,running' )
self.assertListEqual(inputs['token_type_ids'] , [2] + [0] * sentence_len + [1] * sentence_len )
| 712 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a : List[str] = Mock()
_a : str = conn, Mock()
_a : Union[str, Any] = iter([1, None] )
_a : List[str] = lambda A : next(A )
# ===== invoke =====
send_file(filename='mytext.txt' , testing=A )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 424 | 0 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def A_ ( __lowercase , __lowercase = 0.0 , __lowercase = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 357 |
'''simple docstring'''
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> tuple[float, float]:
# Check if the input is valid
if not len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('Both a & b of two equations can\'t be zero.' )
# Extract the coefficients
A_ , A_ , A_ = equationa
A_ , A_ , A_ = equationa
# Calculate the determinants of the matrices
A_ = aa * ba - aa * ba
A_ = ca * ba - ca * ba
A_ = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('Infinite solutions. (Consistent system)' )
else:
raise ValueError('No solution. (Inconsistent system)' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
A_ = determinant_x / determinant
A_ = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 366 | 0 |
"""simple docstring"""
from math import pow
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
A__ = int(pow(UpperCAmelCase_ , UpperCAmelCase_ ) )
if current_sum + i_to_n <= needed_sum:
# If the sum of the powers is less than needed_sum, then continue adding powers.
current_sum += i_to_n
A__ , A__ = backtrack(
UpperCAmelCase_ , UpperCAmelCase_ , current_number + 1 , UpperCAmelCase_ , UpperCAmelCase_ )
current_sum -= i_to_n
if i_to_n < needed_sum:
# If the power of i is less than needed_sum, then try with the next power.
A__ , A__ = backtrack(
UpperCAmelCase_ , UpperCAmelCase_ , current_number + 1 , UpperCAmelCase_ , UpperCAmelCase_ )
return current_sum, solutions_count
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
raise ValueError(
"""Invalid input\n"""
"""needed_sum must be between 1 and 1000, power between 2 and 10.""" )
return backtrack(UpperCAmelCase_ , UpperCAmelCase_ , 1 , 0 , 0 )[1] # Return the solutions_count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 500 |
"""simple docstring"""
from collections import defaultdict
def _snake_case ( UpperCAmelCase_ : int ):
A__ = 1
A__ = True
for v in tree[start]:
if v not in visited:
ret += dfs(UpperCAmelCase_ )
if ret % 2 == 0:
cuts.append(UpperCAmelCase_ )
return ret
def _snake_case ( ):
dfs(1 )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ : Any = 1_0, 9
SCREAMING_SNAKE_CASE_ : Any = defaultdict(list)
SCREAMING_SNAKE_CASE_ : dict[int, bool] = {}
SCREAMING_SNAKE_CASE_ : list[int] = []
SCREAMING_SNAKE_CASE_ : Optional[int] = 0
SCREAMING_SNAKE_CASE_ : Any = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 500 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
lowercase_ = logging.get_logger('''transformers.models.encodec''')
lowercase_ = {
'''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
'''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
'''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
'''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
lowercase_ = {
'''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
'''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
'''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
'''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
'''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
'''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
'''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
'''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
'''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
'''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
'''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
'''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
'''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
'''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
'''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
'''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
'''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
'''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
'''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
lowercase_ = {
'''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
'''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
'''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
'''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
'''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
'''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
'''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
'''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
'''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
'''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
'''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
'''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
'''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
'''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
'''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
'''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
'''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
'''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
lowercase_ = {
'''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
'''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
'''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
'''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
'''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
'''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
'''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
'''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
'''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
'''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
'''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
'''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
'''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
'''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
'''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
'''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
'''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
'''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
'''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
lowercase_ = {
'''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
'''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
'''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
'''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
'''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
'''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
'''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
'''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
'''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
'''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
'''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
'''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
'''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
'''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
'''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
'''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
'''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
'''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
lowercase_ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
lowercase_ = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
lowercase_ = []
lowercase_ = []
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
for attribute in key.split('''.''' ):
__magic_name__ : Optional[Any] = getattr(UpperCAmelCase, UpperCAmelCase )
if weight_type is not None:
__magic_name__ : Tuple = getattr(UpperCAmelCase, UpperCAmelCase ).shape
else:
__magic_name__ : Dict = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
__magic_name__ : Dict = value
elif weight_type == "weight_g":
__magic_name__ : List[str] = value
elif weight_type == "weight_v":
__magic_name__ : Tuple = value
elif weight_type == "bias":
__magic_name__ : Union[str, Any] = value
elif weight_type == "running_mean":
__magic_name__ : int = value
elif weight_type == "running_var":
__magic_name__ : int = value
elif weight_type == "num_batches_tracked":
__magic_name__ : Any = value
elif weight_type == "weight_ih_l0":
__magic_name__ : Optional[int] = value
elif weight_type == "weight_hh_l0":
__magic_name__ : str = value
elif weight_type == "bias_ih_l0":
__magic_name__ : Any = value
elif weight_type == "bias_hh_l0":
__magic_name__ : Union[str, Any] = value
elif weight_type == "weight_ih_l1":
__magic_name__ : List[Any] = value
elif weight_type == "weight_hh_l1":
__magic_name__ : Any = value
elif weight_type == "bias_ih_l1":
__magic_name__ : Dict = value
elif weight_type == "bias_hh_l1":
__magic_name__ : Dict = value
else:
__magic_name__ : Optional[int] = value
logger.info(F'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.''' )
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
for key in ignore_keys:
if key.endswith('''.*''' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
__magic_name__ , __magic_name__ : str = key.split('''.*.''' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase ) ->str:
"""simple docstring"""
__magic_name__ : Optional[int] = []
if model_name == "encodec_24khz" or "encodec_32khz":
__magic_name__ : int = MAPPING_24K
elif model_name == "encodec_48khz":
__magic_name__ : Tuple = MAPPING_48K
else:
raise ValueError(F'''Unsupported model: {model_name}''' )
for name, value in orig_dict.items():
if should_ignore(UpperCAmelCase, UpperCAmelCase ):
logger.info(F'''{name} was ignored''' )
continue
__magic_name__ : int = False
for key, mapped_key in MAPPING.items():
if "*" in key:
__magic_name__ , __magic_name__ : str = key.split('''.*.''' )
if prefix in name and suffix in name:
__magic_name__ : Optional[Any] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('''embed''' ) and name.endswith('''embed_avg''' ):
continue
__magic_name__ : Union[str, Any] = True
if "*" in mapped_key:
__magic_name__ : Optional[int] = name.split(UpperCAmelCase )[0].split('''.''' )[-2]
__magic_name__ : Optional[int] = mapped_key.replace('''*''', UpperCAmelCase )
if "weight_g" in name:
__magic_name__ : Tuple = '''weight_g'''
elif "weight_v" in name:
__magic_name__ : Any = '''weight_v'''
elif "weight_ih_l0" in name:
__magic_name__ : List[Any] = '''weight_ih_l0'''
elif "weight_hh_l0" in name:
__magic_name__ : Any = '''weight_hh_l0'''
elif "bias_ih_l0" in name:
__magic_name__ : Optional[Any] = '''bias_ih_l0'''
elif "bias_hh_l0" in name:
__magic_name__ : Union[str, Any] = '''bias_hh_l0'''
elif "weight_ih_l1" in name:
__magic_name__ : int = '''weight_ih_l1'''
elif "weight_hh_l1" in name:
__magic_name__ : str = '''weight_hh_l1'''
elif "bias_ih_l1" in name:
__magic_name__ : Dict = '''bias_ih_l1'''
elif "bias_hh_l1" in name:
__magic_name__ : List[Any] = '''bias_hh_l1'''
elif "bias" in name:
__magic_name__ : Any = '''bias'''
elif "weight" in name:
__magic_name__ : int = '''weight'''
elif "running_mean" in name:
__magic_name__ : Tuple = '''running_mean'''
elif "running_var" in name:
__magic_name__ : int = '''running_var'''
elif "num_batches_tracked" in name:
__magic_name__ : List[Any] = '''num_batches_tracked'''
else:
__magic_name__ : str = None
set_recursively(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
continue
if not is_used:
unused_weights.append(UpperCAmelCase )
logger.warning(F'''Unused weights: {unused_weights}''' )
@torch.no_grad()
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase=None, UpperCAmelCase=None, ) ->Any:
"""simple docstring"""
if config_path is not None:
__magic_name__ : Optional[int] = EncodecConfig.from_pretrained(UpperCAmelCase )
else:
__magic_name__ : List[str] = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
__magic_name__ : Optional[int] = [8, 5, 4, 4]
__magic_name__ : Tuple = [2.2]
__magic_name__ : Dict = 64
__magic_name__ : str = 3_2000
__magic_name__ : Tuple = 2048
__magic_name__ : List[Any] = False
__magic_name__ : List[Any] = False
__magic_name__ : Tuple = False
elif model_name == "encodec_48khz":
__magic_name__ : Dict = [8, 5, 4, 2]
__magic_name__ : Dict = [3.0, 6.0, 12.0, 24.0]
__magic_name__ : Optional[int] = 4_8000
__magic_name__ : str = 2
__magic_name__ : str = False
__magic_name__ : Optional[Any] = '''time_group_norm'''
__magic_name__ : str = True
__magic_name__ : Optional[int] = 1.0
__magic_name__ : int = 0.01
else:
raise ValueError(F'''Unknown model name: {model_name}''' )
__magic_name__ : Dict = EncodecModel(UpperCAmelCase )
__magic_name__ : Optional[int] = EncodecFeatureExtractor(
feature_size=config.audio_channels, sampling_rate=config.sampling_rate, chunk_length_s=config.chunk_length_s, overlap=config.overlap, )
feature_extractor.save_pretrained(UpperCAmelCase )
__magic_name__ : Optional[Any] = torch.load(UpperCAmelCase )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
__magic_name__ : str = original_checkpoint['''best_state''']
recursively_load_weights(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
model.save_pretrained(UpperCAmelCase )
if repo_id:
print('''Pushing to the hub...''' )
feature_extractor.push_to_hub(UpperCAmelCase )
model.push_to_hub(UpperCAmelCase )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''--model''',
default='''encodec_24khz''',
type=str,
help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
lowercase_ = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 154 |
from timeit import timeit
lowercase_ = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def lowerCAmelCase ( UpperCAmelCase ) ->bool:
"""simple docstring"""
__magic_name__ : List[str] = 0
__magic_name__ : List[Any] = len(UpperCAmelCase ) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def lowerCAmelCase ( UpperCAmelCase ) ->bool:
"""simple docstring"""
__magic_name__ : Optional[Any] = len(UpperCAmelCase ) // 2
__magic_name__ : int = len(UpperCAmelCase )
# We need to traverse till half of the length of string
# as we can get access of the i'th last element from
# i'th index.
# eg: [0,1,2,3,4,5] => 4th index can be accessed
# with the help of 1st index (i==n-i-1)
# where n is length of string
return all(s[i] == s[n - i - 1] for i in range(UpperCAmelCase ) )
def lowerCAmelCase ( UpperCAmelCase ) ->bool:
"""simple docstring"""
if len(UpperCAmelCase ) <= 2:
return True
if s[0] == s[len(UpperCAmelCase ) - 1]:
return is_palindrome_recursive(s[1:-1] )
else:
return False
def lowerCAmelCase ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return s == s[::-1]
def lowerCAmelCase ( UpperCAmelCase ) ->None:
"""simple docstring"""
__magic_name__ : str = F'''all({name}(key) is value for key, value in test_data.items())'''
__magic_name__ : Optional[Any] = F'''from __main__ import test_data, {name}'''
__magic_name__ : int = 50_0000
__magic_name__ : List[str] = timeit(stmt=UpperCAmelCase, setup=UpperCAmelCase, number=UpperCAmelCase )
print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''' )
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(f"{key:21} {value}")
print('''a man a plan a canal panama''')
# finished 500,000 runs in 0.46793 seconds
benchmark_function('''is_palindrome_slice''')
# finished 500,000 runs in 0.85234 seconds
benchmark_function('''is_palindrome''')
# finished 500,000 runs in 1.32028 seconds
benchmark_function('''is_palindrome_recursive''')
# finished 500,000 runs in 2.08679 seconds
benchmark_function('''is_palindrome_traversal''')
| 154 | 1 |
"""simple docstring"""
import os
from math import logaa
def _SCREAMING_SNAKE_CASE ( __snake_case : str = "base_exp.txt" ):
'''simple docstring'''
lowercase = 0
lowercase = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__snake_case ) , __snake_case ) ) ):
lowercase , lowercase = list(map(__snake_case , line.split(',' ) ) )
if x * logaa(__snake_case ) > largest:
lowercase = x * logaa(__snake_case )
lowercase = i + 1
return result
if __name__ == "__main__":
print(solution())
| 134 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class a :
def UpperCamelCase_ ( self ):
torch.manual_seed(0 )
lowercase = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
lowercase = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
sample_size=3_2 , layers_per_block=1 , block_out_channels=[3_2, 6_4] , down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] , mid_block_type='UNetMidBlock2DSimpleCrossAttn' , up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] , in_channels=3 , out_channels=6 , cross_attention_dim=3_2 , encoder_hid_dim=3_2 , attention_head_dim=8 , addition_embed_type='text' , addition_embed_type_num_heads=2 , cross_attention_norm='group_norm' , resnet_time_scale_shift='scale_shift' , act_fn='gelu' , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=_lowerCamelCase , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCamelCase_ ( self ):
torch.manual_seed(0 )
lowercase = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
lowercase = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
sample_size=3_2 , layers_per_block=[1, 2] , block_out_channels=[3_2, 6_4] , down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] , mid_block_type='UNetMidBlock2DSimpleCrossAttn' , up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] , in_channels=6 , out_channels=6 , cross_attention_dim=3_2 , encoder_hid_dim=3_2 , attention_head_dim=8 , addition_embed_type='text' , addition_embed_type_num_heads=2 , cross_attention_norm='group_norm' , resnet_time_scale_shift='scale_shift' , act_fn='gelu' , class_embed_type='timestep' , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn='gelu' , time_embedding_dim=3_2 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=_lowerCamelCase , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
lowercase = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
lowercase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCamelCase_ ( self ):
lowercase = self.get_dummy_components()
lowercase = self.pipeline_class(**_lowerCamelCase )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowercase = self.get_dummy_inputs(_lowerCamelCase )
lowercase = inputs['prompt']
lowercase = inputs['generator']
lowercase = inputs['num_inference_steps']
lowercase = inputs['output_type']
if "image" in inputs:
lowercase = inputs['image']
else:
lowercase = None
if "mask_image" in inputs:
lowercase = inputs['mask_image']
else:
lowercase = None
if "original_image" in inputs:
lowercase = inputs['original_image']
else:
lowercase = None
lowercase , lowercase = pipe.encode_prompt(_lowerCamelCase )
# inputs with prompt converted to embeddings
lowercase = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
lowercase = image
if mask_image is not None:
lowercase = mask_image
if original_image is not None:
lowercase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
lowercase = pipe(**_lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowerCamelCase )
lowercase = self.pipeline_class.from_pretrained(_lowerCamelCase )
pipe_loaded.to(_lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowerCamelCase , _lowerCamelCase ) is None , F'`{optional_component}` did not stay set to None after loading.' , )
lowercase = self.get_dummy_inputs(_lowerCamelCase )
lowercase = inputs['generator']
lowercase = inputs['num_inference_steps']
lowercase = inputs['output_type']
# inputs with prompt converted to embeddings
lowercase = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
lowercase = image
if mask_image is not None:
lowercase = mask_image
if original_image is not None:
lowercase = original_image
lowercase = pipe_loaded(**_lowerCamelCase )[0]
lowercase = np.abs(to_np(_lowerCamelCase ) - to_np(_lowerCamelCase ) ).max()
self.assertLess(_lowerCamelCase , 1e-4 )
def UpperCamelCase_ ( self ):
lowercase = self.get_dummy_components()
lowercase = self.pipeline_class(**_lowerCamelCase )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowercase = self.get_dummy_inputs(_lowerCamelCase )
lowercase = pipe(**_lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowerCamelCase )
lowercase = self.pipeline_class.from_pretrained(_lowerCamelCase )
pipe_loaded.to(_lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowercase = self.get_dummy_inputs(_lowerCamelCase )
lowercase = pipe_loaded(**_lowerCamelCase )[0]
lowercase = np.abs(to_np(_lowerCamelCase ) - to_np(_lowerCamelCase ) ).max()
self.assertLess(_lowerCamelCase , 1e-4 )
| 134 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict =[
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREAMING_SNAKE_CASE__ : List[Any] =[
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
SCREAMING_SNAKE_CASE__ : Optional[int] =[
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
SCREAMING_SNAKE_CASE__ : List[Any] =[
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
SCREAMING_SNAKE_CASE__ : Optional[int] =[
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 434 | """simple docstring"""
from jiwer import compute_measures
import datasets
SCREAMING_SNAKE_CASE__ : Dict ='\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
SCREAMING_SNAKE_CASE__ : Dict ='\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'
SCREAMING_SNAKE_CASE__ : Dict ='\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> wer = datasets.load_metric("wer")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
"""simple docstring"""
def a__ ( self ) -> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/jitsi/jiwer/'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/Word_error_rate''',
] , )
def a__ ( self , _lowercase=None , _lowercase=None , _lowercase=False ) -> Dict:
if concatenate_texts:
return compute_measures(_lowercase , _lowercase )["wer"]
else:
_lowerCamelCase : Any = 0
_lowerCamelCase : Tuple = 0
for prediction, reference in zip(_lowercase , _lowercase ):
_lowerCamelCase : Any = compute_measures(_lowercase , _lowercase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 434 | 1 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowerCamelCase : Dict = '''src/transformers'''
__lowerCamelCase : Any = '''docs/source/en'''
__lowerCamelCase : Tuple = '''.'''
def lowercase__ ( __A: Optional[int] ,__A: int ,__A: Optional[Any] ):
'''simple docstring'''
with open(__A ,'''r''' ,encoding='''utf-8''' ,newline='''\n''' ) as f:
__magic_name__ : Tuple = f.readlines()
# Find the start prompt.
__magic_name__ : List[Any] = 0
while not lines[start_index].startswith(__A ):
start_index += 1
start_index += 1
__magic_name__ : Optional[int] = start_index
while not lines[end_index].startswith(__A ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
__lowerCamelCase : Any = '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__lowerCamelCase : Optional[int] = re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowerCamelCase : List[Any] = re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCamelCase : Union[str, Any] = re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase : Optional[Any] = direct_transformers_import(TRANSFORMERS_PATH)
def lowercase__ ( __A: str ):
'''simple docstring'''
__magic_name__ : Optional[Any] = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' ,__A )
return [m.group(0 ) for m in matches]
def lowercase__ ( __A: Optional[int] ,__A: Any ):
'''simple docstring'''
__magic_name__ : List[str] = 2 if text == '''✅''' or text == '''❌''' else len(__A )
__magic_name__ : Tuple = (width - text_length) // 2
__magic_name__ : Dict = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def lowercase__ ( ):
'''simple docstring'''
__magic_name__ : Dict = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
__magic_name__ : int = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
__magic_name__ : Tuple = {name: config.replace('''Config''' ,'''''' ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
__magic_name__ : int = collections.defaultdict(__A )
__magic_name__ : Union[str, Any] = collections.defaultdict(__A )
__magic_name__ : Union[str, Any] = collections.defaultdict(__A )
__magic_name__ : Optional[int] = collections.defaultdict(__A )
__magic_name__ : Dict = collections.defaultdict(__A )
# Let's lookup through all transformers object (once).
for attr_name in dir(__A ):
__magic_name__ : Optional[int] = None
if attr_name.endswith('''Tokenizer''' ):
__magic_name__ : List[str] = slow_tokenizers
__magic_name__ : Any = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
__magic_name__ : Optional[Any] = fast_tokenizers
__magic_name__ : Dict = attr_name[:-1_3]
elif _re_tf_models.match(__A ) is not None:
__magic_name__ : str = tf_models
__magic_name__ : Optional[Any] = _re_tf_models.match(__A ).groups()[0]
elif _re_flax_models.match(__A ) is not None:
__magic_name__ : Union[str, Any] = flax_models
__magic_name__ : Any = _re_flax_models.match(__A ).groups()[0]
elif _re_pt_models.match(__A ) is not None:
__magic_name__ : Union[str, Any] = pt_models
__magic_name__ : Any = _re_pt_models.match(__A ).groups()[0]
if lookup_dict is not None:
while len(__A ) > 0:
if attr_name in model_name_to_prefix.values():
__magic_name__ : Optional[Any] = True
break
# Try again after removing the last word in the name
__magic_name__ : List[Any] = ''''''.join(camel_case_split(__A )[:-1] )
# Let's build that table!
__magic_name__ : Dict = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
__magic_name__ : Any = ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
__magic_name__ : Union[str, Any] = [len(__A ) + 2 for c in columns]
__magic_name__ : str = max([len(__A ) for name in model_names] ) + 2
# Build the table per se
__magic_name__ : Tuple = '''|''' + '''|'''.join([_center_text(__A ,__A ) for c, w in zip(__A ,__A )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
__magic_name__ : Dict = {True: '''✅''', False: '''❌'''}
for name in model_names:
__magic_name__ : Dict = model_name_to_prefix[name]
__magic_name__ : int = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(__A ,__A ) for l, w in zip(__A ,__A )] ) + "|\n"
return table
def lowercase__ ( __A: Any=False ):
'''simple docstring'''
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : str = _find_text_in_file(
filename=os.path.join(__A ,'''index.md''' ) ,start_prompt='''<!--This table is updated automatically from the auto modules''' ,end_prompt='''<!-- End table-->''' ,)
__magic_name__ : Dict = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(__A ,'''index.md''' ) ,'''w''' ,encoding='''utf-8''' ,newline='''\n''' ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
'''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.''' )
if __name__ == "__main__":
__lowerCamelCase : Any = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
__lowerCamelCase : Optional[int] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 501 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowerCamelCase : Dict = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def lowercase__ ( __A: List[Any] ):
'''simple docstring'''
__magic_name__ : int = test_results.split(''' ''' )
__magic_name__ : Optional[Any] = 0
__magic_name__ : int = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
__magic_name__ : Optional[Any] = expressions[-2] if '''=''' in expressions[-1] else expressions[-1]
for i, expression in enumerate(__A ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def lowercase__ ( __A: Dict ):
'''simple docstring'''
__magic_name__ : Tuple = {}
__magic_name__ : List[Any] = None
__magic_name__ : int = False
for line in failures_short_lines.split('''\n''' ):
if re.search(r'''_ \[doctest\]''' ,__A ):
__magic_name__ : Dict = True
__magic_name__ : Any = line.split(''' ''' )[2]
elif in_error and not line.split(''' ''' )[0].isdigit():
__magic_name__ : List[Any] = line
__magic_name__ : List[Any] = False
return failures
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : Dict ) -> List[str]:
__magic_name__ : Union[str, Any] = title
__magic_name__ : List[Any] = doc_test_results['''time_spent'''].split(''',''' )[0]
__magic_name__ : Optional[Any] = doc_test_results['''success''']
__magic_name__ : Optional[Any] = doc_test_results['''failures''']
__magic_name__ : Union[str, Any] = self.n_success + self.n_failures
# Failures and success of the modeling tests
__magic_name__ : List[Any] = doc_test_results
@property
def UpperCAmelCase__ ( self : List[str] ) -> str:
__magic_name__ : Union[str, Any] = [self._time_spent]
__magic_name__ : str = 0
for time in time_spent:
__magic_name__ : List[Any] = time.split(''':''' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(lowerCamelCase_ ) == 1:
__magic_name__ : str = [0, 0, time_parts[0]]
__magic_name__ , __magic_name__ , __magic_name__ : List[Any] = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3600 + minutes * 60 + seconds
__magic_name__ , __magic_name__ , __magic_name__ : Dict = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
return F'''{int(lowerCamelCase_ )}h{int(lowerCamelCase_ )}m{int(lowerCamelCase_ )}s'''
@property
def UpperCAmelCase__ ( self : Dict ) -> Dict:
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def UpperCAmelCase__ ( self : Optional[Any] ) -> Dict:
return {
"type": "section",
"text": {
"type": "plain_text",
"text": F'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Dict:
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
F'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
F''' {self.time}.'''
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def UpperCAmelCase__ ( self : Dict ) -> Dict:
__magic_name__ : List[Any] = 40
__magic_name__ : Optional[Any] = {k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(lowerCamelCase_ , lowerCamelCase_ )}
__magic_name__ : Any = ''''''
for category, failures in category_failures.items():
if len(lowerCamelCase_ ) == 0:
continue
if report != "":
report += "\n\n"
report += F'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(lowerCamelCase_ )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": F'''The following examples had failures:\n\n\n{report}\n''',
},
}
@property
def UpperCAmelCase__ ( self : Dict ) -> str:
__magic_name__ : List[str] = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(lowerCamelCase_ )
@staticmethod
def UpperCAmelCase__ ( ) -> List[Any]:
__magic_name__ : Dict = [
{
'''type''': '''section''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''There was an issue running the tests.''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
'''url''': F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
]
print('''Sending the following payload''' )
print(json.dumps({'''blocks''': json.loads(lowerCamelCase_ )} ) )
client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , text='''There was an issue running the tests.''' , blocks=lowerCamelCase_ , )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Union[str, Any]:
print('''Sending the following payload''' )
print(json.dumps({'''blocks''': json.loads(self.payload )} ) )
__magic_name__ : Tuple = F'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else '''All tests passed.'''
__magic_name__ : List[Any] = client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , blocks=self.payload , text=lowerCamelCase_ , )
def UpperCAmelCase__ ( self : Tuple , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Any , lowerCamelCase_ : Any ) -> Optional[Any]:
__magic_name__ : Optional[Any] = ''''''
for key, value in failures.items():
__magic_name__ : int = value[:200] + ''' [Truncated]''' if len(lowerCamelCase_ ) > 250 else value
failures_text += F'''*{key}*\n_{value}_\n\n'''
__magic_name__ : Dict = job_name
__magic_name__ : Tuple = {'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}
if job_link is not None:
__magic_name__ : int = {
'''type''': '''button''',
'''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
'''url''': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def UpperCAmelCase__ ( self : Any ) -> Tuple:
if self.thread_ts is None:
raise ValueError('''Can only post reply if a post has been made.''' )
__magic_name__ : List[str] = self.doc_test_results.pop('''job_link''' )
self.doc_test_results.pop('''failures''' )
self.doc_test_results.pop('''success''' )
self.doc_test_results.pop('''time_spent''' )
__magic_name__ : int = sorted(self.doc_test_results.items() , key=lambda lowerCamelCase_ : t[0] )
for job, job_result in sorted_dict:
if len(job_result['''failures'''] ):
__magic_name__ : List[Any] = F'''*Num failures* :{len(job_result['failed'] )} \n'''
__magic_name__ : str = job_result['''failures''']
__magic_name__ : str = self.get_reply_blocks(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , text=lowerCamelCase_ )
print('''Sending the following reply''' )
print(json.dumps({'''blocks''': blocks} ) )
client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , text=F'''Results for {job}''' , blocks=lowerCamelCase_ , thread_ts=self.thread_ts['''ts'''] , )
time.sleep(1 )
def lowercase__ ( ):
'''simple docstring'''
__magic_name__ : List[Any] = os.environ['''GITHUB_RUN_ID''']
__magic_name__ : Optional[int] = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
__magic_name__ : Any = requests.get(__A ).json()
__magic_name__ : List[str] = {}
try:
jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
__magic_name__ : Optional[int] = math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0 )
for i in range(__A ):
__magic_name__ : Optional[Any] = requests.get(url + F'''&page={i + 2}''' ).json()
jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
return jobs
except Exception as e:
print('''Unknown error, could not fetch links.''' ,__A )
return {}
def lowercase__ ( __A: str ):
'''simple docstring'''
__magic_name__ : List[str] = {}
if os.path.exists(__A ):
__magic_name__ : Any = os.listdir(__A )
for file in files:
try:
with open(os.path.join(__A ,__A ) ,encoding='''utf-8''' ) as f:
__magic_name__ : Dict = f.read()
except UnicodeDecodeError as e:
raise ValueError(F'''Could not open {os.path.join(__A ,__A )}.''' ) from e
return _artifact
def lowercase__ ( ):
'''simple docstring'''
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCamelCase_ : str ) -> int:
__magic_name__ : int = name
__magic_name__ : Dict = []
def __str__( self : List[str] ) -> Union[str, Any]:
return self.name
def UpperCAmelCase__ ( self : List[Any] , lowerCamelCase_ : str ) -> Optional[Any]:
self.paths.append({'''name''': self.name, '''path''': path} )
__magic_name__ : Dict[str, Artifact] = {}
__magic_name__ : Tuple = filter(os.path.isdir ,os.listdir() )
for directory in directories:
__magic_name__ : int = directory
if artifact_name not in _available_artifacts:
__magic_name__ : List[Any] = Artifact(__A )
_available_artifacts[artifact_name].add_path(__A )
return _available_artifacts
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = get_job_links()
__lowerCamelCase : Tuple = retrieve_available_artifacts()
__lowerCamelCase : Tuple = collections.OrderedDict(
[
('''*.py''', '''API Examples'''),
('''*.md''', '''MD Examples'''),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
__lowerCamelCase : Tuple = {
v: {
'''failed''': [],
'''failures''': {},
}
for v in docs.values()
}
# Link to the GitHub Action job
__lowerCamelCase : Optional[int] = github_actions_job_links.get('''run_doctests''')
__lowerCamelCase : Optional[Any] = available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
__lowerCamelCase : Optional[int] = retrieve_artifact(artifact_path['''name'''])
if "stats" in artifact:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Dict = handle_test_results(artifact['''stats'''])
__lowerCamelCase : Optional[Any] = failed
__lowerCamelCase : List[Any] = success
__lowerCamelCase : int = time_spent[1:-1] + ''', '''
__lowerCamelCase : Dict = extract_first_line_failure(artifact['''failures_short'''])
for line in artifact["summary_short"].split('''\n'''):
if re.search('''FAILED''', line):
__lowerCamelCase : List[Any] = line.replace('''FAILED ''', '''''')
__lowerCamelCase : int = line.split()[0].replace('''\n''', '''''')
if "::" in line:
__lowerCamelCase , __lowerCamelCase : List[str] = line.split('''::''')
else:
__lowerCamelCase , __lowerCamelCase : Optional[int] = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
__lowerCamelCase : Union[str, Any] = docs[file_regex]
doc_test_results[category]["failed"].append(test)
__lowerCamelCase : List[Any] = all_failures[test] if test in all_failures else '''N/A'''
__lowerCamelCase : int = failure
break
__lowerCamelCase : List[Any] = Message('''🤗 Results of the doc tests.''', doc_test_results)
message.post()
message.post_reply()
| 501 | 1 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
lowerCamelCase_ = logging.get_logger(__name__)
@add_end_docstrings(snake_case_ )
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self : Tuple , *__UpperCAmelCase : Any , **__UpperCAmelCase : str ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
requires_backends(self , "decord" )
self.check_model_type(__UpperCAmelCase )
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : Tuple=None , __UpperCAmelCase : List[str]=None ):
'''simple docstring'''
_A = {}
if frame_sampling_rate is not None:
_A = frame_sampling_rate
if num_frames is not None:
_A = num_frames
_A = {}
if top_k is not None:
_A = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : Optional[int] , __UpperCAmelCase : Union[str, List[str]] , **__UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
return super().__call__(__UpperCAmelCase , **__UpperCAmelCase )
def lowerCAmelCase ( self : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : str=None , __UpperCAmelCase : Dict=1 ):
'''simple docstring'''
if num_frames is None:
_A = self.model.config.num_frames
if video.startswith("http://" ) or video.startswith("https://" ):
_A = BytesIO(requests.get(__UpperCAmelCase ).content )
_A = VideoReader(__UpperCAmelCase )
videoreader.seek(0 )
_A = 0
_A = num_frames * frame_sampling_rate - 1
_A = np.linspace(__UpperCAmelCase , __UpperCAmelCase , num=__UpperCAmelCase , dtype=np.intaa )
_A = videoreader.get_batch(__UpperCAmelCase ).asnumpy()
_A = list(__UpperCAmelCase )
_A = self.image_processor(__UpperCAmelCase , return_tensors=self.framework )
return model_inputs
def lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Optional[Any] ):
'''simple docstring'''
_A = self.model(**__UpperCAmelCase )
return model_outputs
def lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[int]=5 ):
'''simple docstring'''
if top_k > self.model.config.num_labels:
_A = self.model.config.num_labels
if self.framework == "pt":
_A = model_outputs.logits.softmax(-1 )[0]
_A , _A = probs.topk(__UpperCAmelCase )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
_A = scores.tolist()
_A = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase , __UpperCAmelCase )]
| 330 |
'''simple docstring'''
import random
from typing import Any
def __lowercase ( __lowercase ) -> list[Any]:
'''simple docstring'''
for _ in range(len(__lowercase ) ):
_A = random.randint(0 , len(__lowercase ) - 1 )
_A = random.randint(0 , len(__lowercase ) - 1 )
_A , _A = data[b], data[a]
return data
if __name__ == "__main__":
lowerCamelCase_ = [0, 1, 2, 3, 4, 5, 6, 7]
lowerCamelCase_ = ['''python''', '''says''', '''hello''', '''!''']
print('''Fisher-Yates Shuffle:''')
print('''List''', integers, strings)
print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 330 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig'''],
'''tokenization_lxmert''': ['''LxmertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''LxmertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''LxmertEncoder''',
'''LxmertForPreTraining''',
'''LxmertForQuestionAnswering''',
'''LxmertModel''',
'''LxmertPreTrainedModel''',
'''LxmertVisualFeatureEncoder''',
'''LxmertXLayer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLxmertForPreTraining''',
'''TFLxmertMainLayer''',
'''TFLxmertModel''',
'''TFLxmertPreTrainedModel''',
'''TFLxmertVisualFeatureEncoder''',
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 716 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = DanceDiffusionPipeline
__lowerCamelCase = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
__lowerCamelCase = PipelineTesterMixin.required_optional_params - {
'callback',
'latents',
'callback_steps',
'output_type',
'num_images_per_prompt',
}
__lowerCamelCase = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
A__ = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=lowercase , use_timestep_embedding=lowercase , time_embedding_type="fourier" , mid_block_type="UNetMidBlock1D" , down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , )
A__ = IPNDMScheduler()
A__ = {
"unet": unet,
"scheduler": scheduler,
}
return components
def UpperCamelCase ( self , lowercase , lowercase=0 ) -> Union[str, Any]:
'''simple docstring'''
if str(lowercase ).startswith("mps" ):
A__ = torch.manual_seed(lowercase )
else:
A__ = torch.Generator(device=lowercase ).manual_seed(lowercase )
A__ = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 4,
}
return inputs
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = "cpu" # ensure determinism for the device-dependent torch.Generator
A__ = self.get_dummy_components()
A__ = DanceDiffusionPipeline(**lowercase )
A__ = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
A__ = self.get_dummy_inputs(lowercase )
A__ = pipe(**lowercase )
A__ = output.audios
A__ = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
A__ = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return super().test_save_load_optional_components()
@skip_mps
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return super().test_attention_slicing_forward_pass()
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = torch_device
A__ = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" )
A__ = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
A__ = torch.manual_seed(0 )
A__ = pipe(generator=lowercase , num_inference_steps=100 , audio_length_in_s=4.096 )
A__ = output.audios
A__ = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
A__ = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = torch_device
A__ = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" , torch_dtype=torch.floataa )
A__ = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
A__ = torch.manual_seed(0 )
A__ = pipe(generator=lowercase , num_inference_steps=100 , audio_length_in_s=4.096 )
A__ = output.audios
A__ = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
A__ = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
| 626 | 0 |
def _snake_case (_snake_case : int) -> int:
assert isinstance(_snake_case , _snake_case), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
_lowercase =f'''The input value of [n={number}] has to be > 0'''
raise ValueError(_snake_case)
else:
_lowercase =sylvester(number - 1)
_lowercase =num - 1
_lowercase =num
return lower * upper + 1
if __name__ == "__main__":
print(f'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')
| 181 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE_ ( _a ):
"""simple docstring"""
__lowerCAmelCase : Union[List[PIL.Image.Image], np.ndarray]
__lowerCAmelCase : Optional[List[bool]]
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 181 | 1 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Union[str, Any], _snake_case : Any, _snake_case : List[str]=1_3, _snake_case : Dict=7, _snake_case : Any=True, _snake_case : Union[str, Any]=True, _snake_case : Union[str, Any]=True, _snake_case : Union[str, Any]=True, _snake_case : Optional[Any]=9_9, _snake_case : Optional[int]=3_2, _snake_case : int=5, _snake_case : List[str]=4, _snake_case : List[str]=3_7, _snake_case : Optional[Any]="gelu", _snake_case : Dict=0.1, _snake_case : List[Any]=0.1, _snake_case : Optional[Any]=5_1_2, _snake_case : Optional[Any]=1_6, _snake_case : List[str]=2, _snake_case : str=0.0_2, _snake_case : int=4, ) ->Dict:
snake_case__ : Optional[int] = parent
snake_case__ : str = batch_size
snake_case__ : Dict = seq_length
snake_case__ : List[str] = is_training
snake_case__ : List[str] = use_attention_mask
snake_case__ : Union[str, Any] = use_token_type_ids
snake_case__ : List[Any] = use_labels
snake_case__ : List[Any] = vocab_size
snake_case__ : List[str] = hidden_size
snake_case__ : Tuple = num_hidden_layers
snake_case__ : List[str] = num_attention_heads
snake_case__ : Optional[Any] = intermediate_size
snake_case__ : Any = hidden_act
snake_case__ : Union[str, Any] = hidden_dropout_prob
snake_case__ : List[str] = attention_probs_dropout_prob
snake_case__ : List[str] = max_position_embeddings
snake_case__ : List[Any] = type_vocab_size
snake_case__ : int = type_sequence_label_size
snake_case__ : str = initializer_range
snake_case__ : Union[str, Any] = num_choices
def lowercase_ ( self : List[str] ) ->Tuple:
snake_case__ : int = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
snake_case__ : Tuple = None
if self.use_attention_mask:
snake_case__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ : Tuple = None
if self.use_token_type_ids:
snake_case__ : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
snake_case__ : int = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=_lowerCAmelCase, initializer_range=self.initializer_range, )
return config, input_ids, token_type_ids, attention_mask
def lowercase_ ( self : int ) ->Tuple:
snake_case__ : List[Any] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Optional[Any] = config_and_inputs
snake_case__ : int = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def lowercase_ ( self : Any ) ->int:
snake_case__ : Dict = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[str] = config_and_inputs
snake_case__ : str = True
snake_case__ : Dict = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case__ : List[Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class snake_case__ ( _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase_ ( self : Any ) ->Dict:
snake_case__ : Any = FlaxRobertaPreLayerNormModelTester(self )
@slow
def lowercase_ ( self : Union[str, Any] ) ->str:
for model_class_name in self.all_model_classes:
snake_case__ : Any = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=_lowerCAmelCase )
snake_case__ : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowerCAmelCase )
@require_flax
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self : str ) ->str:
snake_case__ : List[str] = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=_lowerCAmelCase )
snake_case__ : List[Any] = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]], dtype=jnp.intaa )
snake_case__ : Optional[Any] = model(_lowerCAmelCase )[0]
snake_case__ : Any = [1, 1_1, 5_0_2_6_5]
self.assertEqual(list(output.shape ), _lowerCAmelCase )
# compare the actual values for a slice.
snake_case__ : Optional[int] = np.array(
[[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]], dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3], _lowerCAmelCase, atol=1e-4 ) )
@slow
def lowercase_ ( self : List[Any] ) ->Dict:
snake_case__ : Union[str, Any] = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=_lowerCAmelCase )
snake_case__ : Union[str, Any] = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]], dtype=jnp.intaa )
snake_case__ : Dict = model(_lowerCAmelCase )[0]
# compare the actual values for a slice.
snake_case__ : Any = np.array(
[[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]], dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3], _lowerCAmelCase, atol=1e-4 ) )
| 709 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
a_ :Dict = None
a_ :List[str] = logging.get_logger(__name__)
a_ :Dict = "▁"
a_ :Dict = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a_ :Union[str, Any] = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
"tokenizer_file": {
"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
},
}
a_ :List[Any] = {
"google/pegasus-xsum": 512,
}
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = PegasusTokenizer
_SCREAMING_SNAKE_CASE = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, _snake_case : Any=None, _snake_case : Optional[Any]=None, _snake_case : Tuple="<pad>", _snake_case : Tuple="</s>", _snake_case : List[str]="<unk>", _snake_case : Any="<mask_2>", _snake_case : Optional[Any]="<mask_1>", _snake_case : Tuple=None, _snake_case : str=1_0_3, **_snake_case : Dict, ) ->List[Any]:
snake_case__ : Any = offset
if additional_special_tokens is not None:
if not isinstance(_snake_case, _snake_case ):
raise TypeError(
F'''additional_special_tokens should be of type {type(_snake_case )}, but is'''
F''' {type(_snake_case )}''' )
snake_case__ : int = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F'''<unk_{i}>''' for i in range(len(_snake_case ), self.offset - 1 )
]
if len(set(_snake_case ) ) != len(_snake_case ):
raise ValueError(
'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
snake_case__ : Optional[int] = additional_special_tokens_extended
else:
snake_case__ : Optional[Any] = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F'''<unk_{i}>''' for i in range(2, self.offset )]
super().__init__(
_snake_case, tokenizer_file=_snake_case, pad_token=_snake_case, eos_token=_snake_case, unk_token=_snake_case, mask_token=_snake_case, mask_token_sent=_snake_case, offset=_snake_case, additional_special_tokens=_snake_case, **_snake_case, )
snake_case__ : str = vocab_file
snake_case__ : int = False if not self.vocab_file else True
def lowercase_ ( self : Optional[Any], _snake_case : str ) ->Dict:
snake_case__ : int = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def lowercase_ ( self : Dict, _snake_case : List, _snake_case : Optional[List] = None, _snake_case : bool = False ) ->List[int]:
if already_has_special_tokens:
return self._special_token_mask(_snake_case )
elif token_ids_a is None:
return self._special_token_mask(_snake_case ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowercase_ ( self : Any, _snake_case : Union[str, Any], _snake_case : Union[str, Any]=None ) ->List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowercase_ ( self : Optional[Any], _snake_case : str, _snake_case : Optional[str] = None ) ->Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_snake_case ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case__ : List[Any] = os.path.join(
_snake_case, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_snake_case ):
copyfile(self.vocab_file, _snake_case )
return (out_vocab_file,)
| 243 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( unittest.TestCase ):
@slow
def _a ( self ) -> Any:
_UpperCAmelCase = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base" )
_UpperCAmelCase = {
"input_ids": tf.convert_to_tensor([[0, 2646, 10269, 83, 99942, 2]] , dtype=tf.intaa ), # "My dog is cute"
"attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ),
}
_UpperCAmelCase = model(a_ )["last_hidden_state"]
_UpperCAmelCase = tf.TensorShape((1, 6, 768) )
self.assertEqual(output.shape , a_ )
# compare the actual values for a slice.
_UpperCAmelCase = tf.convert_to_tensor(
[
[
[0.0681762, 0.10894451, 0.06772504],
[-0.06423668, 0.02366615, 0.04329344],
[-0.06057295, 0.09974135, -0.00070584],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 657 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCAmelCase :
def __init__( self , a_ , a_=2 , a_=3 , a_=4 , a_=2 , a_=7 , a_=True , a_=True , a_=True , a_=True , a_=99 , a_=36 , a_=3 , a_=4 , a_=37 , a_="gelu" , a_=0.1 , a_=0.1 , a_=512 , a_=16 , a_=2 , a_=0.02 , a_=6 , a_=6 , a_=3 , a_=4 , a_=None , a_=1000 , ) -> Optional[Any]:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = text_seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = coordinate_size
_UpperCAmelCase = shape_size
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_choices
_UpperCAmelCase = scope
_UpperCAmelCase = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
_UpperCAmelCase = text_seq_length
_UpperCAmelCase = (image_size // patch_size) ** 2 + 1
_UpperCAmelCase = self.text_seq_length + self.image_seq_length
def _a ( self ) -> Dict:
_UpperCAmelCase = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase = bbox[i, j, 3]
_UpperCAmelCase = bbox[i, j, 1]
_UpperCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase = bbox[i, j, 2]
_UpperCAmelCase = bbox[i, j, 0]
_UpperCAmelCase = t
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.text_seq_length] )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
_UpperCAmelCase = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _a ( self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ ) -> Tuple:
_UpperCAmelCase = LayoutLMvaModel(config=a_ )
model.to(a_ )
model.eval()
# text + image
_UpperCAmelCase = model(a_ , pixel_values=a_ )
_UpperCAmelCase = model(
a_ , bbox=a_ , pixel_values=a_ , attention_mask=a_ , token_type_ids=a_ )
_UpperCAmelCase = model(a_ , bbox=a_ , pixel_values=a_ , token_type_ids=a_ )
_UpperCAmelCase = model(a_ , bbox=a_ , pixel_values=a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
_UpperCAmelCase = model(a_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
_UpperCAmelCase = model(pixel_values=a_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _a ( self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = LayoutLMvaForSequenceClassification(a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase = model(
a_ , bbox=a_ , pixel_values=a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ ) -> Union[str, Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = LayoutLMvaForTokenClassification(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase = model(
a_ , bbox=a_ , pixel_values=a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _a ( self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ ) -> Dict:
_UpperCAmelCase = LayoutLMvaForQuestionAnswering(config=a_ )
model.to(a_ )
model.eval()
_UpperCAmelCase = model(
a_ , bbox=a_ , pixel_values=a_ , attention_mask=a_ , token_type_ids=a_ , start_positions=a_ , end_positions=a_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _a ( self ) -> Optional[int]:
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase_ : Any = False
lowercase_ : Dict = False
lowercase_ : List[str] = False
lowercase_ : str = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowercase_ : int = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _a ( self , a_ , a_ , a_ , a_ , a_ ) -> List[str]:
# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
# the sequence dimension of the text embedding only.
# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
return True
def _a ( self ) -> Union[str, Any]:
_UpperCAmelCase = LayoutLMvaModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=a_ , hidden_size=37 )
def _a ( self , a_ , a_ , a_=False ) -> List[str]:
_UpperCAmelCase = copy.deepcopy(a_ )
if model_class in get_values(a_ ):
_UpperCAmelCase = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(a_ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(a_ ):
_UpperCAmelCase = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=a_ )
elif model_class in get_values(a_ ):
_UpperCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a_ )
_UpperCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a_ )
elif model_class in [
*get_values(a_ ),
]:
_UpperCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=a_ )
elif model_class in [
*get_values(a_ ),
]:
_UpperCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=a_ , )
return inputs_dict
def _a ( self ) -> int:
self.config_tester.run_common_tests()
def _a ( self ) -> List[str]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def _a ( self ) -> List[str]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*a_ )
def _a ( self ) -> int:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*a_ )
def _a ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*a_ )
def _a ( self ) -> List[Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*a_ )
@slow
def _a ( self ) -> List[str]:
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = LayoutLMvaModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def __lowerCamelCase ( ):
"""simple docstring"""
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
@cached_property
def _a ( self ) -> List[Any]:
return LayoutLMvaImageProcessor(apply_ocr=a_ ) if is_vision_available() else None
@slow
def _a ( self ) -> Union[str, Any]:
_UpperCAmelCase = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(a_ )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=a_ , return_tensors="pt" ).pixel_values.to(a_ )
_UpperCAmelCase = torch.tensor([[1, 2]] )
_UpperCAmelCase = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
_UpperCAmelCase = model(
input_ids=input_ids.to(a_ ) , bbox=bbox.to(a_ ) , pixel_values=pixel_values.to(a_ ) , )
# verify the logits
_UpperCAmelCase = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , a_ )
_UpperCAmelCase = torch.tensor(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] ).to(a_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , a_ , atol=1e-4 ) )
| 657 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _lowerCamelCase ( a_ : Union[str, Any]):
lowerCamelCase :Dict = 3_84
if "tiny" in model_name:
lowerCamelCase :Optional[int] = [3, 3, 9, 3]
lowerCamelCase :int = [96, 1_92, 3_84, 7_68]
if "small" in model_name:
lowerCamelCase :Any = [3, 3, 27, 3]
lowerCamelCase :Any = [96, 1_92, 3_84, 7_68]
if "base" in model_name:
lowerCamelCase :List[str] = [3, 3, 27, 3]
lowerCamelCase :Optional[Any] = [1_28, 2_56, 5_12, 10_24]
lowerCamelCase :Any = 5_12
if "large" in model_name:
lowerCamelCase :int = [3, 3, 27, 3]
lowerCamelCase :Any = [1_92, 3_84, 7_68, 15_36]
lowerCamelCase :int = 7_68
if "xlarge" in model_name:
lowerCamelCase :int = [3, 3, 27, 3]
lowerCamelCase :Union[str, Any] = [2_56, 5_12, 10_24, 20_48]
lowerCamelCase :Any = 10_24
# set label information
lowerCamelCase :Optional[Any] = 1_50
lowerCamelCase :str = '''huggingface/label-files'''
lowerCamelCase :List[str] = '''ade20k-id2label.json'''
lowerCamelCase :Any = json.load(open(hf_hub_download(a_ , a_ , repo_type='''dataset''') , '''r'''))
lowerCamelCase :Optional[Any] = {int(a_): v for k, v in idalabel.items()}
lowerCamelCase :Union[str, Any] = {v: k for k, v in idalabel.items()}
lowerCamelCase :List[Any] = ConvNextConfig(
depths=a_ , hidden_sizes=a_ , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''])
lowerCamelCase :List[Any] = UperNetConfig(
backbone_config=a_ , auxiliary_in_channels=a_ , num_labels=a_ , idalabel=a_ , labelaid=a_ , )
return config
def _lowerCamelCase ( a_ : List[Any]):
lowerCamelCase :str = []
# fmt: off
# stem
rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight'''))
rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias'''))
rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight'''))
rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.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.stages.{i}.{j}.gamma", F"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter"))
rename_keys.append((F"backbone.stages.{i}.{j}.depthwise_conv.weight", F"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight"))
rename_keys.append((F"backbone.stages.{i}.{j}.depthwise_conv.bias", F"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias"))
rename_keys.append((F"backbone.stages.{i}.{j}.norm.weight", F"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight"))
rename_keys.append((F"backbone.stages.{i}.{j}.norm.bias", F"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias"))
rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv1.weight", F"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight"))
rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv1.bias", F"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias"))
rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv2.weight", F"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight"))
rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv2.bias", F"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias"))
if i > 0:
rename_keys.append((F"backbone.downsample_layers.{i}.0.weight", F"backbone.encoder.stages.{i}.downsampling_layer.0.weight"))
rename_keys.append((F"backbone.downsample_layers.{i}.0.bias", F"backbone.encoder.stages.{i}.downsampling_layer.0.bias"))
rename_keys.append((F"backbone.downsample_layers.{i}.1.weight", F"backbone.encoder.stages.{i}.downsampling_layer.1.weight"))
rename_keys.append((F"backbone.downsample_layers.{i}.1.bias", F"backbone.encoder.stages.{i}.downsampling_layer.1.bias"))
rename_keys.append((F"backbone.norm{i}.weight", F"backbone.hidden_states_norms.stage{i+1}.weight"))
rename_keys.append((F"backbone.norm{i}.bias", F"backbone.hidden_states_norms.stage{i+1}.bias"))
# decode head
rename_keys.extend(
[
('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
])
# fmt: on
return rename_keys
def _lowerCamelCase ( a_ : int , a_ : List[str] , a_ : int):
lowerCamelCase :List[Any] = dct.pop(a_)
lowerCamelCase :List[str] = val
def _lowerCamelCase ( a_ : Union[str, Any] , a_ : Optional[Any] , a_ : int):
lowerCamelCase :Optional[int] = {
'''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
'''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
'''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
'''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
'''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
}
lowerCamelCase :List[Any] = model_name_to_url[model_name]
lowerCamelCase :Any = torch.hub.load_state_dict_from_url(a_ , map_location='''cpu''')['''state_dict''']
lowerCamelCase :Tuple = get_upernet_config(a_)
lowerCamelCase :Optional[int] = UperNetForSemanticSegmentation(a_)
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
lowerCamelCase :List[Any] = state_dict.pop(a_)
if "bn" in key:
lowerCamelCase :List[Any] = key.replace('''bn''' , '''batch_norm''')
lowerCamelCase :Optional[int] = val
# rename keys
lowerCamelCase :str = create_rename_keys(a_)
for src, dest in rename_keys:
rename_key(a_ , a_ , a_)
model.load_state_dict(a_)
# verify on image
lowerCamelCase :Tuple = '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
lowerCamelCase :Tuple = Image.open(requests.get(a_ , stream=a_).raw).convert('''RGB''')
lowerCamelCase :List[str] = SegformerImageProcessor()
lowerCamelCase :Any = processor(a_ , return_tensors='''pt''').pixel_values
with torch.no_grad():
lowerCamelCase :Any = model(a_)
if model_name == "upernet-convnext-tiny":
lowerCamelCase :Optional[Any] = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]])
elif model_name == "upernet-convnext-small":
lowerCamelCase :Any = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]])
elif model_name == "upernet-convnext-base":
lowerCamelCase :Any = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]])
elif model_name == "upernet-convnext-large":
lowerCamelCase :List[Any] = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]])
elif model_name == "upernet-convnext-xlarge":
lowerCamelCase :Dict = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]])
print('''Logits:''' , outputs.logits[0, 0, :3, :3])
assert torch.allclose(outputs.logits[0, 0, :3, :3] , a_ , atol=1e-4)
print('''Looks ok!''')
if pytorch_dump_folder_path is not None:
print(F"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(a_)
print(F"Saving processor to {pytorch_dump_folder_path}")
processor.save_pretrained(a_)
if push_to_hub:
print(F"Pushing model and processor for {model_name} to hub")
model.push_to_hub(F"openmmlab/{model_name}")
processor.push_to_hub(F"openmmlab/{model_name}")
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""upernet-convnext-tiny""",
type=str,
choices=[F'upernet-convnext-{size}' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
help="""Name of the ConvNext UperNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
A__ = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 701 | import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
A__ = [
"""cross_validation.py""",
"""gradient_accumulation.py""",
"""local_sgd.py""",
"""multi_process_metrics.py""",
"""memory.py""",
"""automatic_gradient_accumulation.py""",
"""fsdp_with_peak_mem_tracking.py""",
"""deepspeed_with_config_support.py""",
"""megatron_lm_gpt_pretraining.py""",
]
class _lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self : Any , __snake_case : str , __snake_case : bool , __snake_case : str = None , __snake_case : list = None ):
lowerCamelCase :Tuple = None
lowerCamelCase :Tuple = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
lowerCamelCase :Optional[int] = os.path.abspath('''examples''' )
for item in os.listdir(__snake_case ):
if item not in EXCLUDE_EXAMPLES:
lowerCamelCase :Optional[int] = os.path.join(__snake_case , __snake_case )
if os.path.isfile(__snake_case ) and ".py" in item_path:
with self.subTest(
tested_script=__snake_case , feature_script=__snake_case , tested_section='''main()''' if parser_only else '''training_function()''' , ):
lowerCamelCase :Union[str, Any] = compare_against_test(
os.path.join(__snake_case , __snake_case ) , __snake_case , __snake_case , __snake_case )
lowerCamelCase :int = '''\n'''.join(__snake_case )
if special_strings is not None:
for string in special_strings:
lowerCamelCase :int = diff.replace(__snake_case , '''''' )
self.assertEqual(__snake_case , '''''' )
def snake_case ( self : Dict ):
self.one_complete_example('''complete_nlp_example.py''' , __snake_case )
self.one_complete_example('''complete_nlp_example.py''' , __snake_case )
def snake_case ( self : Optional[Any] ):
lowerCamelCase :Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
lowerCamelCase :Optional[int] = [
''' ''' * 16 + '''{\n\n''',
''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 20 + '''"epoch": epoch,\n\n''',
''' ''' * 16 + '''},\n\n''',
''' ''' * 16 + '''step=epoch,\n''',
''' ''' * 12,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , __snake_case , __snake_case , __snake_case )
self.one_complete_example('''complete_cv_example.py''' , __snake_case , __snake_case , __snake_case )
@mock.patch.dict(os.environ , {'TESTING_MOCKED_DATALOADERS': '1'} )
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = False
@classmethod
def snake_case ( cls : Optional[Any] ):
super().setUpClass()
lowerCamelCase :Any = tempfile.mkdtemp()
lowerCamelCase :Optional[int] = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
lowerCamelCase :List[str] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def snake_case ( cls : Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def snake_case ( self : int ):
lowerCamelCase :Any = F"\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n ".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def snake_case ( self : List[Any] ):
lowerCamelCase :Tuple = F"\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n ".split()
lowerCamelCase :List[Any] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def snake_case ( self : List[str] ):
lowerCamelCase :Dict = F"\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}\n ".split()
lowerCamelCase :Dict = run_command(self._launch_args + testargs , return_stdout=__snake_case )
self.assertNotIn('''epoch 0:''' , __snake_case )
self.assertIn('''epoch 1:''' , __snake_case )
def snake_case ( self : str ):
lowerCamelCase :List[Any] = F"\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}\n ".split()
lowerCamelCase :Optional[int] = run_command(self._launch_args + testargs , return_stdout=__snake_case )
if torch.cuda.is_available():
lowerCamelCase :Union[str, Any] = torch.cuda.device_count()
else:
lowerCamelCase :Dict = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , __snake_case )
self.assertIn('''epoch 1:''' , __snake_case )
else:
self.assertIn('''epoch 0:''' , __snake_case )
self.assertIn('''epoch 1:''' , __snake_case )
@slow
def snake_case ( self : Any ):
lowerCamelCase :Tuple = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
lowerCamelCase :Dict = run_command(self._launch_args + testargs , return_stdout=__snake_case )
lowerCamelCase :Tuple = re.findall('''({.+})''' , __snake_case )
lowerCamelCase :Optional[Any] = [r for r in results if '''accuracy''' in r][-1]
lowerCamelCase :List[str] = ast.literal_eval(__snake_case )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def snake_case ( self : int ):
lowerCamelCase :Dict = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def snake_case ( self : Any ):
with tempfile.TemporaryDirectory() as tmpdir:
lowerCamelCase :Tuple = F"\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n ".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(__snake_case , '''tracking''' ) ) )
def snake_case ( self : Tuple ):
lowerCamelCase :Tuple = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def snake_case ( self : Optional[Any] ):
lowerCamelCase :int = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 49 | 0 |
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : Tuple , __snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase = arr.split(',' )
def lowerCamelCase__ ( self : int ) -> Dict:
'''simple docstring'''
lowerCamelCase = [int(self.array[0] )] * len(self.array )
lowerCamelCase = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
lowerCamelCase = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
lowerCamelCase = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
_lowerCAmelCase : Dict = input('please input some numbers:')
_lowerCAmelCase : Any = SubArray(whole_array)
_lowerCAmelCase : Optional[Any] = array.solve_sub_array()
print(('the results is:', re))
| 246 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : Dict = logging.get_logger(__name__)
def a_ ( UpperCamelCase_ : List[Any] ) -> str:
"""simple docstring"""
lowerCamelCase = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
lowerCamelCase = 1_2_8
elif "12-12" in model_name:
lowerCamelCase = 1_2
lowerCamelCase = 1_2
elif "14-14" in model_name:
lowerCamelCase = 1_4
lowerCamelCase = 1_4
elif "16-16" in model_name:
lowerCamelCase = 1_6
lowerCamelCase = 1_6
else:
raise ValueError('Model not supported' )
lowerCamelCase = 'huggingface/label-files'
if "speech-commands" in model_name:
lowerCamelCase = 3_5
lowerCamelCase = 'speech-commands-v2-id2label.json'
else:
lowerCamelCase = 5_2_7
lowerCamelCase = 'audioset-id2label.json'
lowerCamelCase = json.load(open(hf_hub_download(UpperCamelCase_ , UpperCamelCase_ , repo_type='dataset' ) , 'r' ) )
lowerCamelCase = {int(UpperCamelCase_ ): v for k, v in idalabel.items()}
lowerCamelCase = idalabel
lowerCamelCase = {v: k for k, v in idalabel.items()}
return config
def a_ ( UpperCamelCase_ : Optional[Any] ) -> Tuple:
"""simple docstring"""
if "module.v" in name:
lowerCamelCase = name.replace('module.v' , 'audio_spectrogram_transformer' )
if "cls_token" in name:
lowerCamelCase = name.replace('cls_token' , 'embeddings.cls_token' )
if "dist_token" in name:
lowerCamelCase = name.replace('dist_token' , 'embeddings.distillation_token' )
if "pos_embed" in name:
lowerCamelCase = name.replace('pos_embed' , 'embeddings.position_embeddings' )
if "patch_embed.proj" in name:
lowerCamelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
# transformer blocks
if "blocks" in name:
lowerCamelCase = name.replace('blocks' , 'encoder.layer' )
if "attn.proj" in name:
lowerCamelCase = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
lowerCamelCase = name.replace('attn' , 'attention.self' )
if "norm1" in name:
lowerCamelCase = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowerCamelCase = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowerCamelCase = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCamelCase = name.replace('mlp.fc2' , 'output.dense' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
lowerCamelCase = name.replace('audio_spectrogram_transformer.norm' , 'audio_spectrogram_transformer.layernorm' )
# classifier head
if "module.mlp_head.0" in name:
lowerCamelCase = name.replace('module.mlp_head.0' , 'classifier.layernorm' )
if "module.mlp_head.1" in name:
lowerCamelCase = name.replace('module.mlp_head.1' , 'classifier.dense' )
return name
def a_ ( UpperCamelCase_ : List[str] , UpperCamelCase_ : Dict ) -> Tuple:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCamelCase = orig_state_dict.pop(UpperCamelCase_ )
if "qkv" in key:
lowerCamelCase = key.split('.' )
lowerCamelCase = int(key_split[3] )
lowerCamelCase = config.hidden_size
if "weight" in key:
lowerCamelCase = val[:dim, :]
lowerCamelCase = val[dim : dim * 2, :]
lowerCamelCase = val[-dim:, :]
else:
lowerCamelCase = val[:dim]
lowerCamelCase = val[dim : dim * 2]
lowerCamelCase = val[-dim:]
else:
lowerCamelCase = val
return orig_state_dict
def a_ ( UpperCamelCase_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase = [
'module.v.head.weight',
'module.v.head.bias',
'module.v.head_dist.weight',
'module.v.head_dist.bias',
]
for k in ignore_keys:
state_dict.pop(UpperCamelCase_ , UpperCamelCase_ )
@torch.no_grad()
def a_ ( UpperCamelCase_ : Dict , UpperCamelCase_ : Any , UpperCamelCase_ : int=False ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase = get_audio_spectrogram_transformer_config(UpperCamelCase_ )
lowerCamelCase = {
'ast-finetuned-audioset-10-10-0.4593': (
'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.450': (
'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448': (
'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448-v2': (
'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
),
'ast-finetuned-audioset-12-12-0.447': (
'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
),
'ast-finetuned-audioset-14-14-0.443': (
'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
),
'ast-finetuned-audioset-16-16-0.442': (
'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
),
'ast-finetuned-speech-commands-v2': (
'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
),
}
# load original state_dict
lowerCamelCase = model_name_to_url[model_name]
lowerCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase_ , map_location='cpu' )
# remove some keys
remove_keys(UpperCamelCase_ )
# rename some keys
lowerCamelCase = convert_state_dict(UpperCamelCase_ , UpperCamelCase_ )
# load 🤗 model
lowerCamelCase = ASTForAudioClassification(UpperCamelCase_ )
model.eval()
model.load_state_dict(UpperCamelCase_ )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
lowerCamelCase = -4.2_677_393 if 'speech-commands' not in model_name else -6.845_978
lowerCamelCase = 4.5_689_974 if 'speech-commands' not in model_name else 5.5_654_526
lowerCamelCase = 1_0_2_4 if 'speech-commands' not in model_name else 1_2_8
lowerCamelCase = ASTFeatureExtractor(mean=UpperCamelCase_ , std=UpperCamelCase_ , max_length=UpperCamelCase_ )
if "speech-commands" in model_name:
lowerCamelCase = load_dataset('speech_commands' , 'v0.02' , split='validation' )
lowerCamelCase = dataset[0]['audio']['array']
else:
lowerCamelCase = hf_hub_download(
repo_id='nielsr/audio-spectogram-transformer-checkpoint' , filename='sample_audio.flac' , repo_type='dataset' , )
lowerCamelCase , lowerCamelCase = torchaudio.load(UpperCamelCase_ )
lowerCamelCase = waveform.squeeze().numpy()
lowerCamelCase = feature_extractor(UpperCamelCase_ , sampling_rate=1_6_0_0_0 , return_tensors='pt' )
# forward pass
lowerCamelCase = model(**UpperCamelCase_ )
lowerCamelCase = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
lowerCamelCase = torch.tensor([-0.8_760, -7.0_042, -8.6_602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
lowerCamelCase = torch.tensor([-1.1_986, -7.0_903, -8.2_718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
lowerCamelCase = torch.tensor([-2.6_128, -8.0_080, -9.4_344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
lowerCamelCase = torch.tensor([-1.5_080, -7.4_534, -8.8_917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
lowerCamelCase = torch.tensor([-0.5_050, -6.5_833, -8.0_843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
lowerCamelCase = torch.tensor([-0.3_826, -7.0_336, -8.2_413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
lowerCamelCase = torch.tensor([-1.2_113, -6.9_101, -8.3_470] )
elif model_name == "ast-finetuned-speech-commands-v2":
lowerCamelCase = torch.tensor([6.1_589, -8.0_566, -8.7_984] )
else:
raise ValueError('Unknown model name' )
if not torch.allclose(logits[0, :3] , UpperCamelCase_ , atol=1E-4 ):
raise ValueError('Logits don\'t match' )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCamelCase_ )
print(f'''Saving feature extractor to {pytorch_dump_folder_path}''' )
feature_extractor.save_pretrained(UpperCamelCase_ )
if push_to_hub:
print('Pushing model and feature extractor to the hub...' )
model.push_to_hub(f'''MIT/{model_name}''' )
feature_extractor.push_to_hub(f'''MIT/{model_name}''' )
if __name__ == "__main__":
_lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
_lowerCAmelCase : Tuple = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 246 | 1 |
from collections import defaultdict
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
lowerCAmelCase_ = 1
lowerCAmelCase_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__lowerCAmelCase )
if ret % 2 == 0:
cuts.append(__lowerCAmelCase )
return ret
def lowerCamelCase__ ( ):
"""simple docstring"""
dfs(1 )
if __name__ == "__main__":
_A, _A = 10, 9
_A = defaultdict(list)
_A = {}
_A = []
_A = 0
_A = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 279 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_A = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class _lowerCAmelCase ( nn.Module ):
def __init__( self , _UpperCamelCase ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ = torchvision.models.resnetaaa(pretrained=_UpperCamelCase )
lowerCAmelCase_ = list(model.children() )[:-2]
lowerCAmelCase_ = nn.Sequential(*_UpperCamelCase )
lowerCAmelCase_ = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def __a ( self , _UpperCamelCase ) -> Dict:
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
lowerCAmelCase_ = self.pool(self.model(_UpperCamelCase ) )
lowerCAmelCase_ = torch.flatten(_UpperCamelCase , start_dim=2 )
lowerCAmelCase_ = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class _lowerCAmelCase ( __a ):
def __init__( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[str]:
lowerCAmelCase_ = [json.loads(_UpperCamelCase ) for l in open(_UpperCamelCase )]
lowerCAmelCase_ = os.path.dirname(_UpperCamelCase )
lowerCAmelCase_ = tokenizer
lowerCAmelCase_ = labels
lowerCAmelCase_ = len(_UpperCamelCase )
lowerCAmelCase_ = max_seq_length
lowerCAmelCase_ = transforms
def __len__( self ) -> Any:
return len(self.data )
def __getitem__( self , _UpperCamelCase ) -> Any:
lowerCAmelCase_ = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] , add_special_tokens=_UpperCamelCase ) )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = sentence[0], sentence[1:-1], sentence[-1]
lowerCAmelCase_ = sentence[: self.max_seq_length]
lowerCAmelCase_ = torch.zeros(self.n_classes )
lowerCAmelCase_ = 1
lowerCAmelCase_ = Image.open(os.path.join(self.data_dir , self.data[index]["img"] ) ).convert("RGB" )
lowerCAmelCase_ = self.transforms(_UpperCamelCase )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def __a ( self ) -> str:
lowerCAmelCase_ = Counter()
for row in self.data:
label_freqs.update(row["label"] )
return label_freqs
def lowerCamelCase__ ( __lowerCAmelCase : List[str] ):
"""simple docstring"""
lowerCAmelCase_ = [len(row["sentence"] ) for row in batch]
lowerCAmelCase_ , lowerCAmelCase_ = len(__lowerCAmelCase ), max(__lowerCAmelCase )
lowerCAmelCase_ = torch.zeros(__lowerCAmelCase , __lowerCAmelCase , dtype=torch.long )
lowerCAmelCase_ = torch.zeros(__lowerCAmelCase , __lowerCAmelCase , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(__lowerCAmelCase , __lowerCAmelCase ) ):
lowerCAmelCase_ = input_row["sentence"]
lowerCAmelCase_ = 1
lowerCAmelCase_ = torch.stack([row["image"] for row in batch] )
lowerCAmelCase_ = torch.stack([row["label"] for row in batch] )
lowerCAmelCase_ = torch.stack([row["image_start_token"] for row in batch] )
lowerCAmelCase_ = torch.stack([row["image_end_token"] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def lowerCamelCase__ ( ):
"""simple docstring"""
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def lowerCamelCase__ ( ):
"""simple docstring"""
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46_777_044, 0.44_531_429, 0.40_661_017] , std=[0.12_221_994, 0.12_145_835, 0.14_380_469] , ),
] )
| 279 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'andreasmadsen/efficient_mlm_m0.40': (
'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
),
}
class _SCREAMING_SNAKE_CASE ( A__ ):
lowerCamelCase_ = '''roberta-prelayernorm'''
def __init__( self : List[str] , snake_case_ : int=5_0265 , snake_case_ : Any=768 , snake_case_ : Dict=12 , snake_case_ : Optional[int]=12 , snake_case_ : Union[str, Any]=3072 , snake_case_ : Dict="gelu" , snake_case_ : List[str]=0.1 , snake_case_ : str=0.1 , snake_case_ : List[str]=512 , snake_case_ : Optional[int]=2 , snake_case_ : List[Any]=0.02 , snake_case_ : str=1E-12 , snake_case_ : Dict=1 , snake_case_ : Tuple=0 , snake_case_ : List[Any]=2 , snake_case_ : int="absolute" , snake_case_ : int=True , snake_case_ : List[str]=None , **snake_case_ : Tuple , ):
"""simple docstring"""
super().__init__(pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
A : int = vocab_size
A : List[Any] = hidden_size
A : List[Any] = num_hidden_layers
A : str = num_attention_heads
A : Tuple = hidden_act
A : List[Any] = intermediate_size
A : Optional[int] = hidden_dropout_prob
A : List[str] = attention_probs_dropout_prob
A : List[str] = max_position_embeddings
A : Dict = type_vocab_size
A : List[Any] = initializer_range
A : Dict = layer_norm_eps
A : Dict = position_embedding_type
A : Optional[Any] = use_cache
A : Union[str, Any] = classifier_dropout
class _SCREAMING_SNAKE_CASE ( A__ ):
@property
def _UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
if self.task == "multiple-choice":
A : Optional[int] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
A : Union[str, Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 256 |
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ ):
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' )
if years_to_repay <= 0 or not isinstance(lowercase__ , lowercase__ ):
raise Exception('''Years to repay must be an integer > 0''' )
# Yearly rate is divided by 12 to get monthly rate
__SCREAMING_SNAKE_CASE : int = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
__SCREAMING_SNAKE_CASE : Union[str, Any] = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 696 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCAmelCase : Any = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
parser.add_argument('--model_name', default='roberta-large', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
lowerCAmelCase : int = parser.parse_args()
if args.model_type == "roberta":
lowerCAmelCase : int = RobertaForMaskedLM.from_pretrained(args.model_name)
lowerCAmelCase : int = 'roberta'
elif args.model_type == "gpt2":
lowerCAmelCase : Optional[int] = GPTaLMHeadModel.from_pretrained(args.model_name)
lowerCAmelCase : Optional[int] = 'transformer'
lowerCAmelCase : str = model.state_dict()
lowerCAmelCase : List[str] = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
lowerCAmelCase : Any = state_dict[f"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
lowerCAmelCase : Tuple = f"""{prefix}.embeddings.{w}.weight"""
lowerCAmelCase : str = state_dict[param_name]
for w in ["weight", "bias"]:
lowerCAmelCase : List[Any] = f"""{prefix}.embeddings.LayerNorm.{w}"""
lowerCAmelCase : str = state_dict[param_name]
# Transformer Blocks #
lowerCAmelCase : Any = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
lowerCAmelCase : int = state_dict[
f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
lowerCAmelCase : Union[str, Any] = state_dict[f"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
lowerCAmelCase : Optional[Any] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
lowerCAmelCase : Any = state_dict[f"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
lowerCAmelCase : List[str] = state_dict[f"""lm_head.dense.{w}"""]
lowerCAmelCase : Any = state_dict[f"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
lowerCAmelCase : Dict = state_dict[f"""{prefix}.ln_f.{w}"""]
lowerCAmelCase : Tuple = state_dict['lm_head.weight']
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 432 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE__ ( snake_case_):
def __init__( self , A_ , A_ , A_ , A_ = None , )-> Optional[int]:
'''simple docstring'''
super().__init__()
self.register_modules(transformer=A_ , vae=A_ , scheduler=A_ )
# create a imagenet -> id dictionary for easier use
UpperCamelCase = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(',' ):
UpperCamelCase = int(A_ )
UpperCamelCase = dict(sorted(self.labels.items() ) )
def UpperCAmelCase_ ( self , A_ )-> List[int]:
'''simple docstring'''
if not isinstance(A_ , A_ ):
UpperCamelCase = list(A_ )
for l in label:
if l not in self.labels:
raise ValueError(
F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self , A_ , A_ = 4.0 , A_ = None , A_ = 50 , A_ = "pil" , A_ = True , )-> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
UpperCamelCase = len(A_ )
UpperCamelCase = self.transformer.config.sample_size
UpperCamelCase = self.transformer.config.in_channels
UpperCamelCase = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=A_ , device=self.device , dtype=self.transformer.dtype , )
UpperCamelCase = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
UpperCamelCase = torch.tensor(A_ , device=self.device ).reshape(-1 )
UpperCamelCase = torch.tensor([1000] * batch_size , device=self.device )
UpperCamelCase = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(A_ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
UpperCamelCase = latent_model_input[: len(A_ ) // 2]
UpperCamelCase = torch.cat([half, half] , dim=0 )
UpperCamelCase = self.scheduler.scale_model_input(A_ , A_ )
UpperCamelCase = t
if not torch.is_tensor(A_ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
UpperCamelCase = latent_model_input.device.type == 'mps'
if isinstance(A_ , A_ ):
UpperCamelCase = torch.floataa if is_mps else torch.floataa
else:
UpperCamelCase = torch.intaa if is_mps else torch.intaa
UpperCamelCase = torch.tensor([timesteps] , dtype=A_ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
UpperCamelCase = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
UpperCamelCase = self.transformer(
A_ , timestep=A_ , class_labels=A_ ).sample
# perform guidance
if guidance_scale > 1:
UpperCamelCase , UpperCamelCase = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
UpperCamelCase , UpperCamelCase = torch.split(A_ , len(A_ ) // 2 , dim=0 )
UpperCamelCase = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
UpperCamelCase = torch.cat([half_eps, half_eps] , dim=0 )
UpperCamelCase = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
UpperCamelCase , UpperCamelCase = torch.split(A_ , A_ , dim=1 )
else:
UpperCamelCase = noise_pred
# compute previous image: x_t -> x_t-1
UpperCamelCase = self.scheduler.step(A_ , A_ , A_ ).prev_sample
if guidance_scale > 1:
UpperCamelCase , UpperCamelCase = latent_model_input.chunk(2 , dim=0 )
else:
UpperCamelCase = latent_model_input
UpperCamelCase = 1 / self.vae.config.scaling_factor * latents
UpperCamelCase = self.vae.decode(A_ ).sample
UpperCamelCase = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCamelCase = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCamelCase = self.numpy_to_pil(A_ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=A_ )
| 432 | 1 |
'''simple docstring'''
def a ( _UpperCAmelCase ) -> list:
"""simple docstring"""
if any(not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(_UpperCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(_UpperCAmelCase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 697 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase =logging.get_logger(__name__)
__lowerCAmelCase ={
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class _snake_case ( snake_case ):
"""simple docstring"""
_UpperCamelCase = "lilt"
def __init__( self , UpperCAmelCase__=3_0522 , UpperCAmelCase__=768 , UpperCAmelCase__=12 , UpperCAmelCase__=12 , UpperCAmelCase__=3072 , UpperCAmelCase__="gelu" , UpperCAmelCase__=0.1 , UpperCAmelCase__=0.1 , UpperCAmelCase__=512 , UpperCAmelCase__=2 , UpperCAmelCase__=0.0_2 , UpperCAmelCase__=1e-12 , UpperCAmelCase__=0 , UpperCAmelCase__="absolute" , UpperCAmelCase__=None , UpperCAmelCase__=4 , UpperCAmelCase__=1024 , **UpperCAmelCase__ , ) -> Optional[Any]:
super().__init__(pad_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
a_ = vocab_size
a_ = hidden_size
a_ = num_hidden_layers
a_ = num_attention_heads
a_ = hidden_act
a_ = intermediate_size
a_ = hidden_dropout_prob
a_ = attention_probs_dropout_prob
a_ = max_position_embeddings
a_ = type_vocab_size
a_ = initializer_range
a_ = layer_norm_eps
a_ = position_embedding_type
a_ = classifier_dropout
a_ = channel_shrink_ratio
a_ = max_ad_position_embeddings
| 697 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self : Union[str, Any] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
lowercase__ = FlaxDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''', safety_checker=lowerCamelCase, cache_dir=lowerCamelCase )
lowercase__ = [t[-1] for t in os.walk(os.path.join(lowerCamelCase, os.listdir(lowerCamelCase )[0], '''snapshots''' ) )]
lowercase__ = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('''.bin''' ) for f in files )
@slow
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self : int ):
'''simple docstring'''
lowercase__ , lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''', safety_checker=lowerCamelCase )
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 4
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(lowerCamelCase )
# shard inputs and rng
lowercase__ = replicate(lowerCamelCase )
lowercase__ = jax.random.split(lowerCamelCase, lowerCamelCase )
lowercase__ = shard(lowerCamelCase )
lowercase__ = pipeline(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, jit=lowerCamelCase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 4.1514745 ) < 1E-3
assert np.abs(np.abs(lowerCamelCase, dtype=np.floataa ).sum() - 49947.875 ) < 5E-1
lowercase__ = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(lowerCamelCase ) == num_samples
def lowercase__ ( self : Any ):
'''simple docstring'''
lowercase__ , lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''', revision='''flax''', safety_checker=lowerCamelCase )
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 50
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(lowerCamelCase )
# shard inputs and rng
lowercase__ = replicate(lowerCamelCase )
lowercase__ = jax.random.split(lowerCamelCase, lowerCamelCase )
lowercase__ = shard(lowerCamelCase )
lowercase__ = pipeline(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, jit=lowerCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.05652401) ) < 1E-3
assert np.abs((np.abs(lowerCamelCase, dtype=np.floataa ).sum() - 2383808.2) ) < 5E-1
def lowercase__ ( self : int ):
'''simple docstring'''
lowercase__ , lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''', revision='''bf16''', dtype=jnp.bfloataa, safety_checker=lowerCamelCase )
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 50
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(lowerCamelCase )
# shard inputs and rng
lowercase__ = replicate(lowerCamelCase )
lowercase__ = jax.random.split(lowerCamelCase, lowerCamelCase )
lowercase__ = shard(lowerCamelCase )
lowercase__ = pipeline(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, jit=lowerCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.04003906) ) < 1E-3
assert np.abs((np.abs(lowerCamelCase, dtype=np.floataa ).sum() - 2373516.75) ) < 5E-1
def lowercase__ ( self : Dict ):
'''simple docstring'''
lowercase__ , lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''', revision='''bf16''', dtype=jnp.bfloataa )
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 50
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(lowerCamelCase )
# shard inputs and rng
lowercase__ = replicate(lowerCamelCase )
lowercase__ = jax.random.split(lowerCamelCase, lowerCamelCase )
lowercase__ = shard(lowerCamelCase )
lowercase__ = pipeline(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, jit=lowerCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.04003906) ) < 1E-3
assert np.abs((np.abs(lowerCamelCase, dtype=np.floataa ).sum() - 2373516.75) ) < 5E-1
def lowercase__ ( self : Optional[int] ):
'''simple docstring'''
lowercase__ = FlaxDDIMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule='''scaled_linear''', set_alpha_to_one=lowerCamelCase, steps_offset=1, )
lowercase__ , lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''', revision='''bf16''', dtype=jnp.bfloataa, scheduler=lowerCamelCase, safety_checker=lowerCamelCase, )
lowercase__ = scheduler.create_state()
lowercase__ = scheduler_state
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.random.PRNGKey(0 )
lowercase__ = 50
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = pipeline.prepare_inputs(lowerCamelCase )
# shard inputs and rng
lowercase__ = replicate(lowerCamelCase )
lowercase__ = jax.random.split(lowerCamelCase, lowerCamelCase )
lowercase__ = shard(lowerCamelCase )
lowercase__ = pipeline(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, jit=lowerCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:], dtype=np.floataa ).sum() - 0.045043945) ) < 1E-3
assert np.abs((np.abs(lowerCamelCase, dtype=np.floataa ).sum() - 2347693.5) ) < 5E-1
def lowercase__ ( self : str ):
'''simple docstring'''
lowercase__ = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
lowercase__ = jax.device_count()
lowercase__ = num_samples * [prompt]
lowercase__ = jax.random.split(jax.random.PRNGKey(0 ), lowerCamelCase )
lowercase__ , lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''', revision='''bf16''', dtype=jnp.bfloataa, safety_checker=lowerCamelCase, )
lowercase__ = replicate(lowerCamelCase )
lowercase__ = pipeline.prepare_inputs(lowerCamelCase )
lowercase__ = shard(lowerCamelCase )
lowercase__ = pipeline(lowerCamelCase, lowerCamelCase, lowerCamelCase, jit=lowerCamelCase ).images
assert images.shape == (num_samples, 1, 512, 512, 3)
lowercase__ = images[2, 0, 256, 10:17, 1]
# With memory efficient attention
lowercase__ , lowercase__ = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''', revision='''bf16''', dtype=jnp.bfloataa, safety_checker=lowerCamelCase, use_memory_efficient_attention=lowerCamelCase, )
lowercase__ = replicate(lowerCamelCase )
lowercase__ = pipeline.prepare_inputs(lowerCamelCase )
lowercase__ = shard(lowerCamelCase )
lowercase__ = pipeline(lowerCamelCase, lowerCamelCase, lowerCamelCase, jit=lowerCamelCase ).images
assert images_eff.shape == (num_samples, 1, 512, 512, 3)
lowercase__ = images[2, 0, 256, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1E-2
| 700 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A__ : Optional[int] = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
A__ : List[str] = {
'bert-base-uncased': 5_12,
'bert-large-uncased': 5_12,
'bert-base-cased': 5_12,
'bert-large-cased': 5_12,
'bert-base-multilingual-uncased': 5_12,
'bert-base-multilingual-cased': 5_12,
'bert-base-chinese': 5_12,
'bert-base-german-cased': 5_12,
'bert-large-uncased-whole-word-masking': 5_12,
'bert-large-cased-whole-word-masking': 5_12,
'bert-large-uncased-whole-word-masking-finetuned-squad': 5_12,
'bert-large-cased-whole-word-masking-finetuned-squad': 5_12,
'bert-base-cased-finetuned-mrpc': 5_12,
'bert-base-german-dbmdz-cased': 5_12,
'bert-base-german-dbmdz-uncased': 5_12,
'TurkuNLP/bert-base-finnish-cased-v1': 5_12,
'TurkuNLP/bert-base-finnish-uncased-v1': 5_12,
'wietsedv/bert-base-dutch-cased': 5_12,
}
A__ : Optional[int] = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_INIT_CONFIGURATION
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = BertTokenizer
def __init__( self : Any, lowerCamelCase : Optional[Any]=None, lowerCamelCase : Any=None, lowerCamelCase : Tuple=True, lowerCamelCase : Dict="[UNK]", lowerCamelCase : Any="[SEP]", lowerCamelCase : List[Any]="[PAD]", lowerCamelCase : Optional[Any]="[CLS]", lowerCamelCase : Dict="[MASK]", lowerCamelCase : List[Any]=True, lowerCamelCase : Tuple=None, **lowerCamelCase : Dict, ):
'''simple docstring'''
super().__init__(
lowerCamelCase, tokenizer_file=lowerCamelCase, do_lower_case=lowerCamelCase, unk_token=lowerCamelCase, sep_token=lowerCamelCase, pad_token=lowerCamelCase, cls_token=lowerCamelCase, mask_token=lowerCamelCase, tokenize_chinese_chars=lowerCamelCase, strip_accents=lowerCamelCase, **lowerCamelCase, )
lowercase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''', lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''', lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''', lowerCamelCase ) != tokenize_chinese_chars
):
lowercase__ = getattr(lowerCamelCase, normalizer_state.pop('''type''' ) )
lowercase__ = do_lower_case
lowercase__ = strip_accents
lowercase__ = tokenize_chinese_chars
lowercase__ = normalizer_class(**lowerCamelCase )
lowercase__ = do_lower_case
def lowercase__ ( self : Any, lowerCamelCase : List[Any], lowerCamelCase : Dict=None ):
'''simple docstring'''
lowercase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowercase__ ( self : List[Any], lowerCamelCase : List[int], lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = [self.sep_token_id]
lowercase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase__ ( self : Any, lowerCamelCase : str, lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
lowercase__ = self._tokenizer.model.save(lowerCamelCase, name=lowerCamelCase )
return tuple(lowerCamelCase )
| 671 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''MobileViTFeatureExtractor''']
lowerCAmelCase__ = ['''MobileViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileViTForImageClassification''',
'''MobileViTForSemanticSegmentation''',
'''MobileViTModel''',
'''MobileViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFMobileViTForImageClassification''',
'''TFMobileViTForSemanticSegmentation''',
'''TFMobileViTModel''',
'''TFMobileViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 83 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowercase_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : List[Any] = ['pixel_values']
def __init__( self : List[str] , a : bool = True , a : Dict[str, int] = None , a : int = 0.9 , a : PILImageResampling = PILImageResampling.BICUBIC , a : bool = True , a : Dict[str, int] = None , a : Union[int, float] = 1 / 255 , a : bool = True , a : bool = True , a : Optional[Union[float, List[float]]] = None , a : Optional[Union[float, List[float]]] = None , **a : int , )-> None:
"""simple docstring"""
super().__init__(**a )
lowercase__ = size if size is not None else {'shortest_edge': 224}
lowercase__ = get_size_dict(a , default_to_square=a )
lowercase__ = crop_size if crop_size is not None else {'height': 224, 'width': 224}
lowercase__ = get_size_dict(a , param_name='crop_size' )
lowercase__ = do_resize
lowercase__ = size
lowercase__ = crop_pct
lowercase__ = resample
lowercase__ = do_center_crop
lowercase__ = crop_size
lowercase__ = do_rescale
lowercase__ = rescale_factor
lowercase__ = do_normalize
lowercase__ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase__ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , a : np.ndarray , a : Dict[str, int] , a : Optional[float] = None , a : PILImageResampling = PILImageResampling.BICUBIC , a : Optional[Union[str, ChannelDimension]] = None , **a : Tuple , )-> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(a , default_to_square=a )
if "shortest_edge" not in size and ("height" not in size or "width" not in size):
raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
if crop_pct is not None:
if "shortest_edge" in size:
lowercase__ = int(size['shortest_edge'] / crop_pct )
elif "height" in size and "width" in size:
if size["height"] == size["width"]:
lowercase__ = int(size['height'] / crop_pct )
else:
lowercase__ = (int(size['height'] / crop_pct ), int(size['width'] / crop_pct ))
else:
raise ValueError('Invalid size for resize: {}'.format(a ) )
lowercase__ = get_resize_output_image_size(a , size=a , default_to_square=a )
else:
if "shortest_edge" in size:
lowercase__ = get_resize_output_image_size(a , size=size['shortest_edge'] , default_to_square=a )
elif "height" in size and "width" in size:
lowercase__ = (size['height'], size['width'])
else:
raise ValueError('Invalid size for resize: {}'.format(a ) )
return resize(a , size=a , resample=a , data_format=a , **a )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , a : np.ndarray , a : Dict[str, int] , a : Optional[Union[str, ChannelDimension]] = None , **a : Tuple , )-> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(a )
if "height" not in size or "width" not in size:
raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(a , size=(size['height'], size['width']) , data_format=a , **a )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , a : np.ndarray , a : Union[int, float] , a : Optional[Union[str, ChannelDimension]] = None , **a : List[str] , )-> Optional[Any]:
"""simple docstring"""
return rescale(a , scale=a , data_format=a , **a )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , a : np.ndarray , a : Union[float, List[float]] , a : Union[float, List[float]] , a : Optional[Union[str, ChannelDimension]] = None , **a : str , )-> np.ndarray:
"""simple docstring"""
return normalize(a , mean=a , std=a , data_format=a , **a )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , a : ImageInput , a : bool = None , a : Dict[str, int] = None , a : int = None , a : PILImageResampling = None , a : bool = None , a : Dict[str, int] = None , a : bool = None , a : float = None , a : bool = None , a : Optional[Union[float, List[float]]] = None , a : Optional[Union[float, List[float]]] = None , a : Optional[Union[str, TensorType]] = None , a : ChannelDimension = ChannelDimension.FIRST , **a : Any , )-> PIL.Image.Image:
"""simple docstring"""
lowercase__ = do_resize if do_resize is not None else self.do_resize
lowercase__ = crop_pct if crop_pct is not None else self.crop_pct
lowercase__ = resample if resample is not None else self.resample
lowercase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ = image_mean if image_mean is not None else self.image_mean
lowercase__ = image_std if image_std is not None else self.image_std
lowercase__ = size if size is not None else self.size
lowercase__ = get_size_dict(a , default_to_square=a )
lowercase__ = crop_size if crop_size is not None else self.crop_size
lowercase__ = get_size_dict(a , param_name='crop_size' )
lowercase__ = make_list_of_images(a )
if not valid_images(a ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_pct is None:
raise ValueError('Crop_pct must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
lowercase__ = [to_numpy_array(a ) for image in images]
if do_resize:
lowercase__ = [self.resize(image=a , size=a , crop_pct=a , resample=a ) for image in images]
if do_center_crop:
lowercase__ = [self.center_crop(image=a , size=a ) for image in images]
if do_rescale:
lowercase__ = [self.rescale(image=a , scale=a ) for image in images]
if do_normalize:
lowercase__ = [self.normalize(image=a , mean=a , std=a ) for image in images]
lowercase__ = [to_channel_dimension_format(a , a ) for image in images]
lowercase__ = {'pixel_values': images}
return BatchFeature(data=a , tensor_type=a )
| 235 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ) -> bool:
a_ : Dict = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ = 5_000 ) -> int:
a_ : str = [(i * (3 * i - 1)) // 2 for i in range(1, SCREAMING_SNAKE_CASE__ )]
for i, pentagonal_i in enumerate(SCREAMING_SNAKE_CASE__ ):
for j in range(SCREAMING_SNAKE_CASE__, len(SCREAMING_SNAKE_CASE__ ) ):
a_ : Dict = pentagonal_nums[j]
a_ : List[Any] = pentagonal_i + pentagonal_j
a_ : List[str] = pentagonal_j - pentagonal_i
if is_pentagonal(SCREAMING_SNAKE_CASE__ ) and is_pentagonal(SCREAMING_SNAKE_CASE__ ):
return b
return -1
if __name__ == "__main__":
print(F"""{solution() = }""")
| 713 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case_ ( a_ ):
__lowerCAmelCase = ["image_processor", "tokenizer"]
__lowerCAmelCase = "ViltImageProcessor"
__lowerCAmelCase = ("BertTokenizer", "BertTokenizerFast")
def __init__( self , a_=None , a_=None , **a_ ):
a_ : int = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , a_ , )
a_ : List[Any] = kwargs.pop("feature_extractor" )
a_ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(a_ , a_ )
a_ : Dict = self.image_processor
def __call__( self , a_ , a_ = None , a_ = True , a_ = False , a_ = None , a_ = None , a_ = 0 , a_ = None , a_ = None , a_ = None , a_ = False , a_ = False , a_ = False , a_ = False , a_ = True , a_ = None , **a_ , ):
a_ : Union[str, Any] = self.tokenizer(
text=a_ , add_special_tokens=a_ , padding=a_ , truncation=a_ , max_length=a_ , stride=a_ , pad_to_multiple_of=a_ , return_token_type_ids=a_ , return_attention_mask=a_ , return_overflowing_tokens=a_ , return_special_tokens_mask=a_ , return_offsets_mapping=a_ , return_length=a_ , verbose=a_ , return_tensors=a_ , **a_ , )
# add pixel_values + pixel_mask
a_ : List[Any] = self.image_processor(a_ , return_tensors=a_ )
encoding.update(a_ )
return encoding
def snake_case_ ( self , *a_ , **a_ ):
return self.tokenizer.batch_decode(*a_ , **a_ )
def snake_case_ ( self , *a_ , **a_ ):
return self.tokenizer.decode(*a_ , **a_ )
@property
def snake_case_ ( self ):
a_ : Union[str, Any] = self.tokenizer.model_input_names
a_ : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def snake_case_ ( self ):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , a_ , )
return self.image_processor_class
@property
def snake_case_ ( self ):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , a_ , )
return self.image_processor | 370 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase : List[str] = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : List[Any] = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[Any] = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
_lowercase : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 210 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class A__ ( _snake_case ):
lowercase = "bert-generation"
def __init__( self , UpperCamelCase__=50358 , UpperCamelCase__=1024 , UpperCamelCase__=24 , UpperCamelCase__=16 , UpperCamelCase__=4096 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=512 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-1_2 , UpperCamelCase__=0 , UpperCamelCase__=2 , UpperCamelCase__=1 , UpperCamelCase__="absolute" , UpperCamelCase__=True , **UpperCamelCase__ , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
A_ = vocab_size
A_ = hidden_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = hidden_act
A_ = intermediate_size
A_ = hidden_dropout_prob
A_ = attention_probs_dropout_prob
A_ = max_position_embeddings
A_ = initializer_range
A_ = layer_norm_eps
A_ = position_embedding_type
A_ = use_cache
| 288 | 0 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase ( self ):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(UpperCamelCase_ ):
lowercase_ :int = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :Any = FlaxAutoModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def UpperCamelCase ( self ):
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(UpperCamelCase_ ):
lowercase_ :str = AutoConfig.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
lowercase_ :Any = FlaxAutoModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ )
@slow
def UpperCamelCase ( self ):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
lowercase_ :Optional[int] = AutoTokenizer.from_pretrained(UpperCamelCase_ )
lowercase_ :int = FlaxBertModel.from_pretrained(UpperCamelCase_ )
lowercase_ :Dict = tokenizer('''Do you support jax jitted function?''' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**UpperCamelCase_ ):
return model(**UpperCamelCase_ )
eval(**UpperCamelCase_ ).block_until_ready()
@slow
def UpperCamelCase ( self ):
for model_name in ["roberta-base", "roberta-large"]:
lowercase_ :int = AutoTokenizer.from_pretrained(UpperCamelCase_ )
lowercase_ :int = FlaxRobertaModel.from_pretrained(UpperCamelCase_ )
lowercase_ :Any = tokenizer('''Do you support jax jitted function?''' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**UpperCamelCase_ ):
return model(**UpperCamelCase_ )
eval(**UpperCamelCase_ ).block_until_ready()
def UpperCamelCase ( self ):
with self.assertRaisesRegex(
UpperCamelCase_ , '''bert-base is not a local folder and is not a valid model identifier''' ):
lowercase_ :Optional[Any] = FlaxAutoModel.from_pretrained('''bert-base''' )
def UpperCamelCase ( self ):
with self.assertRaisesRegex(
UpperCamelCase_ , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
lowercase_ :Union[str, Any] = FlaxAutoModel.from_pretrained(UpperCamelCase_ , revision='''aaaaaa''' )
def UpperCamelCase ( self ):
with self.assertRaisesRegex(
UpperCamelCase_ , '''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''' , ):
lowercase_ :List[Any] = FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )
def UpperCamelCase ( self ):
with self.assertRaisesRegex(UpperCamelCase_ , '''Use `from_pt=True` to load this model''' ):
lowercase_ :Tuple = FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )
| 713 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase ( self ):
lowercase_ :Dict = 1
lowercase_ :Optional[Any] = 3
lowercase_ :Optional[int] = (32, 32)
lowercase_ :Dict = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(UpperCamelCase_ )
return image
@property
def UpperCamelCase ( self ):
torch.manual_seed(0 )
lowercase_ :Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
return model
@property
def UpperCamelCase ( self ):
torch.manual_seed(0 )
lowercase_ :Dict = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
return model
@property
def UpperCamelCase ( self ):
torch.manual_seed(0 )
lowercase_ :str = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5006 , )
return RobertaSeriesModelWithTransformation(UpperCamelCase_ )
@property
def UpperCamelCase ( self ):
def extract(*UpperCamelCase_ , **UpperCamelCase_ ):
class UpperCamelCase :
'''simple docstring'''
def __init__( self ):
lowercase_ :List[str] = torch.ones([0] )
def UpperCamelCase ( self , UpperCamelCase_ ):
self.pixel_values.to(UpperCamelCase_ )
return self
return Out()
return extract
def UpperCamelCase ( self ):
lowercase_ :List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowercase_ :Dict = self.dummy_cond_unet
lowercase_ :Optional[Any] = PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
lowercase_ :Union[str, Any] = self.dummy_vae
lowercase_ :Any = self.dummy_text_encoder
lowercase_ :List[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
lowercase_ :List[str] = 77
lowercase_ :int = self.dummy_image.to(UpperCamelCase_ )
lowercase_ :Optional[Any] = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
lowercase_ :str = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase_ , scheduler=UpperCamelCase_ , vae=UpperCamelCase_ , text_encoder=UpperCamelCase_ , tokenizer=UpperCamelCase_ , safety_checker=UpperCamelCase_ , feature_extractor=self.dummy_extractor , )
lowercase_ :List[str] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCamelCase_ )
lowercase_ :int = alt_pipe.to(UpperCamelCase_ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase_ :Any = '''A painting of a squirrel eating a burger'''
lowercase_ :Tuple = torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
lowercase_ :Optional[int] = alt_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , image=UpperCamelCase_ , )
lowercase_ :Dict = output.images
lowercase_ :Tuple = torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
lowercase_ :Optional[int] = alt_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type='''np''' , image=UpperCamelCase_ , return_dict=UpperCamelCase_ , )[0]
lowercase_ :Optional[int] = image[0, -3:, -3:, -1]
lowercase_ :Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase_ :int = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def UpperCamelCase ( self ):
lowercase_ :List[str] = self.dummy_cond_unet
lowercase_ :Dict = PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
lowercase_ :Tuple = self.dummy_vae
lowercase_ :Dict = self.dummy_text_encoder
lowercase_ :Tuple = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
lowercase_ :str = 77
lowercase_ :str = self.dummy_image.to(UpperCamelCase_ )
# put models in fp16
lowercase_ :Union[str, Any] = unet.half()
lowercase_ :Union[str, Any] = vae.half()
lowercase_ :List[str] = bert.half()
# make sure here that pndm scheduler skips prk
lowercase_ :List[Any] = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase_ , scheduler=UpperCamelCase_ , vae=UpperCamelCase_ , text_encoder=UpperCamelCase_ , tokenizer=UpperCamelCase_ , safety_checker=UpperCamelCase_ , feature_extractor=self.dummy_extractor , )
lowercase_ :List[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=UpperCamelCase_ )
lowercase_ :List[str] = alt_pipe.to(UpperCamelCase_ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase_ :str = '''A painting of a squirrel eating a burger'''
lowercase_ :Union[str, Any] = torch.manual_seed(0 )
lowercase_ :Any = alt_pipe(
[prompt] , generator=UpperCamelCase_ , num_inference_steps=2 , output_type='''np''' , image=UpperCamelCase_ , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def UpperCamelCase ( self ):
lowercase_ :Optional[int] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
# resize to resolution that is divisible by 8 but not 16 or 32
lowercase_ :Optional[Any] = init_image.resize((760, 504) )
lowercase_ :List[str] = '''BAAI/AltDiffusion'''
lowercase_ :Optional[Any] = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase_ , safety_checker=UpperCamelCase_ , )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
lowercase_ :Optional[Any] = '''A fantasy landscape, trending on artstation'''
lowercase_ :Optional[Any] = torch.manual_seed(0 )
lowercase_ :str = pipe(
prompt=UpperCamelCase_ , image=UpperCamelCase_ , strength=0.75 , guidance_scale=7.5 , generator=UpperCamelCase_ , output_type='''np''' , )
lowercase_ :Optional[Any] = output.images[0]
lowercase_ :Optional[int] = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
lowercase_ :Any = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ):
lowercase_ :Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
lowercase_ :Any = init_image.resize((768, 512) )
lowercase_ :List[str] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''' )
lowercase_ :List[Any] = '''BAAI/AltDiffusion'''
lowercase_ :Tuple = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase_ , safety_checker=UpperCamelCase_ , )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
lowercase_ :List[str] = '''A fantasy landscape, trending on artstation'''
lowercase_ :Optional[int] = torch.manual_seed(0 )
lowercase_ :Tuple = pipe(
prompt=UpperCamelCase_ , image=UpperCamelCase_ , strength=0.75 , guidance_scale=7.5 , generator=UpperCamelCase_ , output_type='''np''' , )
lowercase_ :int = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1E-2
| 441 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 ,len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 ,len(a__ ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 ,len(a__ ) ):
for j in range(1 ,len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] ,matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'AutoTokenizer'
__lowerCamelCase = ['tokenizer']
__lowerCamelCase = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self :Dict , _lowercase :List[str] , _lowercase :List[Any]=None ):
'''simple docstring'''
super().__init__(_lowercase )
lowercase__ = speaker_embeddings
@classmethod
def UpperCAmelCase ( cls :Any , _lowercase :int , _lowercase :str="speaker_embeddings_path.json" , **_lowercase :List[str] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
lowercase__ = get_file_from_repo(
_lowercase , _lowercase , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if speaker_embeddings_path is None:
logger.warning(
f'''`{os.path.join(_lowercase , _lowercase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
lowercase__ = None
else:
with open(_lowercase ) as speaker_embeddings_json:
lowercase__ = json.load(_lowercase )
else:
lowercase__ = None
lowercase__ = AutoTokenizer.from_pretrained(_lowercase , **_lowercase )
return cls(tokenizer=_lowercase , speaker_embeddings=_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :List[str]="speaker_embeddings_path.json" , _lowercase :Any="speaker_embeddings" , _lowercase :bool = False , **_lowercase :Any , ):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_lowercase , _lowercase , "v2" ) , exist_ok=_lowercase )
lowercase__ = {}
lowercase__ = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
lowercase__ = self._load_voice_preset(_lowercase )
lowercase__ = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , _lowercase , f'''{prompt_key}_{key}''' ) , voice_preset[key] , allow_pickle=_lowercase , )
lowercase__ = os.path.join(_lowercase , f'''{prompt_key}_{key}.npy''' )
lowercase__ = tmp_dict
with open(os.path.join(_lowercase , _lowercase ) , "w" ) as fp:
json.dump(_lowercase , _lowercase )
super().save_pretrained(_lowercase , _lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :str = None , **_lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = self.speaker_embeddings[voice_preset]
lowercase__ = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
lowercase__ = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if path is None:
raise ValueError(
f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
lowercase__ = np.load(_lowercase )
return voice_preset_dict
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self :Optional[Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=None , _lowercase :List[str]="pt" , _lowercase :List[Any]=2_56 , _lowercase :List[str]=False , _lowercase :Union[str, Any]=True , _lowercase :Dict=False , **_lowercase :Tuple , ):
'''simple docstring'''
if voice_preset is not None and not isinstance(_lowercase , _lowercase ):
if (
isinstance(_lowercase , _lowercase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
lowercase__ = self._load_voice_preset(_lowercase )
else:
if isinstance(_lowercase , _lowercase ) and not voice_preset.endswith(".npz" ):
lowercase__ = voice_preset + ".npz"
lowercase__ = np.load(_lowercase )
if voice_preset is not None:
self._validate_voice_preset_dict(_lowercase , **_lowercase )
lowercase__ = BatchFeature(data=_lowercase , tensor_type=_lowercase )
lowercase__ = self.tokenizer(
_lowercase , return_tensors=_lowercase , padding="max_length" , max_length=_lowercase , return_attention_mask=_lowercase , return_token_type_ids=_lowercase , add_special_tokens=_lowercase , **_lowercase , )
if voice_preset is not None:
lowercase__ = voice_preset
return encoded_text
| 655 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = ["image_processor", "tokenizer"]
lowerCAmelCase = "ViTImageProcessor"
lowerCAmelCase = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase):
'''simple docstring'''
__A : str = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , _UpperCAmelCase , )
__A : str = kwargs.pop('feature_extractor')
__A : Optional[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.')
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.')
super().__init__(_UpperCAmelCase , _UpperCAmelCase)
def __call__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase):
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError('You have to specify either text, visual prompt or images.')
if text is not None and visual_prompt is not None:
raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.')
if text is not None:
__A : Tuple = self.tokenizer(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase)
if visual_prompt is not None:
__A : Dict = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase)
if images is not None:
__A : List[Any] = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase)
if visual_prompt is not None and images is not None:
__A : Optional[int] = {
'pixel_values': image_features.pixel_values,
'conditional_pixel_values': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
__A : List[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
__A : Union[str, Any] = {
'conditional_pixel_values': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**_UpperCAmelCase) , tensor_type=_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , *_UpperCAmelCase , **_UpperCAmelCase):
'''simple docstring'''
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , *_UpperCAmelCase , **_UpperCAmelCase):
'''simple docstring'''
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase)
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , _UpperCAmelCase , )
return self.image_processor_class
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _UpperCAmelCase , )
return self.image_processor | 706 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase__ : Optional[int] = logging.get_logger(__name__)
lowercase__ : List[str] = {'''vocab_file''': '''spiece.model'''}
lowercase__ : Optional[int] = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
}
}
lowercase__ : List[str] = {
'''google/bigbird-roberta-base''': 40_96,
'''google/bigbird-roberta-large''': 40_96,
'''google/bigbird-base-trivia-itc''': 40_96,
}
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['''input_ids''', '''attention_mask''']
lowerCAmelCase = []
def __init__( self , _UpperCAmelCase , _UpperCAmelCase="<unk>" , _UpperCAmelCase="<s>" , _UpperCAmelCase="</s>" , _UpperCAmelCase="<pad>" , _UpperCAmelCase="[SEP]" , _UpperCAmelCase="[MASK]" , _UpperCAmelCase="[CLS]" , _UpperCAmelCase = None , **_UpperCAmelCase , ):
'''simple docstring'''
__A : Union[str, Any] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else bos_token
__A : List[Any] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else eos_token
__A : Optional[int] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else unk_token
__A : str = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else pad_token
__A : int = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else cls_token
__A : Optional[int] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__A : Optional[Any] = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase , _UpperCAmelCase) else mask_token
__A : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , )
__A : Tuple = vocab_file
__A : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(_UpperCAmelCase)
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return self.sp_model.get_piece_size()
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = {self.convert_ids_to_tokens(_UpperCAmelCase): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self):
'''simple docstring'''
__A : Optional[int] = self.__dict__.copy()
__A : List[str] = None
return state
def __setstate__( self , _UpperCAmelCase):
'''simple docstring'''
__A : Dict = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
__A : Tuple = {}
__A : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
return self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
return self.sp_model.piece_to_id(_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
__A : Optional[Any] = self.sp_model.IdToPiece(_UpperCAmelCase)
return token
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
__A : str = []
__A : int = ''
__A : str = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCAmelCase) + token
__A : Dict = True
__A : List[Any] = []
else:
current_sub_tokens.append(_UpperCAmelCase)
__A : Union[str, Any] = False
out_string += self.sp_model.decode(_UpperCAmelCase)
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = False , _UpperCAmelCase = None , _UpperCAmelCase = True , **_UpperCAmelCase , ):
'''simple docstring'''
__A : Tuple = kwargs.pop('use_source_tokenizer' , _UpperCAmelCase)
__A : str = self.convert_ids_to_tokens(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
__A : Dict = []
__A : Dict = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase))
__A : Any = []
sub_texts.append(_UpperCAmelCase)
else:
current_sub_text.append(_UpperCAmelCase)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase))
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
__A : Tuple = re.sub(R' (\[(MASK|SEP)\])' , R'\1' , ' '.join(_UpperCAmelCase))
else:
__A : Any = ''.join(_UpperCAmelCase)
__A : List[str] = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
__A : str = self.clean_up_tokenization(_UpperCAmelCase)
return clean_text
else:
return text
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = None):
'''simple docstring'''
if not os.path.isdir(_UpperCAmelCase):
logger.error(F'Vocabulary path ({save_directory}) should be a directory')
return
__A : int = os.path.join(
_UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(_UpperCAmelCase) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , _UpperCAmelCase)
elif not os.path.isfile(self.vocab_file):
with open(_UpperCAmelCase , 'wb') as fi:
__A : Any = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase)
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = None):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A : Union[str, Any] = [self.cls_token_id]
__A : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = False):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase)
if token_ids_a is None:
return [1] + ([0] * len(_UpperCAmelCase)) + [1]
return [1] + ([0] * len(_UpperCAmelCase)) + [1] + ([0] * len(_UpperCAmelCase)) + [1]
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = None):
'''simple docstring'''
__A : str = [self.sep_token_id]
__A : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] | 338 | 0 |
'''simple docstring'''
def __lowercase (_lowercase = 100 ) -> int:
"""simple docstring"""
__lowerCamelCase : str = n * (n + 1) * (2 * n + 1) / 6
__lowerCamelCase : int = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 150 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ :List[str] = logging.get_logger(__name__)
UpperCAmelCase__ :Union[str, Any] = {
"""BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
snake_case__ : str = 'altclip_text_model'
def __init__( self : List[Any] , A__ : Optional[int]=250002 , A__ : Any=1024 , A__ : List[Any]=24 , A__ : Dict=16 , A__ : Union[str, Any]=4096 , A__ : Union[str, Any]="gelu" , A__ : str=0.1 , A__ : int=0.1 , A__ : str=514 , A__ : Optional[int]=1 , A__ : Optional[Any]=0.02 , A__ : int=0.02 , A__ : Optional[Any]=1e-0_5 , A__ : int=1 , A__ : Optional[Any]=0 , A__ : Dict=2 , A__ : Optional[int]="absolute" , A__ : Optional[int]=True , A__ : List[str]=768 , **A__ : List[str] , ):
"""simple docstring"""
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
__lowerCamelCase : List[str] = vocab_size
__lowerCamelCase : Optional[Any] = hidden_size
__lowerCamelCase : List[str] = num_hidden_layers
__lowerCamelCase : Tuple = num_attention_heads
__lowerCamelCase : List[Any] = hidden_act
__lowerCamelCase : Union[str, Any] = intermediate_size
__lowerCamelCase : Dict = hidden_dropout_prob
__lowerCamelCase : Any = attention_probs_dropout_prob
__lowerCamelCase : List[str] = max_position_embeddings
__lowerCamelCase : Optional[Any] = type_vocab_size
__lowerCamelCase : int = initializer_range
__lowerCamelCase : Optional[int] = initializer_factor
__lowerCamelCase : List[Any] = layer_norm_eps
__lowerCamelCase : List[str] = position_embedding_type
__lowerCamelCase : str = use_cache
__lowerCamelCase : Optional[Any] = project_dim
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
snake_case__ : List[Any] = 'altclip_vision_model'
def __init__( self : Optional[int] , A__ : str=768 , A__ : str=3072 , A__ : str=512 , A__ : Optional[int]=12 , A__ : List[Any]=12 , A__ : Union[str, Any]=3 , A__ : Dict=224 , A__ : List[Any]=32 , A__ : List[Any]="quick_gelu" , A__ : Dict=1e-5 , A__ : List[str]=0.0 , A__ : Dict=0.02 , A__ : List[str]=1.0 , **A__ : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**A__ )
__lowerCamelCase : Optional[int] = hidden_size
__lowerCamelCase : Optional[int] = intermediate_size
__lowerCamelCase : Optional[Any] = projection_dim
__lowerCamelCase : Union[str, Any] = num_hidden_layers
__lowerCamelCase : Optional[Any] = num_attention_heads
__lowerCamelCase : str = num_channels
__lowerCamelCase : Any = patch_size
__lowerCamelCase : Any = image_size
__lowerCamelCase : Any = initializer_range
__lowerCamelCase : List[str] = initializer_factor
__lowerCamelCase : List[str] = attention_dropout
__lowerCamelCase : Any = layer_norm_eps
__lowerCamelCase : Any = hidden_act
@classmethod
def a_ ( cls : str , A__ : Union[str, os.PathLike] , **A__ : List[str] ):
"""simple docstring"""
cls._set_token_in_kwargs(A__ )
__lowerCamelCase , __lowerCamelCase : str = cls.get_config_dict(A__ , **A__ )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get("""model_type""" ) == "altclip":
__lowerCamelCase : Optional[Any] = 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(A__ , **A__ )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
snake_case__ : int = 'altclip'
snake_case__ : Dict = True
def __init__( self : Optional[Any] , A__ : Optional[Any]=None , A__ : Union[str, Any]=None , A__ : Union[str, Any]=768 , A__ : Tuple=2.6592 , **A__ : List[Any] ):
"""simple docstring"""
__lowerCamelCase : str = kwargs.pop("""text_config_dict""" , A__ )
__lowerCamelCase : Dict = kwargs.pop("""vision_config_dict""" , A__ )
super().__init__(**A__ )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
__lowerCamelCase : Any = {}
# This is the complete result when using `text_config_dict`.
__lowerCamelCase : Tuple = AltCLIPTextConfig(**A__ ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
__lowerCamelCase : Optional[Any] = (
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
f"The value `text_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
__lowerCamelCase : int = (
f"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
f"value `text_config[\"{key}\"]` will be overriden."
)
logger.warning(A__ )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
__lowerCamelCase : Dict = {}
# This is the complete result when using `vision_config_dict`.
__lowerCamelCase : List[str] = AltCLIPVisionConfig(**A__ ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
__lowerCamelCase : str = {
str(A__ ): value for key, value in _vision_config_dict["""id2label"""].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
__lowerCamelCase : List[Any] = (
f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
f"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
__lowerCamelCase : Optional[Any] = (
f"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
f"The value `vision_config[\"{key}\"]` will be overriden."
)
logger.warning(A__ )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
__lowerCamelCase : List[Any] = {}
logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.""" )
if vision_config is None:
__lowerCamelCase : List[str] = {}
logger.info("""`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.""" )
__lowerCamelCase : Union[str, Any] = AltCLIPTextConfig(**A__ )
__lowerCamelCase : Optional[int] = AltCLIPVisionConfig(**A__ )
__lowerCamelCase : Optional[Any] = projection_dim
__lowerCamelCase : List[str] = logit_scale_init_value
__lowerCamelCase : Union[str, Any] = 1.0
@classmethod
def a_ ( cls : Optional[Any] , A__ : AltCLIPTextConfig , A__ : AltCLIPVisionConfig , **A__ : List[str] ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **A__ )
def a_ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase : List[Any] = copy.deepcopy(self.__dict__ )
__lowerCamelCase : Optional[Any] = self.text_config.to_dict()
__lowerCamelCase : Tuple = self.vision_config.to_dict()
__lowerCamelCase : Tuple = self.__class__.model_type
return output
| 150 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torchaudio,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Any = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
_SCREAMING_SNAKE_CASE : Union[str, Any] = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
def a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> Optional[Any]:
__lowerCAmelCase = AudioClassificationPipeline(model=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ )
# test with a raw waveform
__lowerCAmelCase = np.zeros((3_40_00,) )
__lowerCAmelCase = np.zeros((1_40_00,) )
return audio_classifier, [audioa, audio]
def a ( self : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> List[str]:
__lowerCAmelCase , __lowerCAmelCase = examples
__lowerCAmelCase = audio_classifier(SCREAMING_SNAKE_CASE__ )
# by default a model is initialized with num_labels=2
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{"""score""": ANY(SCREAMING_SNAKE_CASE__ ), """label""": ANY(SCREAMING_SNAKE_CASE__ )},
{"""score""": ANY(SCREAMING_SNAKE_CASE__ ), """label""": ANY(SCREAMING_SNAKE_CASE__ )},
] , )
__lowerCAmelCase = audio_classifier(SCREAMING_SNAKE_CASE__ , top_k=1 )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{"""score""": ANY(SCREAMING_SNAKE_CASE__ ), """label""": ANY(SCREAMING_SNAKE_CASE__ )},
] , )
self.run_torchaudio(SCREAMING_SNAKE_CASE__ )
@require_torchaudio
def a ( self : List[str] , SCREAMING_SNAKE_CASE__ : Dict ) -> List[str]:
import datasets
# test with a local file
__lowerCAmelCase = datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
__lowerCAmelCase = dataset[0]["""audio"""]["""array"""]
__lowerCAmelCase = audio_classifier(SCREAMING_SNAKE_CASE__ )
self.assertEqual(
SCREAMING_SNAKE_CASE__ , [
{"""score""": ANY(SCREAMING_SNAKE_CASE__ ), """label""": ANY(SCREAMING_SNAKE_CASE__ )},
{"""score""": ANY(SCREAMING_SNAKE_CASE__ ), """label""": ANY(SCREAMING_SNAKE_CASE__ )},
] , )
@require_torch
def a ( self : str ) -> Union[str, Any]:
__lowerCAmelCase = """anton-l/wav2vec2-random-tiny-classifier"""
__lowerCAmelCase = pipeline("""audio-classification""" , model=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = np.ones((80_00,) )
__lowerCAmelCase = audio_classifier(SCREAMING_SNAKE_CASE__ , top_k=4 )
__lowerCAmelCase = [
{"""score""": 0.0_8_4_2, """label""": """no"""},
{"""score""": 0.0_8_3_8, """label""": """up"""},
{"""score""": 0.0_8_3_7, """label""": """go"""},
{"""score""": 0.0_8_3_4, """label""": """right"""},
]
__lowerCAmelCase = [
{"""score""": 0.0_8_4_5, """label""": """stop"""},
{"""score""": 0.0_8_4_4, """label""": """on"""},
{"""score""": 0.0_8_4_1, """label""": """right"""},
{"""score""": 0.0_8_3_4, """label""": """left"""},
]
self.assertIn(nested_simplify(SCREAMING_SNAKE_CASE__ , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
__lowerCAmelCase = {"""array""": np.ones((80_00,) ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
__lowerCAmelCase = audio_classifier(SCREAMING_SNAKE_CASE__ , top_k=4 )
self.assertIn(nested_simplify(SCREAMING_SNAKE_CASE__ , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
@require_torch
@slow
def a ( self : List[str] ) -> List[str]:
import datasets
__lowerCAmelCase = """superb/wav2vec2-base-superb-ks"""
__lowerCAmelCase = pipeline("""audio-classification""" , model=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = datasets.load_dataset("""anton-l/superb_dummy""" , """ks""" , split="""test""" )
__lowerCAmelCase = np.array(dataset[3]["""speech"""] , dtype=np.floataa )
__lowerCAmelCase = audio_classifier(SCREAMING_SNAKE_CASE__ , top_k=4 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ , decimals=3 ) , [
{"""score""": 0.9_8_1, """label""": """go"""},
{"""score""": 0.0_0_7, """label""": """up"""},
{"""score""": 0.0_0_6, """label""": """_unknown_"""},
{"""score""": 0.0_0_1, """label""": """down"""},
] , )
@require_tf
@unittest.skip("""Audio classification is not implemented for TF""" )
def a ( self : int ) -> Optional[int]:
pass
| 330 | '''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
_A : List[str] = logging.get_logger(__name__)
_A : List[Any] = {
'''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}
# fmt: off
_A : str = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
_A : Any = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[str] = """whisper"""
_SCREAMING_SNAKE_CASE : List[Any] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE : Optional[int] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[Any]=5_18_65 , SCREAMING_SNAKE_CASE__ : List[str]=80 , SCREAMING_SNAKE_CASE__ : Tuple=6 , SCREAMING_SNAKE_CASE__ : Optional[Any]=4 , SCREAMING_SNAKE_CASE__ : Optional[int]=6 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=4 , SCREAMING_SNAKE_CASE__ : Optional[Any]=15_36 , SCREAMING_SNAKE_CASE__ : Optional[Any]=15_36 , SCREAMING_SNAKE_CASE__ : List[Any]=0.0 , SCREAMING_SNAKE_CASE__ : List[Any]=0.0 , SCREAMING_SNAKE_CASE__ : List[str]=5_02_57 , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE__ : Optional[int]=2_56 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE__ : Dict=0.0 , SCREAMING_SNAKE_CASE__ : List[Any]=0.0 , SCREAMING_SNAKE_CASE__ : Tuple=0.0_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=False , SCREAMING_SNAKE_CASE__ : Any=15_00 , SCREAMING_SNAKE_CASE__ : Optional[int]=4_48 , SCREAMING_SNAKE_CASE__ : Dict=5_02_56 , SCREAMING_SNAKE_CASE__ : List[str]=5_02_56 , SCREAMING_SNAKE_CASE__ : List[str]=5_02_56 , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : int=[2_20, 5_02_56] , SCREAMING_SNAKE_CASE__ : str=False , SCREAMING_SNAKE_CASE__ : Tuple=2_56 , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : Optional[int]=0.0_5 , SCREAMING_SNAKE_CASE__ : Optional[int]=10 , SCREAMING_SNAKE_CASE__ : int=2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE__ : Any=10 , SCREAMING_SNAKE_CASE__ : str=0 , SCREAMING_SNAKE_CASE__ : Tuple=7 , **SCREAMING_SNAKE_CASE__ : int , ) -> Dict:
__lowerCAmelCase = vocab_size
__lowerCAmelCase = num_mel_bins
__lowerCAmelCase = d_model
__lowerCAmelCase = encoder_layers
__lowerCAmelCase = encoder_attention_heads
__lowerCAmelCase = decoder_layers
__lowerCAmelCase = decoder_attention_heads
__lowerCAmelCase = decoder_ffn_dim
__lowerCAmelCase = encoder_ffn_dim
__lowerCAmelCase = dropout
__lowerCAmelCase = attention_dropout
__lowerCAmelCase = activation_dropout
__lowerCAmelCase = activation_function
__lowerCAmelCase = init_std
__lowerCAmelCase = encoder_layerdrop
__lowerCAmelCase = decoder_layerdrop
__lowerCAmelCase = use_cache
__lowerCAmelCase = encoder_layers
__lowerCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
__lowerCAmelCase = max_source_positions
__lowerCAmelCase = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
__lowerCAmelCase = classifier_proj_size
__lowerCAmelCase = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCAmelCase = apply_spec_augment
__lowerCAmelCase = mask_time_prob
__lowerCAmelCase = mask_time_length
__lowerCAmelCase = mask_time_min_masks
__lowerCAmelCase = mask_feature_prob
__lowerCAmelCase = mask_feature_length
__lowerCAmelCase = mask_feature_min_masks
__lowerCAmelCase = median_filter_width
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ , is_encoder_decoder=SCREAMING_SNAKE_CASE__ , decoder_start_token_id=SCREAMING_SNAKE_CASE__ , suppress_tokens=SCREAMING_SNAKE_CASE__ , begin_suppress_tokens=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def a ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
__lowerCAmelCase = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
__lowerCAmelCase = {0: """batch"""}
else:
__lowerCAmelCase = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__ , direction="""inputs""" )
return common_inputs
def a ( self : List[str] , SCREAMING_SNAKE_CASE__ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , SCREAMING_SNAKE_CASE__ : int = -1 , SCREAMING_SNAKE_CASE__ : int = -1 , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : Optional["TensorType"] = None , SCREAMING_SNAKE_CASE__ : int = 2_20_50 , SCREAMING_SNAKE_CASE__ : float = 5.0 , SCREAMING_SNAKE_CASE__ : int = 2_20 , ) -> Mapping[str, Any]:
__lowerCAmelCase = OrderedDict()
__lowerCAmelCase = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=SCREAMING_SNAKE_CASE__ , framework=SCREAMING_SNAKE_CASE__ , sampling_rate=SCREAMING_SNAKE_CASE__ , time_duration=SCREAMING_SNAKE_CASE__ , frequency=SCREAMING_SNAKE_CASE__ , )
__lowerCAmelCase = encoder_inputs["""input_features"""].shape[2]
__lowerCAmelCase = encoder_sequence_length // 2 if self.use_past else seq_length
__lowerCAmelCase = super().generate_dummy_inputs(
preprocessor.tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = encoder_inputs.pop("""input_features""" )
__lowerCAmelCase = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
__lowerCAmelCase = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def a ( self : Optional[int] ) -> float:
return 1e-3
| 330 | 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class a ( a__ , a__ , a__ , unittest.TestCase ):
snake_case__ = StableDiffusionControlNetImgaImgPipeline
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case__ = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''} )
snake_case__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
torch.manual_seed(0 )
lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
lowerCAmelCase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
lowerCAmelCase = CLIPTextModel(_snake_case )
lowerCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowerCAmelCase = {
'unet': unet,
'controlnet': controlnet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = 2
lowerCAmelCase = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_snake_case , device=torch.device(_snake_case ) , )
lowerCAmelCase = floats_tensor(control_image.shape , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase = Image.fromarray(np.uinta(_snake_case ) ).convert('RGB' ).resize((64, 64) )
lowerCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
'image': image,
'control_image': control_image,
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = StableDiffusionControlNetImgaImgPipeline
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
snake_case__ = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def UpperCamelCase__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
torch.manual_seed(0 )
def init_weights(_snake_case ):
if isinstance(_snake_case , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(_snake_case )
torch.manual_seed(0 )
lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(_snake_case )
torch.manual_seed(0 )
lowerCAmelCase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
lowerCAmelCase = CLIPTextModel(_snake_case )
lowerCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowerCAmelCase = MultiControlNetModel([controlneta, controlneta] )
lowerCAmelCase = {
'unet': unet,
'controlnet': controlnet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def UpperCamelCase__ ( self , _snake_case , _snake_case=0 ):
"""simple docstring"""
if str(_snake_case ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(_snake_case )
else:
lowerCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
lowerCAmelCase = 2
lowerCAmelCase = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_snake_case , device=torch.device(_snake_case ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=_snake_case , device=torch.device(_snake_case ) , ),
]
lowerCAmelCase = floats_tensor(control_image[0].shape , rng=random.Random(_snake_case ) ).to(_snake_case )
lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase = Image.fromarray(np.uinta(_snake_case ) ).convert('RGB' ).resize((64, 64) )
lowerCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
'image': image,
'control_image': control_image,
}
return inputs
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**_snake_case )
pipe.to(_snake_case )
lowerCAmelCase = 10.0
lowerCAmelCase = 4
lowerCAmelCase = self.get_dummy_inputs(_snake_case )
lowerCAmelCase = steps
lowerCAmelCase = scale
lowerCAmelCase = pipe(**_snake_case )[0]
lowerCAmelCase = self.get_dummy_inputs(_snake_case )
lowerCAmelCase = steps
lowerCAmelCase = scale
lowerCAmelCase = pipe(**_snake_case , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
lowerCAmelCase = self.get_dummy_inputs(_snake_case )
lowerCAmelCase = steps
lowerCAmelCase = scale
lowerCAmelCase = pipe(**_snake_case , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
lowerCAmelCase = self.get_dummy_inputs(_snake_case )
lowerCAmelCase = steps
lowerCAmelCase = scale
lowerCAmelCase = pipe(**_snake_case , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(_snake_case )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny' )
lowerCAmelCase = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , safety_checker=_snake_case , controlnet=_snake_case )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_snake_case )
lowerCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCAmelCase = 'evil space-punk bird'
lowerCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png' ).resize((5_12, 5_12) )
lowerCAmelCase = load_image(
'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png' ).resize((5_12, 5_12) )
lowerCAmelCase = pipe(
_snake_case , _snake_case , control_image=_snake_case , generator=_snake_case , output_type='np' , num_inference_steps=50 , strength=0.6 , )
lowerCAmelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
lowerCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy' )
assert np.abs(expected_image - image ).max() < 9E-2
| 4 |
import requests
SCREAMING_SNAKE_CASE :List[str] = 'YOUR API KEY'
def UpperCAmelCase ( a_ , a_ = giphy_api_key ) -> list:
"""simple docstring"""
__A = "+".join(query.split() )
__A = F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
__A = requests.get(a_ ).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print('\n'.join(get_gifs('space ship')))
| 55 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
__lowerCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : List[Any] , __snake_case : CLIPSegForImageSegmentation , __snake_case : CLIPSegProcessor , __snake_case : AutoencoderKL , __snake_case : CLIPTextModel , __snake_case : CLIPTokenizer , __snake_case : UNetaDConditionModel , __snake_case : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , __snake_case : StableDiffusionSafetyChecker , __snake_case : CLIPImageProcessor , ) -> List[str]:
super().__init__()
if hasattr(scheduler.config , """steps_offset""" ) and scheduler.config.steps_offset != 1:
__magic_name__: str = (
F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
"""to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
""" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
""" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
""" file"""
)
deprecate("""steps_offset!=1""" , """1.0.0""" , __snake_case , standard_warn=__snake_case )
__magic_name__: Optional[int] = dict(scheduler.config )
__magic_name__: List[Any] = 1
__magic_name__: Any = FrozenDict(__snake_case )
if hasattr(scheduler.config , """skip_prk_steps""" ) and scheduler.config.skip_prk_steps is False:
__magic_name__: Tuple = (
F'The configuration file of this scheduler: {scheduler} has not set the configuration'
""" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
""" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
""" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
""" Hub, it would be very nice if you could open a Pull request for the"""
""" `scheduler/scheduler_config.json` file"""
)
deprecate("""skip_prk_steps not set""" , """1.0.0""" , __snake_case , standard_warn=__snake_case )
__magic_name__: Union[str, Any] = dict(scheduler.config )
__magic_name__: str = True
__magic_name__: Dict = FrozenDict(__snake_case )
if safety_checker is None:
logger.warning(
F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
""" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
""" results in services or applications open to the public. Both the diffusers team and Hugging Face"""
""" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
""" it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
""" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""" )
self.register_modules(
segmentation_model=__snake_case , segmentation_processor=__snake_case , vae=__snake_case , text_encoder=__snake_case , tokenizer=__snake_case , unet=__snake_case , scheduler=__snake_case , safety_checker=__snake_case , feature_extractor=__snake_case , )
def lowerCamelCase__ ( self : Optional[int] , __snake_case : Optional[Union[str, int]] = "auto" ) -> Optional[int]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__magic_name__: Any = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__snake_case )
def lowerCamelCase__ ( self : Optional[Any] ) -> Union[str, Any]:
self.enable_attention_slicing(__snake_case )
def lowerCamelCase__ ( self : Any ) -> Union[str, Any]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
__magic_name__: List[Any] = torch.device("""cuda""" )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(__snake_case , __snake_case )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCamelCase__ ( self : int ) -> int:
if self.device != torch.device("""meta""" ) or not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__snake_case , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self : Optional[Any] , __snake_case : Union[str, List[str]] , __snake_case : Union[torch.FloatTensor, PIL.Image.Image] , __snake_case : str , __snake_case : int = 5_1_2 , __snake_case : int = 5_1_2 , __snake_case : int = 5_0 , __snake_case : float = 7.5 , __snake_case : Optional[Union[str, List[str]]] = None , __snake_case : Optional[int] = 1 , __snake_case : float = 0.0 , __snake_case : Optional[torch.Generator] = None , __snake_case : Optional[torch.FloatTensor] = None , __snake_case : Optional[str] = "pil" , __snake_case : bool = True , __snake_case : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __snake_case : int = 1 , **__snake_case : Optional[int] , ) -> List[Any]:
__magic_name__: int = self.segmentation_processor(
text=[text] , images=[image] , padding="""max_length""" , return_tensors="""pt""" ).to(self.device )
__magic_name__: Any = self.segmentation_model(**__snake_case )
__magic_name__: Any = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
__magic_name__: List[str] = self.numpy_to_pil(__snake_case )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
__magic_name__: int = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=__snake_case , image=__snake_case , mask_image=__snake_case , height=__snake_case , width=__snake_case , num_inference_steps=__snake_case , guidance_scale=__snake_case , negative_prompt=__snake_case , num_images_per_prompt=__snake_case , eta=__snake_case , generator=__snake_case , latents=__snake_case , output_type=__snake_case , return_dict=__snake_case , callback=__snake_case , callback_steps=__snake_case , )
| 715 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__lowerCamelCase = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
__lowerCamelCase = {
'vocab_file': {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
},
'tokenizer_file': {
'unc-nlp/lxmert-base-uncased': (
'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
),
},
}
__lowerCamelCase = {
'unc-nlp/lxmert-base-uncased': 5_12,
}
__lowerCamelCase = {
'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}
class __A ( SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = LxmertTokenizer
def __init__( self : Union[str, Any] , __snake_case : Optional[Any]=None , __snake_case : Union[str, Any]=None , __snake_case : Tuple=True , __snake_case : Optional[int]="[UNK]" , __snake_case : Tuple="[SEP]" , __snake_case : int="[PAD]" , __snake_case : Optional[Any]="[CLS]" , __snake_case : int="[MASK]" , __snake_case : Union[str, Any]=True , __snake_case : List[Any]=None , **__snake_case : List[str] , ) -> Optional[int]:
super().__init__(
__snake_case , tokenizer_file=__snake_case , do_lower_case=__snake_case , unk_token=__snake_case , sep_token=__snake_case , pad_token=__snake_case , cls_token=__snake_case , mask_token=__snake_case , tokenize_chinese_chars=__snake_case , strip_accents=__snake_case , **__snake_case , )
__magic_name__: List[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , __snake_case ) != do_lower_case
or normalizer_state.get("""strip_accents""" , __snake_case ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , __snake_case ) != tokenize_chinese_chars
):
__magic_name__: List[Any] = getattr(__snake_case , normalizer_state.pop("""type""" ) )
__magic_name__: Dict = do_lower_case
__magic_name__: List[Any] = strip_accents
__magic_name__: List[str] = tokenize_chinese_chars
__magic_name__: Tuple = normalizer_class(**__snake_case )
__magic_name__: Tuple = do_lower_case
def lowerCamelCase__ ( self : List[Any] , __snake_case : str , __snake_case : int=None ) -> List[str]:
__magic_name__: int = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCamelCase__ ( self : int , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]:
__magic_name__: List[str] = [self.sep_token_id]
__magic_name__: List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase__ ( self : Union[str, Any] , __snake_case : str , __snake_case : Optional[str] = None ) -> Tuple[str]:
__magic_name__: List[str] = self._tokenizer.model.save(__snake_case , name=__snake_case )
return tuple(__snake_case )
| 213 | 0 |
from __future__ import annotations
def A ( lowercase__ : list[float] ) -> float:
UpperCamelCase__ :Optional[int] = 0.00
UpperCamelCase__ :Tuple = 0
for resistor in resistors:
if resistor <= 0:
UpperCamelCase__ :List[Any] = f"""Resistor at index {index} has a negative or zero value!"""
raise ValueError(lowercase__ )
first_sum += 1 / float(lowercase__ )
index += 1
return 1 / first_sum
def A ( lowercase__ : list[float] ) -> float:
UpperCamelCase__ :str = 0.00
UpperCamelCase__ :int = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
UpperCamelCase__ :Optional[Any] = f"""Resistor at index {index} has a negative value!"""
raise ValueError(lowercase__ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod() | 45 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
UpperCamelCase_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCamelCase_ = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCamelCase_ = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, float]:
lowercase : int =len([g for position, g in enumerate(__magic_name__ ) if g == main_target[position]] )
return (item, float(__magic_name__ ))
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> tuple[str, str]:
lowercase : Any =random.randint(0 , len(__magic_name__ ) - 1 )
lowercase : Tuple =parent_a[:random_slice] + parent_a[random_slice:]
lowercase : List[str] =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] ) -> str:
lowercase : Union[str, Any] =list(__magic_name__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
lowercase : Dict =random.choice(__magic_name__ )
return "".join(__magic_name__ )
def _lowerCAmelCase ( __magic_name__ : tuple[str, float] , __magic_name__ : list[tuple[str, float]] , __magic_name__ : list[str] , ) -> list[str]:
lowercase : Any =[]
# Generate more children proportionally to the fitness score.
lowercase : Dict =int(parent_a[1] * 100 ) + 1
lowercase : List[str] =10 if child_n >= 10 else child_n
for _ in range(__magic_name__ ):
lowercase : List[str] =population_score[random.randint(0 , __magic_name__ )][0]
lowercase , lowercase : Dict =crossover(parent_a[0] , __magic_name__ )
# Append new string to the population list.
pop.append(mutate(__magic_name__ , __magic_name__ ) )
pop.append(mutate(__magic_name__ , __magic_name__ ) )
return pop
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : list[str] , __magic_name__ : bool = True ) -> tuple[int, int, str]:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
lowercase : List[str] =f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(__magic_name__ )
# Verify that the target contains no genes besides the ones inside genes variable.
lowercase : Optional[int] =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
lowercase : Dict =f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(__magic_name__ )
# Generate random starting population.
lowercase : int =[]
for _ in range(__magic_name__ ):
population.append(''''''.join([random.choice(__magic_name__ ) for i in range(len(__magic_name__ ) )] ) )
# Just some logs to know what the algorithms is doing.
lowercase , lowercase : Optional[int] =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__magic_name__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
lowercase : List[str] =[evaluate(__magic_name__ , __magic_name__ ) for item in population]
# Check if there is a matching evolution.
lowercase : int =sorted(__magic_name__ , key=lambda __magic_name__ : x[1] , reverse=__magic_name__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f'''\nGeneration: {generation}'''
f'''\nTotal Population:{total_population}'''
f'''\nBest score: {population_score[0][1]}'''
f'''\nBest string: {population_score[0][0]}''' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
lowercase : Any =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__magic_name__ )
# Normalize population score to be between 0 and 1.
lowercase : Dict =[
(item, score / len(__magic_name__ )) for item, score in population_score
]
# This is selection
for i in range(__magic_name__ ):
population.extend(select(population_score[int(__magic_name__ )] , __magic_name__ , __magic_name__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__magic_name__ ) > N_POPULATION:
break
if __name__ == "__main__":
UpperCamelCase_ = (
"""This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
)
UpperCamelCase_ = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
)
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 92 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase__ :Union[str, Any] = logging.getLogger(__name__)
def __lowercase (_lowercase, _lowercase ) -> Any:
"""simple docstring"""
__lowerCamelCase : Tuple = np.argmax(_lowercase, axis=1 )
return np.sum(outputs == labels )
def __lowercase (_lowercase ) -> Optional[int]:
"""simple docstring"""
with open(_lowercase, encoding="""utf_8""" ) as f:
__lowerCamelCase : Dict = csv.reader(_lowercase )
__lowerCamelCase : Any = []
next(_lowercase ) # skip the first line
for line in tqdm(_lowercase ):
output.append((""" """.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def __lowercase (_lowercase, _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> Tuple:
"""simple docstring"""
__lowerCamelCase : Dict = []
for dataset in encoded_datasets:
__lowerCamelCase : int = len(_lowercase )
__lowerCamelCase : Any = np.zeros((n_batch, 2, input_len), dtype=np.intaa )
__lowerCamelCase : int = np.zeros((n_batch, 2), dtype=np.intaa )
__lowerCamelCase : int = np.full((n_batch, 2, input_len), fill_value=-100, dtype=np.intaa )
__lowerCamelCase : List[str] = np.zeros((n_batch,), dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_lowercase ):
__lowerCamelCase : List[str] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase : int = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
__lowerCamelCase : Tuple = with_conta
__lowerCamelCase : str = with_conta
__lowerCamelCase : List[Any] = len(_lowercase ) - 1
__lowerCamelCase : Tuple = len(_lowercase ) - 1
__lowerCamelCase : List[str] = with_conta
__lowerCamelCase : List[str] = with_conta
__lowerCamelCase : Dict = mc_label
__lowerCamelCase : Optional[Any] = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_lowercase ) for t in all_inputs ) )
return tensor_datasets
def __lowercase () -> List[Any]:
"""simple docstring"""
__lowerCamelCase : Any = argparse.ArgumentParser()
parser.add_argument("""--model_name""", type=_lowercase, default="""openai-gpt""", help="""pretrained model name""" )
parser.add_argument("""--do_train""", action="""store_true""", help="""Whether to run training.""" )
parser.add_argument("""--do_eval""", action="""store_true""", help="""Whether to run eval on the dev set.""" )
parser.add_argument(
"""--output_dir""", default=_lowercase, type=_lowercase, required=_lowercase, help="""The output directory where the model predictions and checkpoints will be written.""", )
parser.add_argument("""--train_dataset""", type=_lowercase, default="""""" )
parser.add_argument("""--eval_dataset""", type=_lowercase, default="""""" )
parser.add_argument("""--seed""", type=_lowercase, default=42 )
parser.add_argument("""--num_train_epochs""", type=_lowercase, default=3 )
parser.add_argument("""--train_batch_size""", type=_lowercase, default=8 )
parser.add_argument("""--eval_batch_size""", type=_lowercase, default=16 )
parser.add_argument("""--adam_epsilon""", default=1e-8, type=_lowercase, help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""", type=_lowercase, default=1 )
parser.add_argument(
"""--max_steps""", default=-1, type=_lowercase, help=(
"""If > 0: set total number of training steps to perform. Override num_train_epochs."""
), )
parser.add_argument(
"""--gradient_accumulation_steps""", type=_lowercase, default=1, help="""Number of updates steps to accumulate before performing a backward/update pass.""", )
parser.add_argument("""--learning_rate""", type=_lowercase, default=6.25e-5 )
parser.add_argument("""--warmup_steps""", default=0, type=_lowercase, help="""Linear warmup over warmup_steps.""" )
parser.add_argument("""--lr_schedule""", type=_lowercase, default="""warmup_linear""" )
parser.add_argument("""--weight_decay""", type=_lowercase, default=0.01 )
parser.add_argument("""--lm_coef""", type=_lowercase, default=0.9 )
parser.add_argument("""--n_valid""", type=_lowercase, default=374 )
parser.add_argument("""--server_ip""", type=_lowercase, default="""""", help="""Can be used for distant debugging.""" )
parser.add_argument("""--server_port""", type=_lowercase, default="""""", help="""Can be used for distant debugging.""" )
__lowerCamelCase : List[Any] = parser.parse_args()
print(_lowercase )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("""Waiting for debugger attach""" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=_lowercase )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
__lowerCamelCase : List[Any] = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
__lowerCamelCase : int = torch.cuda.device_count()
logger.info("""device: {}, n_gpu {}""".format(_lowercase, _lowercase ) )
if not args.do_train and not args.do_eval:
raise ValueError("""At least one of `do_train` or `do_eval` must be True.""" )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
__lowerCamelCase : Optional[Any] = ["""_start_""", """_delimiter_""", """_classify_"""]
__lowerCamelCase : str = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_lowercase )
__lowerCamelCase : Tuple = tokenizer.convert_tokens_to_ids(_lowercase )
__lowerCamelCase : Optional[Any] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_lowercase ) )
model.to(_lowercase )
# Load and encode the datasets
def tokenize_and_encode(_lowercase ):
if isinstance(_lowercase, _lowercase ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_lowercase ) )
elif isinstance(_lowercase, _lowercase ):
return obj
return [tokenize_and_encode(_lowercase ) for o in obj]
logger.info("""Encoding dataset...""" )
__lowerCamelCase : Any = load_rocstories_dataset(args.train_dataset )
__lowerCamelCase : Union[str, Any] = load_rocstories_dataset(args.eval_dataset )
__lowerCamelCase : Union[str, Any] = (train_dataset, eval_dataset)
__lowerCamelCase : List[str] = tokenize_and_encode(_lowercase )
# Compute the max input length for the Transformer
__lowerCamelCase : Optional[Any] = model.config.n_positions // 2 - 2
__lowerCamelCase : int = max(
len(story[:max_length] ) + max(len(conta[:max_length] ), len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
__lowerCamelCase : Dict = min(_lowercase, model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
__lowerCamelCase : Optional[int] = pre_process_datasets(_lowercase, _lowercase, _lowercase, *_lowercase )
__lowerCamelCase : List[Any] = tensor_datasets[0], tensor_datasets[1]
__lowerCamelCase : List[str] = TensorDataset(*_lowercase )
__lowerCamelCase : Union[str, Any] = RandomSampler(_lowercase )
__lowerCamelCase : List[str] = DataLoader(_lowercase, sampler=_lowercase, batch_size=args.train_batch_size )
__lowerCamelCase : List[Any] = TensorDataset(*_lowercase )
__lowerCamelCase : Union[str, Any] = SequentialSampler(_lowercase )
__lowerCamelCase : Union[str, Any] = DataLoader(_lowercase, sampler=_lowercase, batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
__lowerCamelCase : Union[str, Any] = args.max_steps
__lowerCamelCase : Tuple = args.max_steps // (len(_lowercase ) // args.gradient_accumulation_steps) + 1
else:
__lowerCamelCase : Optional[int] = len(_lowercase ) // args.gradient_accumulation_steps * args.num_train_epochs
__lowerCamelCase : Tuple = list(model.named_parameters() )
__lowerCamelCase : Optional[int] = ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
__lowerCamelCase : int = [
{
"""params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"""weight_decay""": args.weight_decay,
},
{"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], """weight_decay""": 0.0},
]
__lowerCamelCase : Union[str, Any] = AdamW(_lowercase, lr=args.learning_rate, eps=args.adam_epsilon )
__lowerCamelCase : Any = get_linear_schedule_with_warmup(
_lowercase, num_warmup_steps=args.warmup_steps, num_training_steps=_lowercase )
if args.do_train:
__lowerCamelCase : Optional[int] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ), desc="""Epoch""" ):
__lowerCamelCase : Tuple = 0
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : Any = tqdm(_lowercase, desc="""Training""" )
for step, batch in enumerate(_lowercase ):
__lowerCamelCase : Dict = tuple(t.to(_lowercase ) for t in batch )
__lowerCamelCase : Union[str, Any] = batch
__lowerCamelCase : Union[str, Any] = model(_lowercase, mc_token_ids=_lowercase, lm_labels=_lowercase, mc_labels=_lowercase )
__lowerCamelCase : Optional[Any] = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
__lowerCamelCase : Dict = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
__lowerCamelCase : int = """Training loss: {:.2e} lr: {:.2e}""".format(_lowercase, scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
__lowerCamelCase : int = model.module if hasattr(_lowercase, """module""" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
__lowerCamelCase : str = os.path.join(args.output_dir, _lowercase )
__lowerCamelCase : List[Any] = os.path.join(args.output_dir, _lowercase )
torch.save(model_to_save.state_dict(), _lowercase )
model_to_save.config.to_json_file(_lowercase )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
__lowerCamelCase : Any = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
__lowerCamelCase : int = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_lowercase )
if args.do_eval:
model.eval()
__lowerCamelCase : Tuple = 0, 0
__lowerCamelCase : int = 0, 0
for batch in tqdm(_lowercase, desc="""Evaluating""" ):
__lowerCamelCase : List[str] = tuple(t.to(_lowercase ) for t in batch )
__lowerCamelCase : int = batch
with torch.no_grad():
__lowerCamelCase : Union[str, Any] = model(
_lowercase, mc_token_ids=_lowercase, lm_labels=_lowercase, mc_labels=_lowercase )
__lowerCamelCase : str = mc_logits.detach().cpu().numpy()
__lowerCamelCase : str = mc_labels.to("""cpu""" ).numpy()
__lowerCamelCase : Optional[int] = accuracy(_lowercase, _lowercase )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
__lowerCamelCase : List[Any] = eval_loss / nb_eval_steps
__lowerCamelCase : str = eval_accuracy / nb_eval_examples
__lowerCamelCase : List[Any] = tr_loss / nb_tr_steps if args.do_train else None
__lowerCamelCase : str = {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}
__lowerCamelCase : Optional[Any] = os.path.join(args.output_dir, """eval_results.txt""" )
with open(_lowercase, """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""", _lowercase, str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 701 |
'''simple docstring'''
from statistics import mean
import numpy as np
def __lowercase (_lowercase, _lowercase, _lowercase, _lowercase ) -> list:
"""simple docstring"""
__lowerCamelCase : str = 0
# Number of processes finished
__lowerCamelCase : List[Any] = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
__lowerCamelCase : int = [0] * no_of_process
# List to include calculation results
__lowerCamelCase : Optional[int] = [0] * no_of_process
# Sort by arrival time.
__lowerCamelCase : int = [burst_time[i] for i in np.argsort(_lowercase )]
__lowerCamelCase : Union[str, Any] = [process_name[i] for i in np.argsort(_lowercase )]
arrival_time.sort()
while no_of_process > finished_process_count:
__lowerCamelCase : Tuple = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
__lowerCamelCase : List[str] = arrival_time[i]
__lowerCamelCase : Dict = 0
# Index showing the location of the process being performed
__lowerCamelCase : Optional[Any] = 0
# Saves the current response ratio.
__lowerCamelCase : List[str] = 0
for i in range(0, _lowercase ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
__lowerCamelCase : Optional[int] = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
__lowerCamelCase : List[Any] = temp
__lowerCamelCase : str = i
# Calculate the turn around time
__lowerCamelCase : Optional[Any] = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
__lowerCamelCase : Optional[Any] = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def __lowercase (_lowercase, _lowercase, _lowercase, _lowercase ) -> list:
"""simple docstring"""
__lowerCamelCase : List[Any] = [0] * no_of_process
for i in range(0, _lowercase ):
__lowerCamelCase : Tuple = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
UpperCAmelCase__ :List[Any] = 5
UpperCAmelCase__ :Tuple = ["""A""", """B""", """C""", """D""", """E"""]
UpperCAmelCase__ :Tuple = [1, 2, 3, 4, 5]
UpperCAmelCase__ :List[Any] = [1, 2, 3, 4, 5]
UpperCAmelCase__ :Union[str, Any] = calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
UpperCAmelCase__ :Optional[int] = calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
for i in range(0, no_of_process):
print(
f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
)
print(f'''average waiting time : {mean(waiting_time):.5f}''')
print(f'''average turn around time : {mean(turn_around_time):.5f}''')
| 483 | 0 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def lowerCamelCase_ ( A_ ):
if len(A_ ) != 32:
raise ValueError('''Input must be of length 32''' )
__lowerCamelCase = B''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def lowerCamelCase_ ( A_ ):
if i < 0:
raise ValueError('''Input must be non-negative''' )
__lowerCamelCase = format(A_ , '''08x''' )[-8:]
__lowerCamelCase = B''''''
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8''' )
return little_endian_hex
def lowerCamelCase_ ( A_ ):
__lowerCamelCase = B''''''
for char in message:
bit_string += format(A_ , '''08b''' ).encode('''utf-8''' )
__lowerCamelCase = format(len(A_ ) , '''064b''' ).encode('''utf-8''' )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(A_ ) % 5_12 != 4_48:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def lowerCamelCase_ ( A_ ):
if len(A_ ) % 5_12 != 0:
raise ValueError('''Input must have length that\'s a multiple of 512''' )
for pos in range(0 , len(A_ ) , 5_12 ):
__lowerCamelCase = bit_string[pos : pos + 5_12]
__lowerCamelCase = []
for i in range(0 , 5_12 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def lowerCamelCase_ ( A_ ):
if i < 0:
raise ValueError('''Input must be non-negative''' )
__lowerCamelCase = format(A_ , '''032b''' )
__lowerCamelCase = ''''''
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(A_ , 2 )
def lowerCamelCase_ ( A_ , A_ ):
return (a + b) % 2**32
def lowerCamelCase_ ( A_ , A_ ):
if i < 0:
raise ValueError('''Input must be non-negative''' )
if shift < 0:
raise ValueError('''Shift must be non-negative''' )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def lowerCamelCase_ ( A_ ):
__lowerCamelCase = preprocess(A_ )
__lowerCamelCase = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
__lowerCamelCase = 0X6745_2301
__lowerCamelCase = 0Xefcd_ab89
__lowerCamelCase = 0X98ba_dcfe
__lowerCamelCase = 0X1032_5476
__lowerCamelCase = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(A_ ):
__lowerCamelCase = aa
__lowerCamelCase = ba
__lowerCamelCase = ca
__lowerCamelCase = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
__lowerCamelCase = d ^ (b & (c ^ d))
__lowerCamelCase = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
__lowerCamelCase = c ^ (d & (b ^ c))
__lowerCamelCase = (5 * i + 1) % 16
elif i <= 47:
__lowerCamelCase = b ^ c ^ d
__lowerCamelCase = (3 * i + 5) % 16
else:
__lowerCamelCase = c ^ (b | not_aa(A_ ))
__lowerCamelCase = (7 * i) % 16
__lowerCamelCase = (f + a + added_consts[i] + block_words[g]) % 2**32
__lowerCamelCase = d
__lowerCamelCase = c
__lowerCamelCase = b
__lowerCamelCase = sum_aa(A_ , left_rotate_aa(A_ , shift_amounts[i] ) )
# Add hashed chunk to running total
__lowerCamelCase = sum_aa(A_ , A_ )
__lowerCamelCase = sum_aa(A_ , A_ )
__lowerCamelCase = sum_aa(A_ , A_ )
__lowerCamelCase = sum_aa(A_ , A_ )
__lowerCamelCase = reformat_hex(A_ ) + reformat_hex(A_ ) + reformat_hex(A_ ) + reformat_hex(A_ )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 316 |
'''simple docstring'''
from pathlib import Path
import fire
def lowerCamelCase_ ( A_ , A_ , A_ ):
__lowerCamelCase = Path(A_ )
__lowerCamelCase = Path(A_ )
dest_dir.mkdir(exist_ok=A_ )
for path in src_dir.iterdir():
__lowerCamelCase = [x.rstrip() for x in list(path.open().readlines() )][:n]
__lowerCamelCase = dest_dir.joinpath(path.name )
print(A_ )
dest_path.open('''w''' ).write('''\n'''.join(A_ ) )
if __name__ == "__main__":
fire.Fire(minify)
| 316 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__A = TypeVar("""KEY""")
__A = TypeVar("""VAL""")
@dataclass(frozen=__a , slots=__a )
class _lowerCAmelCase ( Generic[KEY, VAL] ):
"""simple docstring"""
__magic_name__ :KEY
__magic_name__ :VAL
class _lowerCAmelCase ( _Item ):
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
super().__init__(lowerCAmelCase_ , lowerCAmelCase_ )
def __bool__( self ):
'''simple docstring'''
return False
__A = _DeletedItem()
class _lowerCAmelCase ( MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase = 8 , __UpperCAmelCase = 0.75 ):
'''simple docstring'''
lowerCAmelCase__ :Any = initial_block_size
lowerCAmelCase__ :List[Any] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
lowerCAmelCase__ :str = capacity_factor
lowerCAmelCase__ :int = 0
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
return hash(lowerCAmelCase_ ) % len(self._buckets )
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = self._buckets[ind]
if not stored:
lowerCAmelCase__ :List[str] = _Item(lowerCAmelCase_ , lowerCAmelCase_ )
self._len += 1
return True
elif stored.key == key:
lowerCAmelCase__ :Any = _Item(lowerCAmelCase_ , lowerCAmelCase_ )
return True
else:
return False
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase_ )
def snake_case ( self ):
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
lowerCAmelCase__ :Optional[Any] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = self._buckets
lowerCAmelCase__ :List[Any] = [None] * new_size
lowerCAmelCase__ :Dict = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def snake_case ( self ):
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def snake_case ( self ):
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :str = self._get_bucket_index(lowerCAmelCase_ )
for _ in range(len(self._buckets ) ):
yield ind
lowerCAmelCase__ :str = self._get_next_ind(lowerCAmelCase_ )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase_ ):
if self._try_set(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
break
def __setitem__( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase_ , lowerCAmelCase_ )
def __delitem__( self , __UpperCAmelCase ):
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase_ ):
lowerCAmelCase__ :List[Any] = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase_ )
if item is _deleted:
continue
if item.key == key:
lowerCAmelCase__ :List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , __UpperCAmelCase ):
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase_ ):
lowerCAmelCase__ :Any = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase_ )
def __len__( self ):
'''simple docstring'''
return self._len
def __iter__( self ):
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self ):
'''simple docstring'''
lowerCAmelCase__ :str = ' ,'.join(
F"{item.key}: {item.val}" for item in self._buckets if item )
return F"HashMap({val_string})"
| 719 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__A = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["""GPTNeoXTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"""GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXForCausalLM""",
"""GPTNeoXForQuestionAnswering""",
"""GPTNeoXForSequenceClassification""",
"""GPTNeoXForTokenClassification""",
"""GPTNeoXLayer""",
"""GPTNeoXModel""",
"""GPTNeoXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 560 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(__a )
class UpperCamelCase__ ( __a ):
def __init__( self : List[Any] , **lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
super().__init__(**A__ )
if self.framework != "pt":
raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )
# No specific FOR_XXX available yet
def __call__( self : List[str] , lowerCamelCase : Dict , **lowerCamelCase : int ):
'''simple docstring'''
return super().__call__(A__ , **A__ )
def __a ( self : Optional[Any] , **lowerCamelCase : List[str] ):
'''simple docstring'''
a__ = {}
if "candidate_labels" in kwargs:
a__ = kwargs["candidate_labels"]
if "hypothesis_template" in kwargs:
a__ = kwargs["hypothesis_template"]
return preprocess_params, {}, {}
def __a ( self : List[Any] , lowerCamelCase : List[Any] , lowerCamelCase : Dict=None , lowerCamelCase : Any="This is a sound of {}." ):
'''simple docstring'''
if isinstance(A__ , A__ ):
if audio.startswith("http://" ) or audio.startswith("https://" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
a__ = requests.get(A__ ).content
else:
with open(A__ , "rb" ) as f:
a__ = f.read()
if isinstance(A__ , A__ ):
a__ = ffmpeg_read(A__ , self.feature_extractor.sampling_rate )
if not isinstance(A__ , np.ndarray ):
raise ValueError("We expect a numpy ndarray as input" )
if len(audio.shape ) != 1:
raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline" )
a__ = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="pt" )
a__ = candidate_labels
a__ = [hypothesis_template.format(A__ ) for x in candidate_labels]
a__ = self.tokenizer(A__ , return_tensors=self.framework , padding=A__ )
a__ = [text_inputs]
return inputs
def __a ( self : int , lowerCamelCase : List[str] ):
'''simple docstring'''
a__ = model_inputs.pop("candidate_labels" )
a__ = model_inputs.pop("text_inputs" )
if isinstance(text_inputs[0] , A__ ):
a__ = text_inputs[0]
else:
# Batching case.
a__ = text_inputs[0][0]
a__ = self.model(**A__ , **A__ )
a__ = {
"candidate_labels": candidate_labels,
"logits": outputs.logits_per_audio,
}
return model_outputs
def __a ( self : Tuple , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
a__ = model_outputs.pop("candidate_labels" )
a__ = model_outputs["logits"][0]
if self.framework == "pt":
a__ = logits.softmax(dim=0 )
a__ = probs.tolist()
else:
raise ValueError("`tf` framework not supported." )
a__ = [
{"score": score, "label": candidate_label}
for score, candidate_label in sorted(zip(A__ , A__ ) , key=lambda lowerCamelCase : -x[0] )
]
return result
| 489 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
_lowercase = 100
_lowercase = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_lowercase = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def __UpperCamelCase ( a : int ) ->set[int]:
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
snake_case = set()
snake_case = 42
snake_case = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def __UpperCamelCase ( a : int = 5000 ) ->int | None:
for number_to_partition in range(1 , a ):
if len(partition(a ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 342 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoCBertForCausalLM",
"RoCBertForMaskedLM",
"RoCBertForMultipleChoice",
"RoCBertForPreTraining",
"RoCBertForQuestionAnswering",
"RoCBertForSequenceClassification",
"RoCBertForTokenClassification",
"RoCBertLayer",
"RoCBertModel",
"RoCBertPreTrainedModel",
"load_tf_weights_in_roc_bert",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 713 | """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__ = logging.getLogger(__name__)
@dataclass
class a :
_snake_case : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_snake_case : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_snake_case : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_snake_case : Optional[str] = field(
default=lowerCAmelCase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
_snake_case : bool = field(default=lowerCAmelCase_ , metadata={'help': 'Whether tp freeze the encoder.'} )
_snake_case : bool = field(default=lowerCAmelCase_ , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class a :
_snake_case : str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
_snake_case : Optional[str] = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
_snake_case : Optional[int] = field(
default=10_24 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_snake_case : Optional[int] = field(
default=1_28 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_snake_case : Optional[int] = field(
default=1_42 , 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``.'
)
} , )
_snake_case : Optional[int] = field(
default=1_42 , 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.'
)
} , )
_snake_case : Optional[int] = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
_snake_case : Optional[int] = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
_snake_case : Optional[int] = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
_snake_case : Optional[str] = field(default=lowerCAmelCase_ , metadata={'help': 'Source language id for translation.'} )
_snake_case : Optional[str] = field(default=lowerCAmelCase_ , metadata={'help': 'Target language id for translation.'} )
_snake_case : Optional[int] = field(default=lowerCAmelCase_ , metadata={'help': '# num_beams to use for evaluation.'} )
_snake_case : bool = field(
default=lowerCAmelCase_ , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ):
"""simple docstring"""
logger.info(f'''***** {split} metrics *****''' )
for key in sorted(metrics.keys() ):
logger.info(f''' {key} = {metrics[key]}''' )
save_json(lowercase ,os.path.join(lowercase ,f'''{split}_results.json''' ) )
def __UpperCAmelCase ( ):
"""simple docstring"""
# 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.
_UpperCAmelCase = 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.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
check_output_dir(lowercase )
# 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""" ,lowercase )
# 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.
_UpperCAmelCase = 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 ,)
_UpperCAmelCase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(lowercase ,lowercase ,lowercase ):
assert hasattr(lowercase ,lowercase ), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
setattr(lowercase ,lowercase ,getattr(lowercase ,lowercase ) )
_UpperCAmelCase = 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 ,)
_UpperCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path ,from_tf=""".ckpt""" in model_args.model_name_or_path ,config=lowercase ,cache_dir=model_args.cache_dir ,)
# use task specific params
use_task_specific_params(lowercase ,data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_UpperCAmelCase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowercase ,(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(lowercase ,lowercase ):
_UpperCAmelCase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowercase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_UpperCAmelCase = SeqaSeqDataset
# Get datasets
_UpperCAmelCase = (
dataset_class(
lowercase ,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
)
_UpperCAmelCase = (
dataset_class(
lowercase ,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
)
_UpperCAmelCase = (
dataset_class(
lowercase ,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
_UpperCAmelCase = (
build_compute_metrics_fn(data_args.task ,lowercase ) if training_args.predict_with_generate else None
)
_UpperCAmelCase = SeqaSeqTrainer(
model=lowercase ,args=lowercase ,data_args=lowercase ,train_dataset=lowercase ,eval_dataset=lowercase ,data_collator=SeqaSeqDataCollator(
lowercase ,lowercase ,model.config.decoder_start_token_id ,training_args.tpu_num_cores ) ,compute_metrics=lowercase ,tokenizer=lowercase ,)
_UpperCAmelCase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
_UpperCAmelCase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" ,lowercase ,training_args.output_dir )
all_metrics.update(lowercase )
# 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 ***""" )
_UpperCAmelCase = trainer.evaluate(metric_key_prefix="""val""" )
_UpperCAmelCase = data_args.n_val
_UpperCAmelCase = round(metrics["""val_loss"""] ,4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" ,lowercase ,training_args.output_dir )
all_metrics.update(lowercase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
_UpperCAmelCase = trainer.predict(test_dataset=lowercase ,metric_key_prefix="""test""" )
_UpperCAmelCase = test_output.metrics
_UpperCAmelCase = data_args.n_test
if trainer.is_world_process_zero():
_UpperCAmelCase = round(metrics["""test_loss"""] ,4 )
handle_metrics("""test""" ,lowercase ,training_args.output_dir )
all_metrics.update(lowercase )
if training_args.predict_with_generate:
_UpperCAmelCase = tokenizer.batch_decode(
test_output.predictions ,skip_special_tokens=lowercase ,clean_up_tokenization_spaces=lowercase )
_UpperCAmelCase = lmap(str.strip ,lowercase )
write_txt_file(lowercase ,os.path.join(training_args.output_dir ,"""test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(lowercase ,os.path.join(training_args.output_dir ,"""all_results.json""" ) )
return all_metrics
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 275 | 0 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowercase = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
_lowercase = direct_transformers_import(PATH_TO_TRANSFORMERS)
_lowercase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
_lowercase = {
# used to compute the property `self.chunk_length`
'EncodecConfig': ['overlap'],
# used as `self.bert_model = BertModel(config, ...)`
'DPRConfig': True,
# not used in modeling files, but it's an important information
'FSMTConfig': ['langs'],
# used internally in the configuration class file
'GPTNeoConfig': ['attention_types'],
# used internally in the configuration class file
'EsmConfig': ['is_folding_model'],
# used during training (despite we don't have training script for these models yet)
'Mask2FormerConfig': ['ignore_value'],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
'OneFormerConfig': ['ignore_value', 'norm'],
# used during preprocessing and collation, see `collating_graphormer.py`
'GraphormerConfig': ['spatial_pos_max'],
# used internally in the configuration class file
'T5Config': ['feed_forward_proj'],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
# used internally in the configuration class file
'LongT5Config': ['feed_forward_proj'],
# used internally in the configuration class file
'SwitchTransformersConfig': ['feed_forward_proj'],
# having default values other than `1e-5` - we can't fix them without breaking
'BioGptConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'GLPNConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'SegformerConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'CvtConfig': ['layer_norm_eps'],
# having default values other than `1e-5` - we can't fix them without breaking
'PerceiverConfig': ['layer_norm_eps'],
# used internally to calculate the feature size
'InformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate the feature size
'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate the feature size
'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
# used internally to calculate `mlp_dim`
'SamVisionConfig': ['mlp_ratio'],
# For (head) training, but so far not implemented
'ClapAudioConfig': ['num_classes'],
# Not used, but providing useful information to users
'SpeechT5HifiGanConfig': ['sampling_rate'],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
'CLIPSegConfig': True,
'DeformableDetrConfig': True,
'DetaConfig': True,
'DinatConfig': True,
'DonutSwinConfig': True,
'EfficientFormerConfig': True,
'FSMTConfig': True,
'JukeboxConfig': True,
'LayoutLMv2Config': True,
'MaskFormerSwinConfig': True,
'MT5Config': True,
'NatConfig': True,
'OneFormerConfig': True,
'PerceiverConfig': True,
'RagConfig': True,
'SpeechT5Config': True,
'SwinConfig': True,
'Swin2SRConfig': True,
'Swinv2Config': True,
'SwitchTransformersConfig': True,
'TableTransformerConfig': True,
'TapasConfig': True,
'TransfoXLConfig': True,
'UniSpeechConfig': True,
'UniSpeechSatConfig': True,
'WavLMConfig': True,
'WhisperConfig': True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
'JukeboxPriorConfig': True,
# TODO: @Younes (for `is_decoder`)
'Pix2StructTextConfig': True,
}
)
def __UpperCamelCase ( a : Dict , a : Optional[int] , a : int , a : Any ) ->Tuple:
snake_case = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f"""config.{attribute}""" in modeling_source
or f"""getattr(config, \"{attribute}\"""" in modeling_source
or f"""getattr(self.config, \"{attribute}\"""" in modeling_source
):
snake_case = True
# Deal with multi-line cases
elif (
re.search(
Rf"""getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"""" , a , )
is not None
):
snake_case = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
snake_case = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
snake_case = [
'''bos_index''',
'''eos_index''',
'''pad_index''',
'''unk_index''',
'''mask_index''',
'''image_size''',
'''use_cache''',
'''out_features''',
'''out_indices''',
]
snake_case = ['''encoder_no_repeat_ngram_size''']
# Special cases to be allowed
snake_case = True
if not attribute_used:
snake_case = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
snake_case = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
snake_case = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
snake_case = True
elif attribute.endswith('''_token_id''' ):
snake_case = True
# configuration class specific cases
if not case_allowed:
snake_case = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
snake_case = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def __UpperCamelCase ( a : Dict ) ->Dict:
snake_case = dict(inspect.signature(config_class.__init__ ).parameters )
snake_case = [x for x in list(signature.keys() ) if x not in ['''self''', '''kwargs''']]
snake_case = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
snake_case = {}
if len(config_class.attribute_map ) > 0:
snake_case = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
snake_case = inspect.getsourcefile(a )
snake_case = os.path.dirname(a )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
snake_case = [os.path.join(a , a ) for fn in os.listdir(a ) if fn.startswith('''modeling_''' )]
# Get the source code strings
snake_case = []
for path in modeling_paths:
if os.path.isfile(a ):
with open(a ) as fp:
modeling_sources.append(fp.read() )
snake_case = []
for config_param, default_value in zip(a , a ):
# `attributes` here is all the variant names for `config_param`
snake_case = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(a , a , a , a ):
unused_attributes.append(attributes[0] )
return sorted(a )
def __UpperCamelCase ( ) ->Any:
snake_case = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
snake_case = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda a : inspect.isclass(a )
and issubclass(a , a )
and inspect.getmodule(a ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
snake_case = check_config_attributes_being_used(a )
if len(a ) > 0:
snake_case = unused_attributes
if len(a ) > 0:
snake_case = '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
for name, attributes in configs_with_unused_attributes.items():
error += f"""{name}: {attributes}\n"""
raise ValueError(a )
if __name__ == "__main__":
check_config_attributes()
| 342 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self ) -> Any:
snake_case = '''ylacombe/bark-small'''
snake_case = tempfile.mkdtemp()
snake_case = '''en_speaker_1'''
snake_case = '''This is a test string'''
snake_case = '''speaker_embeddings_path.json'''
snake_case = '''speaker_embeddings'''
def UpperCamelCase ( self , **A__ ) -> int:
return AutoTokenizer.from_pretrained(self.checkpoint , **A__ )
def UpperCamelCase ( self ) -> Tuple:
shutil.rmtree(self.tmpdirname )
def UpperCamelCase ( self ) -> List[Any]:
snake_case = self.get_tokenizer()
snake_case = BarkProcessor(tokenizer=A__ )
processor.save_pretrained(self.tmpdirname )
snake_case = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def UpperCamelCase ( self ) -> Tuple:
snake_case = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
snake_case = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
snake_case = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='''(BOS)''' , eos_token='''(EOS)''' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def UpperCamelCase ( self ) -> List[Any]:
snake_case = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
snake_case = 35
snake_case = 2
snake_case = 8
snake_case = {
'''semantic_prompt''': np.ones(A__ ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
snake_case = processor(text=self.input_string , voice_preset=A__ )
snake_case = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(A__ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
snake_case = os.path.join(self.tmpdirname , '''file.npz''' )
np.savez(A__ , **A__ )
snake_case = processor(text=self.input_string , voice_preset=A__ )
snake_case = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(A__ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
snake_case = processor(text=self.input_string , voice_preset=self.voice_preset )
def UpperCamelCase ( self ) -> int:
snake_case = self.get_tokenizer()
snake_case = BarkProcessor(tokenizer=A__ )
snake_case = processor(text=self.input_string )
snake_case = tokenizer(
self.input_string , padding='''max_length''' , max_length=2_56 , add_special_tokens=A__ , return_attention_mask=A__ , return_token_type_ids=A__ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 342 | 1 |
"""simple docstring"""
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : Tuple = (CMStochasticIterativeScheduler,)
snake_case__ : str = 10
def UpperCAmelCase_ ( self : Any , **UpperCAmelCase__ : List[str] ) -> List[str]:
__SCREAMING_SNAKE_CASE = {
"num_train_timesteps": 2_0_1,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
config.update(**UpperCAmelCase__ )
return config
def UpperCAmelCase_ ( self : List[str] ) -> str:
__SCREAMING_SNAKE_CASE = 1_0
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0](**UpperCAmelCase__ )
scheduler.set_timesteps(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = scheduler.timesteps[0]
__SCREAMING_SNAKE_CASE = scheduler.timesteps[1]
__SCREAMING_SNAKE_CASE = self.dummy_sample
__SCREAMING_SNAKE_CASE = 0.1 * sample
__SCREAMING_SNAKE_CASE = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ).prev_sample
__SCREAMING_SNAKE_CASE = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCAmelCase_ ( self : Dict ) -> Optional[Any]:
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[str]:
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ) -> Any:
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = 1
scheduler.set_timesteps(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = scheduler.timesteps
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(UpperCAmelCase__ ):
# 1. scale model input
__SCREAMING_SNAKE_CASE = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
# 2. predict noise residual
__SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , UpperCAmelCase__ )
# 3. predict previous sample x_t-1
__SCREAMING_SNAKE_CASE = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , generator=UpperCAmelCase__ ).prev_sample
__SCREAMING_SNAKE_CASE = pred_prev_sample
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(UpperCAmelCase__ ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(UpperCAmelCase__ ) )
assert abs(result_sum.item() - 192.7_614 ) < 1E-2
assert abs(result_mean.item() - 0.2_510 ) < 1E-3
def UpperCAmelCase_ ( self : Any ) -> Tuple:
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [1_0_6, 0]
scheduler.set_timesteps(timesteps=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = scheduler.timesteps
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
__SCREAMING_SNAKE_CASE = scheduler.scale_model_input(UpperCAmelCase__ , UpperCAmelCase__ )
# 2. predict noise residual
__SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , UpperCAmelCase__ )
# 3. predict previous sample x_t-1
__SCREAMING_SNAKE_CASE = scheduler.step(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , generator=UpperCAmelCase__ ).prev_sample
__SCREAMING_SNAKE_CASE = pred_prev_sample
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(UpperCAmelCase__ ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(UpperCAmelCase__ ) )
assert abs(result_sum.item() - 347.6_357 ) < 1E-2
assert abs(result_mean.item() - 0.4_527 ) < 1E-3
def UpperCAmelCase_ ( self : Tuple ) -> int:
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [3_9, 3_0, 1_2, 1_5, 0]
with self.assertRaises(UpperCAmelCase__ , msg="`timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [3_9, 3_0, 1_2, 1, 0]
__SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
with self.assertRaises(UpperCAmelCase__ , msg="Can only pass one of `num_inference_steps` or `timesteps`." ):
scheduler.set_timesteps(num_inference_steps=UpperCAmelCase__ , timesteps=UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Any ) -> Dict:
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCAmelCase__ , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=UpperCAmelCase__ )
| 553 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
a__ : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
def __init__( self : str , *UpperCAmelCase__ : List[Any] , **UpperCAmelCase__ : Tuple ) -> None:
warnings.warn(
"The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use VideoMAEImageProcessor instead." , UpperCAmelCase__ , )
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ )
| 553 | 1 |
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
lowerCAmelCase_ = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class A (__UpperCAmelCase ,unittest.TestCase ):
_SCREAMING_SNAKE_CASE = XLNetTokenizer
_SCREAMING_SNAKE_CASE = XLNetTokenizerFast
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
def __a ( self ) -> Tuple:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_snake_case : Optional[int] = XLNetTokenizer(lowercase_ , keep_accents=lowercase_ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def __a ( self ) -> Optional[int]:
'''simple docstring'''
_snake_case : Union[str, Any] = '''<s>'''
_snake_case : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def __a ( self ) -> Optional[Any]:
'''simple docstring'''
_snake_case : Tuple = 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(lowercase_ ) , 1006 )
def __a ( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def __a ( self ) -> int:
'''simple docstring'''
_snake_case : Optional[int] = XLNetTokenizer(lowercase_ , keep_accents=lowercase_ )
_snake_case : Tuple = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowercase_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , [285, 46, 10, 170, 382] )
_snake_case : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowercase_ , [
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''',
'''.''',
] , )
_snake_case : Optional[int] = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(lowercase_ , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
_snake_case : Tuple = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [
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 __a ( self ) -> Union[str, Any]:
'''simple docstring'''
_snake_case : Optional[int] = XLNetTokenizer(lowercase_ , do_lower_case=lowercase_ )
_snake_case : Optional[int] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowercase_ , [
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 __a ( self ) -> str:
'''simple docstring'''
_snake_case : Union[str, Any] = XLNetTokenizer(lowercase_ , do_lower_case=lowercase_ )
_snake_case : List[str] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowercase_ , [
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 __a ( self ) -> Union[str, Any]:
'''simple docstring'''
_snake_case : List[Any] = XLNetTokenizer.from_pretrained('''xlnet-base-cased''' )
_snake_case : int = tokenizer.encode('''sequence builders''' , add_special_tokens=lowercase_ )
_snake_case : int = tokenizer.encode('''multi-sequence build''' , add_special_tokens=lowercase_ )
_snake_case : Tuple = tokenizer.build_inputs_with_special_tokens(lowercase_ )
_snake_case : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def __a ( self ) -> Any:
'''simple docstring'''
_snake_case : Any = {'''input_ids''': [[17, 2_1442, 270, 17, 10, 1_4645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 2_2018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 1_4431, 13, 5500, 11, 1176, 580, 13, 1_6819, 4797, 23, 17, 10, 1_7135, 658, 19, 457, 7932, 13, 184, 19, 3154, 1_7135, 6468, 19, 1404, 1_2269, 19, 4229, 5356, 1_6264, 46, 19, 17, 2_0545, 1_0395, 9, 9, 9, 11, 28, 6421, 9531, 2_0729, 17, 10, 353, 1_7022, 11, 21, 6421, 9531, 1_6949, 17, 10, 1_1509, 753, 11, 33, 95, 2421, 7385, 956, 1_4431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 2_4738, 19, 1_3203, 658, 218, 787, 21, 430, 1_8482, 847, 2637, 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, 322, 2_2178, 27, 1064, 22, 956, 13, 1_1101, 1429, 5854, 2_4313, 1_8953, 40, 422, 2_4366, 68, 1758, 37, 1_0483, 1_4257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 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, 2049, 3442, 17, 1_3894, 3380, 23, 95, 18, 1_7634, 2288, 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=lowercase_ , model_name='''xlnet-base-cased''' , revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''' , )
| 326 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors
@require_tokenizers
class A (__UpperCAmelCase ,unittest.TestCase ):
_SCREAMING_SNAKE_CASE = MvpTokenizer
_SCREAMING_SNAKE_CASE = MvpTokenizerFast
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = filter_roberta_detectors
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
_snake_case : Union[str, Any] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
_snake_case : Tuple = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
_snake_case : List[str] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_snake_case : str = {'''unk_token''': '''<unk>'''}
_snake_case : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_snake_case : Optional[Any] = 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(lowercase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase_ ) )
def __a ( self , **lowercase_ ) -> List[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ )
def __a ( self , **lowercase_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ )
def __a ( self , lowercase_ ) -> Any:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def __a ( self ) -> int:
'''simple docstring'''
return MvpTokenizer.from_pretrained('''RUCAIBox/mvp''' )
@cached_property
def __a ( self ) -> str:
'''simple docstring'''
return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp''' )
@require_torch
def __a ( self ) -> List[str]:
'''simple docstring'''
_snake_case : int = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
_snake_case : Optional[Any] = [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : Optional[int] = tokenizer(lowercase_ , max_length=len(lowercase_ ) , padding=lowercase_ , return_tensors='''pt''' )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
_snake_case : int = batch.input_ids.tolist()[0]
self.assertListEqual(lowercase_ , lowercase_ )
# Test that special tokens are reset
@require_torch
def __a ( self ) -> Optional[int]:
'''simple docstring'''
_snake_case : Dict = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : str = tokenizer(lowercase_ , padding=lowercase_ , return_tensors='''pt''' )
# check if input_ids are returned and no labels
self.assertIn('''input_ids''' , lowercase_ )
self.assertIn('''attention_mask''' , lowercase_ )
self.assertNotIn('''labels''' , lowercase_ )
self.assertNotIn('''decoder_attention_mask''' , lowercase_ )
@require_torch
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
_snake_case : Tuple = [
'''Summary of the text.''',
'''Another summary.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : List[str] = tokenizer(text_target=lowercase_ , max_length=32 , padding='''max_length''' , return_tensors='''pt''' )
self.assertEqual(32 , targets['''input_ids'''].shape[1] )
@require_torch
def __a ( self ) -> Tuple:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : Union[str, Any] = tokenizer(
['''I am a small frog''' * 1024, '''I am a small frog'''] , padding=lowercase_ , truncation=lowercase_ , return_tensors='''pt''' )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual(batch.input_ids.shape , (2, 1024) )
@require_torch
def __a ( self ) -> int:
'''simple docstring'''
_snake_case : Dict = ['''A long paragraph for summarization.''']
_snake_case : List[str] = [
'''Summary of the text.''',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_snake_case : Dict = tokenizer(lowercase_ , text_target=lowercase_ , return_tensors='''pt''' )
_snake_case : List[Any] = inputs['''input_ids''']
_snake_case : Dict = inputs['''labels''']
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() )
def __a ( self ) -> List[Any]:
'''simple docstring'''
pass
def __a ( self ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_snake_case : Dict = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
_snake_case : str = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
_snake_case : Optional[Any] = '''A, <mask> AllenNLP sentence.'''
_snake_case : Optional[Any] = tokenizer_r.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
_snake_case : str = tokenizer_p.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['''token_type_ids'''] ) , sum(tokens_p['''token_type_ids'''] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ) , sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ) , )
_snake_case : List[str] = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
_snake_case : Dict = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(
lowercase_ , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
lowercase_ , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
| 326 | 1 |
from collections.abc import Callable
import numpy as np
def __a ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
a__ = int(np.ceil((x_end - xa) / step_size ) )
a__ = np.zeros((n + 1,) )
a__ = ya
a__ = xa
for k in range(__UpperCAmelCase ):
a__ = y[k] + step_size * ode_func(__UpperCAmelCase , y[k] )
a__ = y[k] + (
(step_size / 2) * (ode_func(__UpperCAmelCase , y[k] ) + ode_func(x + step_size , __UpperCAmelCase ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 148 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
a_ : Any = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __UpperCamelCase :
"""simple docstring"""
_lowercase : str = PegasusConfig
_lowercase : int = {}
_lowercase : List[Any] = '''gelu'''
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1_3 , SCREAMING_SNAKE_CASE=7 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=9_9 , SCREAMING_SNAKE_CASE=3_2 , SCREAMING_SNAKE_CASE=5 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=3_7 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=2_0 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=1 , SCREAMING_SNAKE_CASE=0 , ) -> int:
a__ = parent
a__ = batch_size
a__ = seq_length
a__ = is_training
a__ = use_labels
a__ = vocab_size
a__ = hidden_size
a__ = num_hidden_layers
a__ = num_attention_heads
a__ = intermediate_size
a__ = hidden_dropout_prob
a__ = attention_probs_dropout_prob
a__ = max_position_embeddings
a__ = eos_token_id
a__ = pad_token_id
a__ = bos_token_id
def _UpperCAmelCase ( self ) -> Dict:
a__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
a__ = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
a__ = np.concatenate([input_ids, eos_tensor] , axis=1 )
a__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
a__ = prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return config, inputs_dict
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[int]:
a__ = 2_0
a__ = model_class_name(SCREAMING_SNAKE_CASE )
a__ = model.encode(inputs_dict['''input_ids'''] )
a__ , a__ = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
a__ = model.init_cache(decoder_input_ids.shape[0] , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
a__ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
a__ = model.decode(
decoder_input_ids[:, :-1] , SCREAMING_SNAKE_CASE , decoder_attention_mask=SCREAMING_SNAKE_CASE , past_key_values=SCREAMING_SNAKE_CASE , decoder_position_ids=SCREAMING_SNAKE_CASE , )
a__ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
a__ = model.decode(
decoder_input_ids[:, -1:] , SCREAMING_SNAKE_CASE , decoder_attention_mask=SCREAMING_SNAKE_CASE , past_key_values=outputs_cache.past_key_values , decoder_position_ids=SCREAMING_SNAKE_CASE , )
a__ = model.decode(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"Max diff is {diff}" )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
a__ = 2_0
a__ = model_class_name(SCREAMING_SNAKE_CASE )
a__ = model.encode(inputs_dict['''input_ids'''] )
a__ , a__ = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
a__ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
a__ = model.init_cache(decoder_input_ids.shape[0] , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
a__ = model.decode(
decoder_input_ids[:, :-1] , SCREAMING_SNAKE_CASE , decoder_attention_mask=SCREAMING_SNAKE_CASE , past_key_values=SCREAMING_SNAKE_CASE , decoder_position_ids=SCREAMING_SNAKE_CASE , )
a__ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
a__ = model.decode(
decoder_input_ids[:, -1:] , SCREAMING_SNAKE_CASE , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=SCREAMING_SNAKE_CASE , decoder_position_ids=SCREAMING_SNAKE_CASE , )
a__ = model.decode(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , decoder_attention_mask=SCREAMING_SNAKE_CASE )
a__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"Max diff is {diff}" )
def __a ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , ):
if attention_mask is None:
a__ = np.not_equal(__UpperCAmelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
a__ = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __UpperCamelCase ( _lowercase , unittest.TestCase ):
"""simple docstring"""
_lowercase : Optional[int] = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
_lowercase : List[Any] = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
_lowercase : int = True
_lowercase : Optional[int] = False
_lowercase : Optional[int] = False
_lowercase : Union[str, Any] = False
def _UpperCAmelCase ( self ) -> Optional[Any]:
a__ = FlaxPegasusModelTester(self )
a__ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self ) -> int:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> str:
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self ) -> List[Any]:
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self ) -> int:
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
a__ = self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
a__ = model_class(SCREAMING_SNAKE_CASE )
@jax.jit
def encode_jitted(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE ):
return model.encode(input_ids=SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE )
with self.subTest('''JIT Enabled''' ):
a__ = encode_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
a__ = encode_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , len(SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
def _UpperCAmelCase ( self ) -> int:
a__ , a__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
a__ = model_class(SCREAMING_SNAKE_CASE )
a__ = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
a__ = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return model.decode(
decoder_input_ids=SCREAMING_SNAKE_CASE , decoder_attention_mask=SCREAMING_SNAKE_CASE , encoder_outputs=SCREAMING_SNAKE_CASE , )
with self.subTest('''JIT Enabled''' ):
a__ = decode_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
a__ = decode_jitted(**SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , len(SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _UpperCAmelCase ( self ) -> List[Any]:
for model_class_name in self.all_model_classes:
a__ = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=SCREAMING_SNAKE_CASE )
a__ = np.ones((1, 1) )
a__ = model(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
@slow
def _UpperCAmelCase ( self ) -> int:
a__ = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''' )
a__ = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''' )
a__ = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
a__ = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
a__ = tokenizer(SCREAMING_SNAKE_CASE , return_tensors='''np''' , truncation=SCREAMING_SNAKE_CASE , max_length=5_1_2 , padding=SCREAMING_SNAKE_CASE )
a__ = model.generate(**SCREAMING_SNAKE_CASE , num_beams=2 ).sequences
a__ = tokenizer.batch_decode(SCREAMING_SNAKE_CASE , skip_special_tokens=SCREAMING_SNAKE_CASE )
assert tgt_text == decoded
| 148 | 1 |
'''simple docstring'''
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:List[str] = [0 for i in range(len(lowerCAmelCase__ ) )]
# initialize interval's left pointer and right pointer
SCREAMING_SNAKE_CASE:int = 0, 0
for i in range(1 , len(lowerCAmelCase__ ) ):
# case when current index is inside the interval
if i <= right_pointer:
SCREAMING_SNAKE_CASE:List[Any] = min(right_pointer - i + 1 , z_result[i - left_pointer] )
SCREAMING_SNAKE_CASE:List[str] = min_edge
while go_next(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
SCREAMING_SNAKE_CASE:str = i, i + z_result[i] - 1
return z_result
def A_ ( snake_case , snake_case , snake_case ):
return i + z_result[i] < len(lowerCAmelCase__ ) and s[z_result[i]] == s[i + z_result[i]]
def A_ ( snake_case , snake_case ):
SCREAMING_SNAKE_CASE:List[Any] = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
SCREAMING_SNAKE_CASE:List[str] = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(lowerCAmelCase__ ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 143 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class __UpperCAmelCase :
def UpperCAmelCase ( self : List[str] ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
a__ : List[str] = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
a__ : int = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
a__ : Any = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
a__ : str = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=a_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
a__ : Optional[int] = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
a__ : List[Any] = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
a__ : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
a__ : Dict = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
a__ : Optional[int] = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=a_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
a__ : Tuple = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
a__ : Optional[Any] = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
'''simple docstring'''
a__ : Dict = self.get_dummy_components()
a__ : Any = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
a__ : Any = self.get_dummy_inputs(a_ )
a__ : Optional[int] = inputs["prompt"]
a__ : List[Any] = inputs["generator"]
a__ : Optional[int] = inputs["num_inference_steps"]
a__ : Any = inputs["output_type"]
if "image" in inputs:
a__ : Any = inputs["image"]
else:
a__ : Dict = None
if "mask_image" in inputs:
a__ : Optional[int] = inputs["mask_image"]
else:
a__ : Any = None
if "original_image" in inputs:
a__ : List[Any] = inputs["original_image"]
else:
a__ : str = None
a__ , a__ : Optional[int] = pipe.encode_prompt(a_ )
# inputs with prompt converted to embeddings
a__ : Union[str, Any] = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
a__ : Dict = image
if mask_image is not None:
a__ : Any = mask_image
if original_image is not None:
a__ : Optional[int] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(a_ , a_ , a_ )
a__ : int = pipe(**a_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(a_ )
a__ : List[str] = self.pipeline_class.from_pretrained(a_ )
pipe_loaded.to(a_ )
pipe_loaded.set_progress_bar_config(disable=a_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(a_ , a_ ) is None , F"`{optional_component}` did not stay set to None after loading." , )
a__ : Union[str, Any] = self.get_dummy_inputs(a_ )
a__ : str = inputs["generator"]
a__ : Dict = inputs["num_inference_steps"]
a__ : Optional[int] = inputs["output_type"]
# inputs with prompt converted to embeddings
a__ : List[Any] = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
a__ : Dict = image
if mask_image is not None:
a__ : Any = mask_image
if original_image is not None:
a__ : Dict = original_image
a__ : Optional[Any] = pipe_loaded(**a_ )[0]
a__ : int = np.abs(to_np(a_ ) - to_np(a_ ) ).max()
self.assertLess(a_ , 1E-4 )
def UpperCAmelCase ( self : int ) -> Any:
'''simple docstring'''
a__ : Dict = self.get_dummy_components()
a__ : Dict = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
a__ : List[str] = self.get_dummy_inputs(a_ )
a__ : Dict = pipe(**a_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(a_ )
a__ : str = self.pipeline_class.from_pretrained(a_ )
pipe_loaded.to(a_ )
pipe_loaded.set_progress_bar_config(disable=a_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
a__ : Optional[int] = self.get_dummy_inputs(a_ )
a__ : Optional[int] = pipe_loaded(**a_ )[0]
a__ : List[Any] = np.abs(to_np(a_ ) - to_np(a_ ) ).max()
self.assertLess(a_ , 1E-4 ) | 642 | 0 |
"""simple docstring"""
import operator as op
def _lowerCAmelCase ( lowerCamelCase__ : Tuple ) -> List[str]:
_SCREAMING_SNAKE_CASE : Optional[int] = []
_SCREAMING_SNAKE_CASE : str = lambda lowerCamelCase__, lowerCamelCase__ : int(x / y ) # noqa: E731 integer division operation
_SCREAMING_SNAKE_CASE : Any = {
"^": op.pow,
"*": op.mul,
"/": div,
"+": op.add,
"-": op.sub,
} # operators & their respective operation
# print table header
print("Symbol".center(8 ), "Action".center(1_2 ), "Stack", sep=" | " )
print("-" * (3_0 + len(lowerCamelCase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowerCamelCase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ), ("push(" + x + ")").ljust(1_2 ), ",".join(lowerCamelCase__ ), sep=" | " )
else:
_SCREAMING_SNAKE_CASE : Dict = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ), ("pop(" + b + ")").ljust(1_2 ), ",".join(lowerCamelCase__ ), sep=" | " )
_SCREAMING_SNAKE_CASE : Any = stack.pop() # pop stack
# output in tabular format
print("".rjust(8 ), ("pop(" + a + ")").ljust(1_2 ), ",".join(lowerCamelCase__ ), sep=" | " )
stack.append(
str(opr[x](int(lowerCamelCase__ ), int(lowerCamelCase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ), ("push(" + a + x + b + ")").ljust(1_2 ), ",".join(lowerCamelCase__ ), sep=" | ", )
return int(stack[0] )
if __name__ == "__main__":
lowercase_ : int = input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
print('''\n\tResult = ''', solve(Postfix))
| 295 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
A__ = ["""image_processor""", """tokenizer"""]
A__ = """AutoImageProcessor"""
A__ = """AutoTokenizer"""
def __init__( self , snake_case__=None , snake_case__=None , **snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : int = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , snake_case__ , )
_SCREAMING_SNAKE_CASE : Any = kwargs.pop("feature_extractor" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(snake_case__ , snake_case__ )
_SCREAMING_SNAKE_CASE : Any = self.image_processor
_SCREAMING_SNAKE_CASE : Any = False
def __call__( self , *snake_case__ , **snake_case__ ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*snake_case__ , **snake_case__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = kwargs.pop("images" , snake_case__ )
_SCREAMING_SNAKE_CASE : Union[str, Any] = kwargs.pop("text" , snake_case__ )
if len(snake_case__ ) > 0:
_SCREAMING_SNAKE_CASE : Optional[Any] = args[0]
_SCREAMING_SNAKE_CASE : List[str] = args[1:]
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processor(snake_case__ , *snake_case__ , **snake_case__ )
if text is not None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer(snake_case__ , **snake_case__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
_SCREAMING_SNAKE_CASE : List[Any] = encodings["input_ids"]
return inputs
def __SCREAMING_SNAKE_CASE ( self , *snake_case__ , **snake_case__ ):
"""simple docstring"""
return self.tokenizer.batch_decode(*snake_case__ , **snake_case__ )
def __SCREAMING_SNAKE_CASE ( self , *snake_case__ , **snake_case__ ):
"""simple docstring"""
return self.tokenizer.decode(*snake_case__ , **snake_case__ )
@contextmanager
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your images inputs, or in a separate call." )
_SCREAMING_SNAKE_CASE : Dict = True
_SCREAMING_SNAKE_CASE : str = self.tokenizer
yield
_SCREAMING_SNAKE_CASE : str = self.image_processor
_SCREAMING_SNAKE_CASE : int = False
def __SCREAMING_SNAKE_CASE ( self , snake_case__ , snake_case__=False , snake_case__=None ):
"""simple docstring"""
if added_vocab is None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer.get_added_vocab()
_SCREAMING_SNAKE_CASE : int = {}
while tokens:
_SCREAMING_SNAKE_CASE : List[Any] = re.search(r"<s_(.*?)>" , snake_case__ , re.IGNORECASE )
if start_token is None:
break
_SCREAMING_SNAKE_CASE : List[Any] = start_token.group(1 )
_SCREAMING_SNAKE_CASE : Any = re.search(rF'''</s_{key}>''' , snake_case__ , re.IGNORECASE )
_SCREAMING_SNAKE_CASE : Tuple = start_token.group()
if end_token is None:
_SCREAMING_SNAKE_CASE : Optional[int] = tokens.replace(snake_case__ , "" )
else:
_SCREAMING_SNAKE_CASE : Any = end_token.group()
_SCREAMING_SNAKE_CASE : Union[str, Any] = re.escape(snake_case__ )
_SCREAMING_SNAKE_CASE : Union[str, Any] = re.escape(snake_case__ )
_SCREAMING_SNAKE_CASE : str = re.search(F'''{start_token_escaped}(.*?){end_token_escaped}''' , snake_case__ , re.IGNORECASE )
if content is not None:
_SCREAMING_SNAKE_CASE : Optional[Any] = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
_SCREAMING_SNAKE_CASE : Dict = self.tokenajson(snake_case__ , is_inner_value=snake_case__ , added_vocab=snake_case__ )
if value:
if len(snake_case__ ) == 1:
_SCREAMING_SNAKE_CASE : Optional[Any] = value[0]
_SCREAMING_SNAKE_CASE : Dict = value
else: # leaf nodes
_SCREAMING_SNAKE_CASE : Dict = []
for leaf in content.split(r"<sep/>" ):
_SCREAMING_SNAKE_CASE : int = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
_SCREAMING_SNAKE_CASE : Optional[Any] = leaf[1:-2] # for categorical special tokens
output[key].append(snake_case__ )
if len(output[key] ) == 1:
_SCREAMING_SNAKE_CASE : Dict = output[key][0]
_SCREAMING_SNAKE_CASE : List[Any] = tokens[tokens.find(snake_case__ ) + len(snake_case__ ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=snake_case__ , added_vocab=snake_case__ )
if len(snake_case__ ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , snake_case__ , )
return self.image_processor_class
@property
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , snake_case__ , )
return self.image_processor
| 295 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"configuration_efficientnet": [
"EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EfficientNetConfig",
"EfficientNetOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["EfficientNetImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"EfficientNetForImageClassification",
"EfficientNetModel",
"EfficientNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 28 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : Dict = (PNDMScheduler,)
lowerCAmelCase__ : Tuple = (("num_inference_steps", 50),)
def _lowerCamelCase ( self : Tuple ,**UpperCamelCase : Any ) -> Dict:
_lowercase : Tuple = {
'num_train_timesteps': 1000,
'beta_start': 0.0_0_0_1,
'beta_end': 0.0_2,
'beta_schedule': 'linear',
}
config.update(**UpperCamelCase )
return config
def _lowerCamelCase ( self : Optional[int] ,UpperCamelCase : Dict=0 ,**UpperCamelCase : List[str] ) -> Any:
_lowercase : Optional[Any] = dict(self.forward_default_kwargs )
_lowercase : Tuple = kwargs.pop('num_inference_steps' ,UpperCamelCase )
_lowercase : Tuple = self.dummy_sample
_lowercase : Optional[int] = 0.1 * sample
_lowercase : Dict = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
_lowercase : str = self.get_scheduler_config(**UpperCamelCase )
_lowercase : List[Any] = scheduler_class(**UpperCamelCase )
scheduler.set_timesteps(UpperCamelCase )
# copy over dummy past residuals
_lowercase : str = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase )
_lowercase : List[str] = scheduler_class.from_pretrained(UpperCamelCase )
new_scheduler.set_timesteps(UpperCamelCase )
# copy over dummy past residuals
_lowercase : Any = dummy_past_residuals[:]
_lowercase : Dict = scheduler.step_prk(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ).prev_sample
_lowercase : Optional[Any] = new_scheduler.step_prk(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
_lowercase : Union[str, Any] = scheduler.step_plms(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ).prev_sample
_lowercase : Optional[Any] = new_scheduler.step_plms(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowerCamelCase ( self : str ) -> List[Any]:
pass
def _lowerCamelCase ( self : Dict ,UpperCamelCase : List[Any]=0 ,**UpperCamelCase : List[Any] ) -> List[Any]:
_lowercase : int = dict(self.forward_default_kwargs )
_lowercase : Any = kwargs.pop('num_inference_steps' ,UpperCamelCase )
_lowercase : List[str] = self.dummy_sample
_lowercase : Dict = 0.1 * sample
_lowercase : Any = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
_lowercase : int = self.get_scheduler_config()
_lowercase : Union[str, Any] = scheduler_class(**UpperCamelCase )
scheduler.set_timesteps(UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_lowercase : int = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase )
_lowercase : Union[str, Any] = scheduler_class.from_pretrained(UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_lowercase : Any = dummy_past_residuals[:]
_lowercase : Dict = scheduler.step_prk(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ).prev_sample
_lowercase : List[Any] = new_scheduler.step_prk(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
_lowercase : int = scheduler.step_plms(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ).prev_sample
_lowercase : Tuple = new_scheduler.step_plms(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,**UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowerCamelCase ( self : Optional[int] ,**UpperCamelCase : Any ) -> List[Any]:
_lowercase : Dict = self.scheduler_classes[0]
_lowercase : Union[str, Any] = self.get_scheduler_config(**UpperCamelCase )
_lowercase : Optional[int] = scheduler_class(**UpperCamelCase )
_lowercase : Dict = 10
_lowercase : str = self.dummy_model()
_lowercase : Tuple = self.dummy_sample_deter
scheduler.set_timesteps(UpperCamelCase )
for i, t in enumerate(scheduler.prk_timesteps ):
_lowercase : Any = model(UpperCamelCase ,UpperCamelCase )
_lowercase : Union[str, Any] = scheduler.step_prk(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
_lowercase : Optional[Any] = model(UpperCamelCase ,UpperCamelCase )
_lowercase : Tuple = scheduler.step_plms(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ).prev_sample
return sample
def _lowerCamelCase ( self : Optional[Any] ) -> List[str]:
_lowercase : Union[str, Any] = dict(self.forward_default_kwargs )
_lowercase : List[str] = kwargs.pop('num_inference_steps' ,UpperCamelCase )
for scheduler_class in self.scheduler_classes:
_lowercase : str = self.get_scheduler_config()
_lowercase : Dict = scheduler_class(**UpperCamelCase )
_lowercase : int = self.dummy_sample
_lowercase : List[str] = 0.1 * sample
if num_inference_steps is not None and hasattr(UpperCamelCase ,'set_timesteps' ):
scheduler.set_timesteps(UpperCamelCase )
elif num_inference_steps is not None and not hasattr(UpperCamelCase ,'set_timesteps' ):
_lowercase : List[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_lowercase : List[str] = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
_lowercase : List[Any] = dummy_past_residuals[:]
_lowercase : List[str] = scheduler.step_prk(UpperCamelCase ,0 ,UpperCamelCase ,**UpperCamelCase ).prev_sample
_lowercase : List[str] = scheduler.step_prk(UpperCamelCase ,1 ,UpperCamelCase ,**UpperCamelCase ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
_lowercase : str = scheduler.step_plms(UpperCamelCase ,0 ,UpperCamelCase ,**UpperCamelCase ).prev_sample
_lowercase : Any = scheduler.step_plms(UpperCamelCase ,1 ,UpperCamelCase ,**UpperCamelCase ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
def _lowerCamelCase ( self : Optional[int] ) -> List[str]:
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=UpperCamelCase )
def _lowerCamelCase ( self : Union[str, Any] ) -> int:
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=UpperCamelCase )
_lowercase : Tuple = self.scheduler_classes[0]
_lowercase : List[str] = self.get_scheduler_config(steps_offset=1 )
_lowercase : Any = scheduler_class(**UpperCamelCase )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps ,torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) ,)
def _lowerCamelCase ( self : Any ) -> Optional[int]:
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1] ,[0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=UpperCamelCase ,beta_end=UpperCamelCase )
def _lowerCamelCase ( self : Any ) -> Union[str, Any]:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=UpperCamelCase )
def _lowerCamelCase ( self : List[Any] ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCamelCase )
def _lowerCamelCase ( self : Optional[Any] ) -> Any:
for t in [1, 5, 10]:
self.check_over_forward(time_step=UpperCamelCase )
def _lowerCamelCase ( self : Dict ) -> Union[str, Any]:
for t, num_inference_steps in zip([1, 5, 10] ,[10, 50, 100] ):
self.check_over_forward(num_inference_steps=UpperCamelCase )
def _lowerCamelCase ( self : Union[str, Any] ) -> Optional[int]:
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
_lowercase : Dict = 27
for scheduler_class in self.scheduler_classes:
_lowercase : List[Any] = self.dummy_sample
_lowercase : List[str] = 0.1 * sample
_lowercase : Union[str, Any] = self.get_scheduler_config()
_lowercase : Any = scheduler_class(**UpperCamelCase )
scheduler.set_timesteps(UpperCamelCase )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
_lowercase : Optional[int] = scheduler.step_prk(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ).prev_sample
def _lowerCamelCase ( self : Dict ) -> Dict:
with self.assertRaises(UpperCamelCase ):
_lowercase : Optional[int] = self.scheduler_classes[0]
_lowercase : str = self.get_scheduler_config()
_lowercase : Tuple = scheduler_class(**UpperCamelCase )
scheduler.step_plms(self.dummy_sample ,1 ,self.dummy_sample ).prev_sample
def _lowerCamelCase ( self : Optional[int] ) -> int:
_lowercase : Any = self.full_loop()
_lowercase : Optional[Any] = torch.sum(torch.abs(UpperCamelCase ) )
_lowercase : Any = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_sum.item() - 1_9_8.1_3_1_8 ) < 1e-2
assert abs(result_mean.item() - 0.2_5_8_0 ) < 1e-3
def _lowerCamelCase ( self : Any ) -> Union[str, Any]:
_lowercase : Tuple = self.full_loop(prediction_type='v_prediction' )
_lowercase : Optional[Any] = torch.sum(torch.abs(UpperCamelCase ) )
_lowercase : int = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_sum.item() - 6_7.3_9_8_6 ) < 1e-2
assert abs(result_mean.item() - 0.0_8_7_8 ) < 1e-3
def _lowerCamelCase ( self : List[Any] ) -> str:
# We specify different beta, so that the first alpha is 0.99
_lowercase : List[Any] = self.full_loop(set_alpha_to_one=UpperCamelCase ,beta_start=0.0_1 )
_lowercase : List[Any] = torch.sum(torch.abs(UpperCamelCase ) )
_lowercase : Any = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_sum.item() - 2_3_0.0_3_9_9 ) < 1e-2
assert abs(result_mean.item() - 0.2_9_9_5 ) < 1e-3
def _lowerCamelCase ( self : Any ) -> Optional[int]:
# We specify different beta, so that the first alpha is 0.99
_lowercase : Union[str, Any] = self.full_loop(set_alpha_to_one=UpperCamelCase ,beta_start=0.0_1 )
_lowercase : List[Any] = torch.sum(torch.abs(UpperCamelCase ) )
_lowercase : List[Any] = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_sum.item() - 1_8_6.9_4_8_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_4_3_4 ) < 1e-3 | 125 | 0 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __SCREAMING_SNAKE_CASE ( __lowercase):
_SCREAMING_SNAKE_CASE : Optional[int] = ComputeEnvironment.AMAZON_SAGEMAKER
_SCREAMING_SNAKE_CASE : Optional[int] = True
_SCREAMING_SNAKE_CASE : Union[str, Any] = '''ml.p3.2xlarge'''
_SCREAMING_SNAKE_CASE : str = '''accelerate_sagemaker_execution_role'''
_SCREAMING_SNAKE_CASE : Optional[int] = '''hf-sm'''
_SCREAMING_SNAKE_CASE : Dict = '''us-east-1'''
_SCREAMING_SNAKE_CASE : Optional[Any] = 1
_SCREAMING_SNAKE_CASE : List[Any] = '''accelerate-sagemaker-1'''
_SCREAMING_SNAKE_CASE : int = '''1.6'''
_SCREAMING_SNAKE_CASE : Tuple = '''4.4'''
_SCREAMING_SNAKE_CASE : List[str] = '''train.py'''
_SCREAMING_SNAKE_CASE : str = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''False''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
_SCREAMING_SNAKE_CASE : Optional[Any] = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''--do_test''',
'''False''',
'''--do_predict''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCamelCase__ ( self ):
"""simple docstring"""
# If no defaults are changed, `to_kwargs` returns an empty dict.
lowerCAmelCase__ = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['model_name_or_path'] , _UpperCamelCase )
assert isinstance(converted_args['do_train'] , _UpperCamelCase )
assert isinstance(converted_args['epochs'] , _UpperCamelCase )
assert isinstance(converted_args['learning_rate'] , _UpperCamelCase )
assert isinstance(converted_args['max_steps'] , _UpperCamelCase )
with pytest.raises(_UpperCamelCase ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 365 |
from typing import Dict, Optional
import numpy as np
import datasets
__snake_case : str = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""
__snake_case : Tuple = """
Args:
predictions (`List[ndarray]`):
List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
references (`List[ndarray]`):
List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
num_labels (`int`):
Number of classes (categories).
ignore_index (`int`):
Index that will be ignored during evaluation.
nan_to_num (`int`, *optional*):
If specified, NaN values will be replaced by the number defined by the user.
label_map (`dict`, *optional*):
If specified, dictionary mapping old label indices to new label indices.
reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
Returns:
`Dict[str, float | ndarray]` comprising various elements:
- *mean_iou* (`float`):
Mean Intersection-over-Union (IoU averaged over all categories).
- *mean_accuracy* (`float`):
Mean accuracy (averaged over all categories).
- *overall_accuracy* (`float`):
Overall accuracy on all images.
- *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
Per category accuracy.
- *per_category_iou* (`ndarray` of shape `(num_labels,)`):
Per category IoU.
Examples:
>>> import numpy as np
>>> mean_iou = datasets.load_metric(\"mean_iou\")
>>> # suppose one has 3 different segmentation maps predicted
>>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
>>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])
>>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
>>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])
>>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
>>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])
>>> predicted = [predicted_1, predicted_2, predicted_3]
>>> ground_truth = [actual_1, actual_2, actual_3]
>>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), 'per_category_accuracy': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}
"""
__snake_case : Any = """\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""
def _UpperCamelCase ( UpperCamelCase_ : str , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : bool , UpperCamelCase_ : Optional[Dict[int, int]] = None , UpperCamelCase_ : bool = False , ) -> List[Any]:
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
lowerCAmelCase__ = new_id
# turn into Numpy arrays
lowerCAmelCase__ = np.array(UpperCamelCase_ )
lowerCAmelCase__ = np.array(UpperCamelCase_ )
if reduce_labels:
lowerCAmelCase__ = 255
lowerCAmelCase__ = label - 1
lowerCAmelCase__ = 255
lowerCAmelCase__ = label != ignore_index
lowerCAmelCase__ = np.not_equal(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase__ = pred_label[mask]
lowerCAmelCase__ = np.array(UpperCamelCase_ )[mask]
lowerCAmelCase__ = pred_label[pred_label == label]
lowerCAmelCase__ = np.histogram(UpperCamelCase_ , bins=UpperCamelCase_ , range=(0, num_labels - 1) )[0]
lowerCAmelCase__ = np.histogram(UpperCamelCase_ , bins=UpperCamelCase_ , range=(0, num_labels - 1) )[0]
lowerCAmelCase__ = np.histogram(UpperCamelCase_ , bins=UpperCamelCase_ , range=(0, num_labels - 1) )[0]
lowerCAmelCase__ = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def _UpperCamelCase ( UpperCamelCase_ : Dict , UpperCamelCase_ : str , UpperCamelCase_ : int , UpperCamelCase_ : bool , UpperCamelCase_ : Optional[Dict[int, int]] = None , UpperCamelCase_ : bool = False , ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase__ = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase__ = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase__ = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(UpperCamelCase_ , UpperCamelCase_ ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = intersect_and_union(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def _UpperCamelCase ( UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : str , UpperCamelCase_ : bool , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[Dict[int, int]] = None , UpperCamelCase_ : bool = False , ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = total_intersect_and_union(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# compute metrics
lowerCAmelCase__ = {}
lowerCAmelCase__ = total_area_intersect.sum() / total_area_label.sum()
lowerCAmelCase__ = total_area_intersect / total_area_union
lowerCAmelCase__ = total_area_intersect / total_area_label
lowerCAmelCase__ = np.nanmean(UpperCamelCase_ )
lowerCAmelCase__ = np.nanmean(UpperCamelCase_ )
lowerCAmelCase__ = all_acc
lowerCAmelCase__ = iou
lowerCAmelCase__ = acc
if nan_to_num is not None:
lowerCAmelCase__ = {metric: np.nan_to_num(UpperCamelCase_ , nan=UpperCamelCase_ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __SCREAMING_SNAKE_CASE ( datasets.Metric):
def UpperCamelCase__ ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
} ) , reference_urls=[
'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
] , )
def UpperCamelCase__ ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = False , ):
"""simple docstring"""
lowerCAmelCase__ = mean_iou(
results=_UpperCamelCase , gt_seg_maps=_UpperCamelCase , num_labels=_UpperCamelCase , ignore_index=_UpperCamelCase , nan_to_num=_UpperCamelCase , label_map=_UpperCamelCase , reduce_labels=_UpperCamelCase , )
return iou_result
| 365 | 1 |
'''simple docstring'''
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
assert column_title.isupper()
__a : str = 0
__a : List[Any] = len(_lowercase ) - 1
__a : List[str] = 0
while index >= 0:
__a : Optional[Any] = (ord(column_title[index] ) - 64) * pow(26 , _lowercase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 597 | '''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 a :
"""simple docstring"""
def __init__( self , snake_case_ , ):
'''simple docstring'''
__UpperCAmelCase: List[Any] = parent
__UpperCAmelCase: Dict = 13
__UpperCAmelCase: Optional[int] = 7
__UpperCAmelCase: List[str] = 30
__UpperCAmelCase: List[Any] = self.seq_length + self.mem_len
__UpperCAmelCase: int = 15
__UpperCAmelCase: Optional[int] = True
__UpperCAmelCase: List[str] = True
__UpperCAmelCase: Union[str, Any] = 99
__UpperCAmelCase: Optional[int] = [10, 50, 80]
__UpperCAmelCase: str = 32
__UpperCAmelCase: Optional[Any] = 32
__UpperCAmelCase: Union[str, Any] = 4
__UpperCAmelCase: int = 8
__UpperCAmelCase: str = 128
__UpperCAmelCase: str = 2
__UpperCAmelCase: Tuple = 2
__UpperCAmelCase: Union[str, Any] = None
__UpperCAmelCase: str = 1
__UpperCAmelCase: Optional[Any] = 0
__UpperCAmelCase: int = 3
__UpperCAmelCase: Dict = self.vocab_size - 1
__UpperCAmelCase: int = 0.0_1
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase: Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase: List[str] = None
if self.use_labels:
__UpperCAmelCase: int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase: 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 lowercase_ ( self ):
'''simple docstring'''
random.seed(self.seed )
tf.random.set_seed(self.seed )
def lowercase_ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
__UpperCAmelCase: Dict = TFTransfoXLModel(snake_case_ )
__UpperCAmelCase, __UpperCAmelCase: List[str] = model(snake_case_ ).to_tuple()
__UpperCAmelCase: Tuple = {"""input_ids""": input_ids_a, """mems""": mems_a}
__UpperCAmelCase, __UpperCAmelCase: Optional[Any] = model(snake_case_ ).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 lowercase_ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
__UpperCAmelCase: str = TFTransfoXLLMHeadModel(snake_case_ )
__UpperCAmelCase, __UpperCAmelCase: Optional[int] = model(snake_case_ ).to_tuple()
__UpperCAmelCase: Optional[Any] = {"""input_ids""": input_ids_a, """labels""": lm_labels}
__UpperCAmelCase, __UpperCAmelCase: Tuple = model(snake_case_ ).to_tuple()
__UpperCAmelCase, __UpperCAmelCase: Dict = model([input_ids_a, mems_a] ).to_tuple()
__UpperCAmelCase: Union[str, Any] = {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}
__UpperCAmelCase, __UpperCAmelCase: List[str] = model(snake_case_ ).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 lowercase_ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
__UpperCAmelCase: Optional[int] = TFTransfoXLForSequenceClassification(snake_case_ )
__UpperCAmelCase: List[Any] = model(snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: Optional[int] = self.prepare_config_and_inputs()
((__UpperCAmelCase), (__UpperCAmelCase), (__UpperCAmelCase), (__UpperCAmelCase)): Dict = config_and_inputs
__UpperCAmelCase: List[str] = {"""input_ids""": input_ids_a}
return config, inputs_dict
@require_tf
class a ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
__lowerCAmelCase = () if is_tf_available() else ()
__lowerCAmelCase = (
{
"""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
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
def lowercase_ ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
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 lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: Dict = TFTransfoXLModelTester(self )
__UpperCAmelCase: Any = ConfigTester(self , config_class=snake_case_ , d_embed=37 )
def lowercase_ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ):
'''simple docstring'''
self.model_tester.set_seed()
__UpperCAmelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*snake_case_ )
def lowercase_ ( self ):
'''simple docstring'''
self.model_tester.set_seed()
__UpperCAmelCase: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*snake_case_ )
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*snake_case_ )
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase, __UpperCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase: str = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
__UpperCAmelCase: int = model_class(snake_case_ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
__UpperCAmelCase: Any = model.get_output_embeddings()
assert isinstance(snake_case_ , tf.keras.layers.Layer )
__UpperCAmelCase: int = model.get_bias()
assert name is None
else:
__UpperCAmelCase: Optional[int] = model.get_output_embeddings()
assert x is None
__UpperCAmelCase: str = model.get_bias()
assert name is None
def lowercase_ ( self ):
'''simple docstring'''
pass
@slow
def lowercase_ ( self ):
'''simple docstring'''
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase: str = TFTransfoXLModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss.""" )
def lowercase_ ( self ):
'''simple docstring'''
pass
@require_tf
class a ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip("""Skip test until #12651 is resolved.""" )
@slow
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: Optional[Any] = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""" )
# fmt: off
__UpperCAmelCase: str = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] , 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
__UpperCAmelCase: Dict = [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
__UpperCAmelCase: Dict = model.generate(snake_case_ , max_length=200 , do_sample=snake_case_ )
self.assertListEqual(output_ids[0].numpy().tolist() , snake_case_ ) | 523 | 0 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
lowercase__ = get_logger()
lowercase__ = None
class __snake_case ( TensorFormatter[Mapping, """jax.Array""", Mapping] ):
def __init__( self , lowercase=None , lowercase=None , **lowercase) -> Optional[Any]:
'''simple docstring'''
super().__init__(features=lowercase)
import jax
from jaxlib.xla_client import Device
if isinstance(lowercase , lowercase):
raise ValueError(
f'Expected {device} to be a `str` not {type(lowercase)}, as `jaxlib.xla_extension.Device` '
'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
'the device with `str()` to get its string identifier that will be internally mapped '
'to the actual `jaxlib.xla_extension.Device`.')
a__: int = device if isinstance(lowercase , lowercase) else str(jax.devices()[0])
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
a__: Any = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys()):
logger.warning(
f'Device with string identifier {self.device} not listed among the available '
f'devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default '
f'device: {str(jax.devices()[0])}.')
a__: Any = str(jax.devices()[0])
a__: Any = jnp_array_kwargs
@staticmethod
def lowerCamelCase_ ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
'''simple docstring'''
import jax
return {str(lowercase): device for device in jax.devices()}
def lowerCamelCase_ ( self , lowercase) -> List[str]:
'''simple docstring'''
import jax
import jax.numpy as jnp
if isinstance(lowercase , lowercase) and column:
if all(
isinstance(lowercase , jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
return jnp.stack(lowercase , axis=0)
return column
def lowerCamelCase_ ( self , lowercase) -> List[Any]:
'''simple docstring'''
import jax
import jax.numpy as jnp
if isinstance(lowercase , (str, bytes, type(lowercase))):
return value
elif isinstance(lowercase , (np.character, np.ndarray)) and np.issubdtype(value.dtype , np.character):
return value.tolist()
a__: Optional[int] = {}
if isinstance(lowercase , (np.number, np.ndarray)) and np.issubdtype(value.dtype , np.integer):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
a__: List[Any] = {'dtype': jnp.intaa}
else:
a__: str = {'dtype': jnp.intaa}
elif isinstance(lowercase , (np.number, np.ndarray)) and np.issubdtype(value.dtype , np.floating):
a__: List[str] = {'dtype': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(lowercase , PIL.Image.Image):
a__: Tuple = np.asarray(lowercase)
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
a__: Dict = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device]):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(lowercase , **{**default_dtype, **self.jnp_array_kwargs})
def lowerCamelCase_ ( self , lowercase) -> Dict:
'''simple docstring'''
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(lowercase , torch.Tensor):
return self._tensorize(data_struct.detach().cpu().numpy()[()])
if hasattr(lowercase , '__array__') and not isinstance(lowercase , jax.Array):
a__: str = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(lowercase , np.ndarray):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(lowercase) for substruct in data_struct])
elif isinstance(lowercase , (list, tuple)):
return self._consolidate([self.recursive_tensorize(lowercase) for substruct in data_struct])
return self._tensorize(lowercase)
def lowerCamelCase_ ( self , lowercase) -> List[str]:
'''simple docstring'''
return map_nested(self._recursive_tensorize , lowercase , map_list=lowercase)
def lowerCamelCase_ ( self , lowercase) -> Mapping:
'''simple docstring'''
a__: Union[str, Any] = self.numpy_arrow_extractor().extract_row(lowercase)
a__: Tuple = self.python_features_decoder.decode_row(lowercase)
return self.recursive_tensorize(lowercase)
def lowerCamelCase_ ( self , lowercase) -> "jax.Array":
'''simple docstring'''
a__: Any = self.numpy_arrow_extractor().extract_column(lowercase)
a__: Union[str, Any] = self.python_features_decoder.decode_column(lowercase , pa_table.column_names[0])
a__: str = self.recursive_tensorize(lowercase)
a__: Tuple = self._consolidate(lowercase)
return column
def lowerCamelCase_ ( self , lowercase) -> Mapping:
'''simple docstring'''
a__: Any = self.numpy_arrow_extractor().extract_batch(lowercase)
a__: str = self.python_features_decoder.decode_batch(lowercase)
a__: List[str] = self.recursive_tensorize(lowercase)
for column_name in batch:
a__: int = self._consolidate(batch[column_name])
return batch
| 217 | """simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class __snake_case :
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=99 , lowercase=64 , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_12 , lowercase=16 , lowercase=2 , lowercase=0.02 , lowercase=3 , lowercase=4 , lowercase=None , ) -> Optional[Any]:
'''simple docstring'''
a__: int = parent
a__: Union[str, Any] = batch_size
a__: Optional[int] = seq_length
a__: int = is_training
a__: Optional[Any] = use_input_mask
a__: List[Any] = use_token_type_ids
a__: List[str] = use_labels
a__: Dict = vocab_size
a__: Tuple = hidden_size
a__: Optional[Any] = embedding_size
a__: Optional[int] = num_hidden_layers
a__: Optional[int] = num_attention_heads
a__: Optional[int] = intermediate_size
a__: Dict = hidden_act
a__: List[str] = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: List[str] = max_position_embeddings
a__: str = type_vocab_size
a__: Tuple = type_sequence_label_size
a__: List[Any] = initializer_range
a__: Optional[Any] = num_labels
a__: Optional[int] = num_choices
a__: int = scope
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__: Union[str, Any] = None
if self.use_input_mask:
a__: Optional[Any] = random_attention_mask([self.batch_size, self.seq_length])
a__: Optional[Any] = None
if self.use_token_type_ids:
a__: Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
a__: List[Any] = None
a__: Optional[int] = None
a__: Optional[Any] = None
if self.use_labels:
a__: List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__: List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
a__: Optional[Any] = ids_tensor([self.batch_size] , self.num_choices)
a__: Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
return MegatronBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[Any]:
'''simple docstring'''
a__: List[str] = MegatronBertModel(config=lowercase)
model.to(lowercase)
model.eval()
a__: List[Any] = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase)
a__: Any = model(lowercase , token_type_ids=lowercase)
a__: List[Any] = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> List[str]:
'''simple docstring'''
a__: List[str] = MegatronBertForMaskedLM(config=lowercase)
model.to(lowercase)
model.eval()
a__: Optional[Any] = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__: Dict = MegatronBertForCausalLM(config=lowercase)
model.to(lowercase)
model.eval()
a__: List[str] = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__: Union[str, Any] = MegatronBertForNextSentencePrediction(config=lowercase)
model.to(lowercase)
model.eval()
a__: str = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__: int = MegatronBertForPreTraining(config=lowercase)
model.to(lowercase)
model.eval()
a__: Dict = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , next_sentence_label=lowercase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> int:
'''simple docstring'''
a__: Dict = MegatronBertForQuestionAnswering(config=lowercase)
model.to(lowercase)
model.eval()
a__: Any = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , start_positions=lowercase , end_positions=lowercase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Optional[Any]:
'''simple docstring'''
a__: Tuple = self.num_labels
a__: Union[str, Any] = MegatronBertForSequenceClassification(lowercase)
model.to(lowercase)
model.eval()
a__: int = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> Dict:
'''simple docstring'''
a__: int = self.num_labels
a__: Optional[Any] = MegatronBertForTokenClassification(config=lowercase)
model.to(lowercase)
model.eval()
a__: Tuple = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase) -> str:
'''simple docstring'''
a__: Dict = self.num_choices
a__: Any = MegatronBertForMultipleChoice(config=lowercase)
model.to(lowercase)
model.eval()
a__: List[str] = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__: Dict = token_type_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__: List[str] = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a__: List[Any] = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: Optional[Any] = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
): Tuple = config_and_inputs
a__: Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __snake_case ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
a__ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
a__ = (
{
"""feature-extraction""": MegatronBertModel,
"""fill-mask""": MegatronBertForMaskedLM,
"""question-answering""": MegatronBertForQuestionAnswering,
"""text-classification""": MegatronBertForSequenceClassification,
"""text-generation""": MegatronBertForCausalLM,
"""token-classification""": MegatronBertForTokenClassification,
"""zero-shot""": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
a__ = True
# test_resize_embeddings = False
a__ = False
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase=False) -> Optional[int]:
'''simple docstring'''
a__: List[Any] = super()._prepare_for_class(lowercase , lowercase , return_labels=lowercase)
if return_labels:
if model_class in get_values(lowercase):
a__: Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowercase)
a__: List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase)
return inputs_dict
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: Dict = MegatronBertModelTester(self)
a__: Dict = ConfigTester(self , config_class=lowercase , hidden_size=37)
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*lowercase)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowercase)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowercase)
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowercase)
def lowerCamelCase_ ( self) -> Optional[Any]:
'''simple docstring'''
a__: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowercase)
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowercase)
def lowerCamelCase_ ( self) -> Optional[int]:
'''simple docstring'''
a__: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowercase)
def __a ( _SCREAMING_SNAKE_CASE ) ->Any:
return torch.tensor(
_SCREAMING_SNAKE_CASE , dtype=torch.long , device=_SCREAMING_SNAKE_CASE , )
lowercase__ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case ( unittest.TestCase ):
@slow
@unittest.skip('Model is not available.')
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Dict = 'nvidia/megatron-bert-uncased-345m'
if "MYDIR" in os.environ:
a__: List[str] = os.path.join(os.environ['MYDIR'] , lowercase)
a__: Tuple = MegatronBertModel.from_pretrained(lowercase)
model.to(lowercase)
model.half()
a__: Any = _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]])
with torch.no_grad():
a__: str = model(lowercase)[0]
a__: Tuple = torch.Size((1, 9, 10_24))
self.assertEqual(output.shape , lowercase)
a__: Optional[Any] = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3):
for jj in range(3):
a__: List[Any] = output[0, ii, jj]
a__: Dict = expected[3 * ii + jj]
a__: str = 'ii={} jj={} a={} b={}'.format(lowercase , lowercase , lowercase , lowercase)
self.assertTrue(math.isclose(lowercase , lowercase , rel_tol=lowercase , abs_tol=lowercase) , msg=lowercase)
| 217 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCamelCase ( _snake_case : Union[str, Any] ,_snake_case : Dict ,_snake_case : Any ,_snake_case : Optional[Any] ,_snake_case : List[Any] ):
'''simple docstring'''
with open(_snake_case ) as metadata_file:
lowercase__ = json.load(_snake_case )
lowercase__ = LukeConfig(use_entity_aware_attention=_snake_case ,**metadata["model_config"] )
# Load in the weights from the checkpoint_path
lowercase__ = torch.load(_snake_case ,map_location="cpu" )
# Load the entity vocab file
lowercase__ = load_entity_vocab(_snake_case )
lowercase__ = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
lowercase__ = AddedToken("<ent>" ,lstrip=_snake_case ,rstrip=_snake_case )
lowercase__ = AddedToken("<ent2>" ,lstrip=_snake_case ,rstrip=_snake_case )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f'''Saving tokenizer to {pytorch_dump_folder_path}''' )
tokenizer.save_pretrained(_snake_case )
with open(os.path.join(_snake_case ,LukeTokenizer.vocab_files_names["entity_vocab_file"] ) ,"w" ) as f:
json.dump(_snake_case ,_snake_case )
lowercase__ = LukeTokenizer.from_pretrained(_snake_case )
# Initialize the embeddings of the special tokens
lowercase__ = state_dict["embeddings.word_embeddings.weight"]
lowercase__ = word_emb[tokenizer.convert_tokens_to_ids(["@"] )[0]].unsqueeze(0 )
lowercase__ = word_emb[tokenizer.convert_tokens_to_ids(["#"] )[0]].unsqueeze(0 )
lowercase__ = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
lowercase__ = f'''encoder.layer.{layer_index}.attention.self.'''
lowercase__ = state_dict[prefix + matrix_name]
lowercase__ = state_dict[prefix + matrix_name]
lowercase__ = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
lowercase__ = state_dict["entity_embeddings.entity_embeddings.weight"]
lowercase__ = entity_emb[entity_vocab["[MASK]"]]
lowercase__ = LukeModel(config=_snake_case ).eval()
lowercase__ , lowercase__ = model.load_state_dict(_snake_case ,strict=_snake_case )
if not (len(_snake_case ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(f'''Missing keys {', '.join(_snake_case )}. Expected only missing embeddings.position_ids''' )
if not (all(key.startswith("entity_predictions" ) or key.startswith("lm_head" ) for key in unexpected_keys )):
raise ValueError(
"Unexpected keys"
f''' {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions' ) or key.startswith('lm_head' ))] )}''' )
# Check outputs
lowercase__ = LukeTokenizer.from_pretrained(_snake_case ,task="entity_classification" )
lowercase__ = (
"Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
" new world number one avoid a humiliating second- round exit at Wimbledon ."
)
lowercase__ = (39, 42)
lowercase__ = tokenizer(_snake_case ,entity_spans=[span] ,add_prefix_space=_snake_case ,return_tensors="pt" )
lowercase__ = model(**_snake_case )
# Verify word hidden states
if model_size == "large":
lowercase__ = torch.Size((1, 42, 1_024) )
lowercase__ = torch.tensor(
[[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] )
else: # base
lowercase__ = torch.Size((1, 42, 768) )
lowercase__ = torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,_snake_case ,atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
lowercase__ = torch.Size((1, 1, 1_024) )
lowercase__ = torch.tensor([[0.0_466, -0.0_106, -0.0_179]] )
else: # base
lowercase__ = torch.Size((1, 1, 768) )
lowercase__ = torch.tensor([[0.1_457, 0.1_044, 0.0_174]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
f''' {expected_shape}''' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,_snake_case ,atol=1e-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(_snake_case ) )
model.save_pretrained(_snake_case )
def lowerCamelCase ( _snake_case : Union[str, Any] ):
'''simple docstring'''
lowercase__ = {}
with open(_snake_case ,"r" ,encoding="utf-8" ) as f:
for index, line in enumerate(_snake_case ):
lowercase__ , lowercase__ = line.rstrip().split("\t" )
lowercase__ = index
return entity_vocab
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 267 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : int = 50_000_000 ):
'''simple docstring'''
lowercase__ = set()
lowercase__ = int((limit - 24) ** (1 / 2) )
lowercase__ = set(range(3 ,prime_square_limit + 1 ,2 ) )
primes.add(2 )
for p in range(3 ,prime_square_limit + 1 ,2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p ,prime_square_limit + 1 ,_snake_case ) ) )
for primea in primes:
lowercase__ = primea * primea
for primea in primes:
lowercase__ = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
lowercase__ = primea * primea * primea * primea
lowercase__ = square + cube + tetr
if total >= limit:
break
ret.add(_snake_case )
return len(_snake_case )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 267 | 1 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
__A : str = logging.get_logger(__name__)
__A : str = {
"""tensor(bool)""": np.bool_,
"""tensor(int8)""": np.inta,
"""tensor(uint8)""": np.uinta,
"""tensor(int16)""": np.intaa,
"""tensor(uint16)""": np.uintaa,
"""tensor(int32)""": np.intaa,
"""tensor(uint32)""": np.uintaa,
"""tensor(int64)""": np.intaa,
"""tensor(uint64)""": np.uintaa,
"""tensor(float16)""": np.floataa,
"""tensor(float)""": np.floataa,
"""tensor(double)""": np.floataa,
}
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : List[str] , a : Tuple=None , **a : Any ) -> List[str]:
logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future.""" )
SCREAMING_SNAKE_CASE = model
SCREAMING_SNAKE_CASE = kwargs.get("""model_save_dir""" , a )
SCREAMING_SNAKE_CASE = kwargs.get("""latest_model_name""" , a )
def __call__( self : List[str] , **a : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE = {k: np.array(a ) for k, v in kwargs.items()}
return self.model.run(a , a )
@staticmethod
def _UpperCAmelCase ( a : Union[str, Path] , a : Any=None , a : Optional[int]=None ) -> Optional[Any]:
if provider is None:
logger.info("""No onnxruntime provider specified, using CPUExecutionProvider""" )
SCREAMING_SNAKE_CASE = """CPUExecutionProvider"""
return ort.InferenceSession(a , providers=[provider] , sess_options=a )
def _UpperCAmelCase ( self : str , a : Union[str, Path] , a : Optional[str] = None , **a : str ) -> Tuple:
SCREAMING_SNAKE_CASE = file_name if file_name is not None else ONNX_WEIGHTS_NAME
SCREAMING_SNAKE_CASE = self.model_save_dir.joinpath(self.latest_model_name )
SCREAMING_SNAKE_CASE = Path(a ).joinpath(a )
try:
shutil.copyfile(a , a )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
SCREAMING_SNAKE_CASE = self.model_save_dir.joinpath(a )
if src_path.exists():
SCREAMING_SNAKE_CASE = Path(a ).joinpath(a )
try:
shutil.copyfile(a , a )
except shutil.SameFileError:
pass
def _UpperCAmelCase ( self : Dict , a : Union[str, os.PathLike] , **a : Tuple , ) -> str:
if os.path.isfile(a ):
logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
return
os.makedirs(a , exist_ok=a )
# saving model weights/files
self._save_pretrained(a , **a )
@classmethod
def _UpperCAmelCase ( cls : List[Any] , a : Union[str, Path] , a : Optional[Union[bool, str, None]] = None , a : Optional[Union[str, None]] = None , a : bool = False , a : Optional[str] = None , a : Optional[str] = None , a : Optional[str] = None , a : Optional["ort.SessionOptions"] = None , **a : Union[str, Any] , ) -> List[str]:
SCREAMING_SNAKE_CASE = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(a ):
SCREAMING_SNAKE_CASE = OnnxRuntimeModel.load_model(
os.path.join(a , a ) , provider=a , sess_options=a )
SCREAMING_SNAKE_CASE = Path(a )
# load model from hub
else:
# download model
SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id=a , filename=a , use_auth_token=a , revision=a , cache_dir=a , force_download=a , )
SCREAMING_SNAKE_CASE = Path(a ).parent
SCREAMING_SNAKE_CASE = Path(a ).name
SCREAMING_SNAKE_CASE = OnnxRuntimeModel.load_model(a , provider=a , sess_options=a )
return cls(model=a , **a )
@classmethod
def _UpperCAmelCase ( cls : List[Any] , a : Union[str, Path] , a : bool = True , a : Optional[str] = None , a : Optional[str] = None , **a : Union[str, Any] , ) -> Any:
SCREAMING_SNAKE_CASE = None
if len(str(a ).split("""@""" ) ) == 2:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model_id.split("""@""" )
return cls._from_pretrained(
model_id=a , revision=a , cache_dir=a , force_download=a , use_auth_token=a , **a , )
| 707 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument(
"""--original_config_file""",
type=str,
required=True,
help="""The YAML config file corresponding to the original architecture.""",
)
parser.add_argument(
"""--num_in_channels""",
default=None,
type=int,
help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
)
parser.add_argument(
"""--image_size""",
default=5_1_2,
type=int,
help=(
"""The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
""" Base. Use 768 for Stable Diffusion v2."""
),
)
parser.add_argument(
"""--extract_ema""",
action="""store_true""",
help=(
"""Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
""" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
""" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
),
)
parser.add_argument(
"""--upcast_attention""",
action="""store_true""",
help=(
"""Whether the attention computation should always be upcasted. This is necessary when running stable"""
""" diffusion 2.1."""
),
)
parser.add_argument(
"""--from_safetensors""",
action="""store_true""",
help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
)
parser.add_argument(
"""--to_safetensors""",
action="""store_true""",
help="""Whether to store pipeline in safetensors format or not.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f"""could not parse string as bool {string}""" )
parser.add_argument(
"""--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
)
parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)
__A : Union[str, Any] = parser.parse_args()
__A : Any = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 450 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
"""RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
"""RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
"""RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
"""RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class lowercase__ ( A_ ):
__UpperCAmelCase = '''rwkv'''
__UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self , SCREAMING_SNAKE_CASE=5_0277 , SCREAMING_SNAKE_CASE=1024 , SCREAMING_SNAKE_CASE=4096 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=1e-5 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=6 , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=True , **SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
_lowerCamelCase : List[str] = vocab_size
_lowerCamelCase : Any = context_length
_lowerCamelCase : str = hidden_size
_lowerCamelCase : Union[str, Any] = num_hidden_layers
_lowerCamelCase : Dict = attention_hidden_size if attention_hidden_size is not None else hidden_size
_lowerCamelCase : Tuple = intermediate_size if intermediate_size is not None else 4 * hidden_size
_lowerCamelCase : int = layer_norm_epsilon
_lowerCamelCase : int = rescale_every
_lowerCamelCase : Optional[int] = use_cache
_lowerCamelCase : Dict = bos_token_id
_lowerCamelCase : List[Any] = eos_token_id
super().__init__(
tie_word_embeddings=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
| 88 |
from __future__ import annotations
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : str , __a : Dict=None ) -> int:
"""simple docstring"""
__lowercase : int = data
__lowercase : Optional[int] = None
def __repr__( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase : Any = []
__lowercase : Dict = self
while temp:
string_rep.append(F"{temp.data}" )
__lowercase : Union[str, Any] = temp.next
return "->".join(__a )
def snake_case_ ( lowerCAmelCase_ : list ):
if not elements_list:
raise Exception("""The Elements List is empty""" )
__lowercase : List[str] = Node(elements_list[0] )
for i in range(1 , len(lowerCAmelCase_ ) ):
__lowercase : int = Node(elements_list[i] )
__lowercase : List[str] = current.next
return head
def snake_case_ ( lowerCAmelCase_ : Node ):
if head_node is not None and isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
print_reverse(head_node.next )
print(head_node.data )
def snake_case_ ( ):
from doctest import testmod
testmod()
__lowercase : List[Any] = make_linked_list([14, 52, 14, 12, 43] )
print("""Linked List:""" )
print(lowerCAmelCase_ )
print("""Elements in Reverse:""" )
print_reverse(lowerCAmelCase_ )
if __name__ == "__main__":
main() | 149 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return [ord(_lowerCamelCase ) - 96 for elem in plain]
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return "".join(chr(elem + 96 ) for elem in encoded )
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :List[str] = encode(input('''-> ''' ).strip().lower() )
print('''Encoded: ''' , _lowerCamelCase )
print('''Decoded:''' , decode(_lowerCamelCase ) )
if __name__ == "__main__":
main()
| 719 | 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()
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = DPTConfig(embedding_type='''hybrid''' )
if "large" in checkpoint_url:
__UpperCamelCase :Dict = 1_024
__UpperCamelCase :Optional[int] = 4_096
__UpperCamelCase :int = 24
__UpperCamelCase :List[str] = 16
__UpperCamelCase :List[str] = [5, 11, 17, 23]
__UpperCamelCase :Optional[int] = [256, 512, 1_024, 1_024]
__UpperCamelCase :str = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
__UpperCamelCase :str = 768
__UpperCamelCase :Dict = [1, 1, 1, 0.5]
__UpperCamelCase :List[str] = [256, 512, 768, 768]
__UpperCamelCase :int = 150
__UpperCamelCase :Union[str, Any] = 16
__UpperCamelCase :str = (1, 384, 384)
__UpperCamelCase :List[str] = False
__UpperCamelCase :List[Any] = '''project'''
if "ade" in checkpoint_url:
__UpperCamelCase :List[Any] = True
__UpperCamelCase :Optional[Any] = 768
__UpperCamelCase :List[Any] = [1, 1, 1, 0.5]
__UpperCamelCase :List[str] = 150
__UpperCamelCase :Optional[int] = 16
__UpperCamelCase :Optional[int] = '''huggingface/label-files'''
__UpperCamelCase :Dict = '''ade20k-id2label.json'''
__UpperCamelCase :int = json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) ) , '''r''' ) )
__UpperCamelCase :Any = {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__UpperCamelCase :Tuple = idalabel
__UpperCamelCase :Dict = {v: k for k, v in idalabel.items()}
__UpperCamelCase :Optional[int] = [1, 150, 480, 480]
return config, expected_shape
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[int] = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
__UpperCamelCase :Dict = name.replace('''pretrained.model''' , '''dpt.encoder''' )
if "pretrained.model" in name:
__UpperCamelCase :Optional[int] = name.replace('''pretrained.model''' , '''dpt.embeddings''' )
if "patch_embed" in name:
__UpperCamelCase :Any = name.replace('''patch_embed''' , '''''' )
if "pos_embed" in name:
__UpperCamelCase :Dict = name.replace('''pos_embed''' , '''position_embeddings''' )
if "attn.proj" in name:
__UpperCamelCase :List[Any] = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "proj" in name and "project" not in name:
__UpperCamelCase :Any = name.replace('''proj''' , '''projection''' )
if "blocks" in name:
__UpperCamelCase :List[Any] = name.replace('''blocks''' , '''layer''' )
if "mlp.fc1" in name:
__UpperCamelCase :int = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
__UpperCamelCase :List[str] = name.replace('''mlp.fc2''' , '''output.dense''' )
if "norm1" in name and "backbone" not in name:
__UpperCamelCase :Dict = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name and "backbone" not in name:
__UpperCamelCase :Optional[int] = name.replace('''norm2''' , '''layernorm_after''' )
if "scratch.output_conv" in name:
__UpperCamelCase :List[str] = name.replace('''scratch.output_conv''' , '''head''' )
if "scratch" in name:
__UpperCamelCase :Optional[int] = name.replace('''scratch''' , '''neck''' )
if "layer1_rn" in name:
__UpperCamelCase :int = name.replace('''layer1_rn''' , '''convs.0''' )
if "layer2_rn" in name:
__UpperCamelCase :Dict = name.replace('''layer2_rn''' , '''convs.1''' )
if "layer3_rn" in name:
__UpperCamelCase :Dict = name.replace('''layer3_rn''' , '''convs.2''' )
if "layer4_rn" in name:
__UpperCamelCase :int = name.replace('''layer4_rn''' , '''convs.3''' )
if "refinenet" in name:
__UpperCamelCase :Tuple = 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
__UpperCamelCase :Dict = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
if "out_conv" in name:
__UpperCamelCase :Union[str, Any] = name.replace('''out_conv''' , '''projection''' )
if "resConfUnit1" in name:
__UpperCamelCase :int = name.replace('''resConfUnit1''' , '''residual_layer1''' )
if "resConfUnit2" in name:
__UpperCamelCase :str = name.replace('''resConfUnit2''' , '''residual_layer2''' )
if "conv1" in name:
__UpperCamelCase :Any = name.replace('''conv1''' , '''convolution1''' )
if "conv2" in name:
__UpperCamelCase :Optional[int] = name.replace('''conv2''' , '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
__UpperCamelCase :Union[str, Any] = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
__UpperCamelCase :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:
__UpperCamelCase :Any = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
__UpperCamelCase :Tuple = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
__UpperCamelCase :Tuple = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
__UpperCamelCase :Optional[int] = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
__UpperCamelCase :Tuple = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
__UpperCamelCase :Dict = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
__UpperCamelCase :Dict = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
__UpperCamelCase :Optional[int] = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
__UpperCamelCase :List[str] = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
__UpperCamelCase :Optional[int] = name.replace('''pretrained''' , '''dpt''' )
if "bn" in name:
__UpperCamelCase :List[str] = name.replace('''bn''' , '''batch_norm''' )
if "head" in name:
__UpperCamelCase :Tuple = name.replace('''head''' , '''head.head''' )
if "encoder.norm" in name:
__UpperCamelCase :List[str] = name.replace('''encoder.norm''' , '''layernorm''' )
if "auxlayer" in name:
__UpperCamelCase :Optional[Any] = name.replace('''auxlayer''' , '''auxiliary_head.head''' )
if "backbone" in name:
__UpperCamelCase :int = name.replace('''backbone''' , '''backbone.bit.encoder''' )
if ".." in name:
__UpperCamelCase :Tuple = name.replace('''..''' , '''.''' )
if "stem.conv" in name:
__UpperCamelCase :List[Any] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' )
if "blocks" in name:
__UpperCamelCase :Tuple = name.replace('''blocks''' , '''layers''' )
if "convolution" in name and "backbone" in name:
__UpperCamelCase :List[Any] = name.replace('''convolution''' , '''conv''' )
if "layer" in name and "backbone" in name:
__UpperCamelCase :Dict = name.replace('''layer''' , '''layers''' )
if "backbone.bit.encoder.bit" in name:
__UpperCamelCase :Dict = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' )
if "embedder.conv" in name:
__UpperCamelCase :Optional[Any] = name.replace('''embedder.conv''' , '''embedder.convolution''' )
if "backbone.bit.encoder.stem.norm" in name:
__UpperCamelCase :List[Any] = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' )
return name
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__UpperCamelCase :Any = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
__UpperCamelCase :Dict = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__UpperCamelCase :Union[str, Any] = in_proj_weight[: config.hidden_size, :]
__UpperCamelCase :Dict = in_proj_bias[: config.hidden_size]
__UpperCamelCase :Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__UpperCamelCase :Dict = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__UpperCamelCase :str = in_proj_weight[
-config.hidden_size :, :
]
__UpperCamelCase :Union[str, Any] = in_proj_bias[-config.hidden_size :]
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__UpperCamelCase :List[Any] = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = get_dpt_config(SCREAMING_SNAKE_CASE )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
__UpperCamelCase :Optional[Any] = torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(SCREAMING_SNAKE_CASE )
# rename keys
for key in state_dict.copy().keys():
__UpperCamelCase :Any = state_dict.pop(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Optional[int] = val
# read in qkv matrices
read_in_q_k_v(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# load HuggingFace model
__UpperCamelCase :Union[str, Any] = DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE )
model.load_state_dict(SCREAMING_SNAKE_CASE )
model.eval()
# Check outputs on an image
__UpperCamelCase :Any = 480 if '''ade''' in checkpoint_url else 384
__UpperCamelCase :Optional[Any] = DPTImageProcessor(size=SCREAMING_SNAKE_CASE )
__UpperCamelCase :Dict = prepare_img()
__UpperCamelCase :Any = image_processor(SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
# forward pass
__UpperCamelCase :Optional[Any] = model(**SCREAMING_SNAKE_CASE ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE ).predicted_depth
if show_prediction:
__UpperCamelCase :str = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=SCREAMING_SNAKE_CASE , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(SCREAMING_SNAKE_CASE )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE )
if push_to_hub:
model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
if __name__ == "__main__":
__lowercase = 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=False,
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.''',
)
parser.add_argument(
'''--show_prediction''',
action='''store_true''',
)
__lowercase = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 452 | 0 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
lowerCAmelCase__ = float('''nan''')
class __snake_case :
def __init__( self : int , __lowerCAmelCase : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : str = sys.stdout
_lowerCamelCase : List[Any] = open(__lowerCAmelCase , '''a''' )
def __getattr__( self : str , __lowerCAmelCase : List[str] ):
"""simple docstring"""
return getattr(self.stdout , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str , __lowerCAmelCase : List[Any] ):
"""simple docstring"""
self.stdout.write(__lowerCAmelCase )
# strip tqdm codes
self.file.write(re.sub(R'''^.*\r''' , '''''' , __lowerCAmelCase , 0 , re.M ) )
def snake_case_ ( A_ : str=80, A_ : List[Any]=False ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = []
# deal with critical env vars
_lowerCamelCase : Union[str, Any] = ['''CUDA_VISIBLE_DEVICES''']
for key in env_keys:
_lowerCamelCase : str = os.environ.get(A_, A_ )
if val is not None:
cmd.append(F'''{key}={val}''' )
# python executable (not always needed if the script is executable)
_lowerCamelCase : int = sys.executable if full_python_path else sys.executable.split('''/''' )[-1]
cmd.append(A_ )
# now the normal args
cmd += list(map(shlex.quote, sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
_lowerCamelCase : Tuple = []
_lowerCamelCase : int = ''''''
while len(A_ ) > 0:
current_line += F'''{cmd.pop(0 )} '''
if len(A_ ) == 0 or len(A_ ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(A_ )
_lowerCamelCase : int = ''''''
return "\\\n".join(A_ )
def snake_case_ ( A_ : Tuple, A_ : Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : List[Any] = re.sub(R'''[\\\n]+''', ''' ''', args.base_cmd )
# remove --output_dir if any and set our own
_lowerCamelCase : Union[str, Any] = re.sub('''--output_dir\s+[^\s]+''', '''''', args.base_cmd )
args.base_cmd += F''' --output_dir {output_dir}'''
# ensure we have --overwrite_output_dir
_lowerCamelCase : Optional[int] = re.sub('''--overwrite_output_dir\s+''', '''''', args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def snake_case_ ( A_ : int, A_ : List[Any], A_ : str, A_ : List[Any], A_ : Optional[int], A_ : Union[str, Any], A_ : List[str] ):
'''simple docstring'''
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0, 1_00 ) for k in metric_keys}, **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222] )}, )
_lowerCamelCase : List[str] = subprocess.run(A_, capture_output=A_, text=A_ )
if verbose:
print('''STDOUT''', result.stdout )
print('''STDERR''', result.stderr )
# save the streams
_lowerCamelCase : Union[str, Any] = variation.replace(''' ''', '''-''' )
with open(Path(A_ ) / F'''log.{prefix}.stdout.txt''', '''w''' ) as f:
f.write(result.stdout )
with open(Path(A_ ) / F'''log.{prefix}.stderr.txt''', '''w''' ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print('''failed''' )
return {target_metric_key: nan}
with io.open(F'''{output_dir}/all_results.json''', '''r''', encoding='''utf-8''' ) as f:
_lowerCamelCase : str = json.load(A_ )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def snake_case_ ( A_ : Dict, A_ : List[str], A_ : Optional[int], A_ : Any, A_ : List[Any], A_ : str, A_ : Union[str, Any], A_ : List[Any], A_ : Optional[Any], A_ : Any, ):
'''simple docstring'''
_lowerCamelCase : Tuple = []
_lowerCamelCase : List[str] = []
_lowerCamelCase : Optional[Any] = F'''{id}: {variation:<{longest_variation_len}}'''
_lowerCamelCase : int = F'''{preamble}: '''
_lowerCamelCase : List[str] = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(A_ ), desc=A_, leave=A_ ):
_lowerCamelCase : Dict = process_run_single(
A_, A_, A_, A_, A_, A_, A_ )
_lowerCamelCase : Dict = single_run_metrics[target_metric_key]
if not math.isnan(A_ ):
metrics.append(A_ )
results.append(A_ )
outcome += "✓"
else:
outcome += "✘"
_lowerCamelCase : List[Any] = F'''\33[2K\r{outcome}'''
if len(A_ ) > 0:
_lowerCamelCase : str = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
_lowerCamelCase : Optional[Any] = round(mean_metrics[target_metric_key], 2 )
_lowerCamelCase : List[Any] = F'''{outcome} {mean_target}'''
if len(A_ ) > 1:
results_str += F''' {tuple(round(A_, 2 ) for x in results )}'''
print(A_ )
_lowerCamelCase : Optional[Any] = variation
return mean_metrics
else:
print(A_ )
return {variation_key: variation, target_metric_key: nan}
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = torch.cuda.get_device_properties(torch.device('''cuda''' ) )
return F'''
Datetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}
Software:
transformers: {transformers.__version__}
torch : {torch.__version__}
cuda : {torch.version.cuda}
python : {platform.python_version()}
Hardware:
{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB
'''
def snake_case_ ( A_ : Union[str, Any], A_ : str, A_ : int, A_ : int, A_ : Any ):
'''simple docstring'''
_lowerCamelCase : str = pd.DataFrame(A_ )
_lowerCamelCase : Optional[Any] = '''variation'''
_lowerCamelCase : List[Any] = '''diff_%'''
_lowerCamelCase : Optional[int] = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
_lowerCamelCase : Optional[Any] = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(A_ ):
# as a fallback, use the minimal value as the sentinel
_lowerCamelCase : str = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(A_ ):
_lowerCamelCase : str = df.apply(
lambda A_ : round(1_00 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0, axis='''columns''', )
# re-order columns
_lowerCamelCase : str = [variation_key, target_metric_key, diff_key, *report_metric_keys]
_lowerCamelCase : Tuple = df.reindex(A_, axis='''columns''' ) # reorder cols
# capitalize
_lowerCamelCase : Any = df.rename(str.capitalize, axis='''columns''' )
# make the cols as narrow as possible
_lowerCamelCase : int = df.rename(lambda A_ : c.replace('''_''', '''<br>''' ), axis='''columns''' )
_lowerCamelCase : int = df.rename(lambda A_ : c.replace('''_''', '''\n''' ), axis='''columns''' )
_lowerCamelCase : str = ['''''', '''Copy between the cut-here-lines and paste as is to github or a forum''']
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=A_, floatfmt='''.2f''' )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=A_, floatfmt='''.2f''' )]
print('''\n\n'''.join(A_ ) )
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--base-cmd''', default=A_, type=A_, required=A_, help='''Base cmd''', )
parser.add_argument(
'''--variations''', default=A_, type=A_, nargs='''+''', required=A_, help='''Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'''', )
parser.add_argument(
'''--base-variation''', default=A_, type=A_, help='''Baseline variation to compare to. if None the minimal target value will be used to compare against''', )
parser.add_argument(
'''--target-metric-key''', default=A_, type=A_, required=A_, help='''Target metric key in output_dir/all_results.json, e.g., train_samples_per_second''', )
parser.add_argument(
'''--report-metric-keys''', default='''''', type=A_, help='''Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples''', )
parser.add_argument(
'''--repeat-times''', default=1, type=A_, help='''How many times to re-run each variation - an average will be reported''', )
parser.add_argument(
'''--output_dir''', default='''output_benchmark''', type=A_, help='''The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked''', )
parser.add_argument(
'''--verbose''', default=A_, action='''store_true''', help='''Whether to show the outputs of each run or just the benchmark progress''', )
_lowerCamelCase : Union[str, Any] = parser.parse_args()
_lowerCamelCase : List[str] = args.output_dir
Path(A_ ).mkdir(exist_ok=A_ )
_lowerCamelCase : List[Any] = get_base_command(A_, A_ )
# split each dimension into its --foo variations
_lowerCamelCase : Tuple = [list(map(str.strip, re.split(R'''\|''', A_ ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
_lowerCamelCase : str = list(map(str.strip, map(''' '''.join, itertools.product(*A_ ) ) ) )
_lowerCamelCase : Optional[Any] = max(len(A_ ) for x in variations )
# split wanted keys
_lowerCamelCase : List[str] = args.report_metric_keys.split()
# capture prints into a log file for convenience
_lowerCamelCase : List[str] = F'''benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S" )}.txt'''
print(F'''\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt''' )
print(F'''and this script\'s output is also piped into {report_fn}''' )
_lowerCamelCase : Tuple = Tee(A_ )
print(F'''\n*** Running {len(A_ )} benchmarks:''' )
print(F'''Base command: {" ".join(A_ )}''' )
_lowerCamelCase : Tuple = '''variation'''
_lowerCamelCase : Optional[Any] = []
for id, variation in enumerate(tqdm(A_, desc='''Total completion: ''', leave=A_ ) ):
_lowerCamelCase : Dict = base_cmd + variation.split()
results.append(
process_run(
id + 1, A_, A_, A_, A_, args.target_metric_key, A_, args.repeat_times, A_, args.verbose, ) )
process_results(A_, args.target_metric_key, A_, args.base_variation, A_ )
if __name__ == "__main__":
main()
| 83 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
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, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=2 , lowerCAmelCase_=8 , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=99 , lowerCAmelCase_=16 , lowerCAmelCase_=5 , lowerCAmelCase_=2 , lowerCAmelCase_=36 , lowerCAmelCase_="gelu" , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=5_12 , lowerCAmelCase_=16 , lowerCAmelCase_=2 , lowerCAmelCase_=0.02 , lowerCAmelCase_=3 , lowerCAmelCase_=4 , lowerCAmelCase_=None , ):
"""simple docstring"""
_snake_case = parent
_snake_case = batch_size
_snake_case = seq_length
_snake_case = is_training
_snake_case = use_input_mask
_snake_case = use_token_type_ids
_snake_case = use_labels
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = intermediate_size
_snake_case = hidden_act
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = type_sequence_label_size
_snake_case = initializer_range
_snake_case = num_labels
_snake_case = num_choices
_snake_case = scope
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case = None
if self.use_input_mask:
_snake_case = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case = None
if self.use_token_type_ids:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case = None
_snake_case = None
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case = ids_tensor([self.batch_size] , self.num_choices )
_snake_case = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
"""simple docstring"""
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.get_config()
_snake_case = 3_00
return config
def lowerCamelCase ( self ):
"""simple docstring"""
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) = self.prepare_config_and_inputs()
_snake_case = True
_snake_case = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_snake_case = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = MraModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )
_snake_case = model(lowerCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = True
_snake_case = MraModel(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , encoder_attention_mask=lowerCAmelCase_ , )
_snake_case = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , )
_snake_case = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = MraForMaskedLM(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = MraForQuestionAnswering(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , start_positions=lowerCAmelCase_ , end_positions=lowerCAmelCase_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.num_labels
_snake_case = MraForSequenceClassification(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.num_labels
_snake_case = MraForTokenClassification(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.num_choices
_snake_case = MraForMultipleChoice(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_snake_case = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_snake_case = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_snake_case = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) = config_and_inputs
_snake_case = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( _lowerCamelCase , unittest.TestCase ):
__lowercase = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
__lowercase = False
__lowercase = False
__lowercase = False
__lowercase = False
__lowercase = ()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = MraModelTester(self )
_snake_case = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=37 )
def lowerCamelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_snake_case = type
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_ )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = MraModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
@unittest.skip(reason='MRA does not output attentions' )
def lowerCamelCase ( self ):
"""simple docstring"""
return
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = MraModel.from_pretrained('uw-madison/mra-base-512-4' )
_snake_case = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
_snake_case = model(lowerCAmelCase_ )[0]
_snake_case = torch.Size((1, 2_56, 7_68) )
self.assertEqual(output.shape , lowerCAmelCase_ )
_snake_case = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase_ , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
_snake_case = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
_snake_case = model(lowerCAmelCase_ )[0]
_snake_case = 5_02_65
_snake_case = torch.Size((1, 2_56, vocab_size) )
self.assertEqual(output.shape , lowerCAmelCase_ )
_snake_case = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase_ , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
_snake_case = torch.arange(40_96 ).unsqueeze(0 )
with torch.no_grad():
_snake_case = model(lowerCAmelCase_ )[0]
_snake_case = 5_02_65
_snake_case = torch.Size((1, 40_96, vocab_size) )
self.assertEqual(output.shape , lowerCAmelCase_ )
_snake_case = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase_ , atol=1E-4 ) )
| 495 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCAmelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCAmelCase = "main"
# Default branch name
_lowerCAmelCase = "f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
_lowerCAmelCase = "aaaaaaa"
# This commit does not exist, so we should 404.
_lowerCAmelCase = "d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCAmelCase = "4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"
@contextlib.contextmanager
def _snake_case ( ):
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def _snake_case ( ):
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Dict ):
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class lowerCAmelCase_ ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def UpperCamelCase_ ( self : Union[str, Any] , _A : str ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def UpperCamelCase_ ( self : int , _A : List[str] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def UpperCamelCase_ ( self : Tuple , _A : Tuple ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def UpperCamelCase_ ( self : int ):
self.assertEqual(find_labels(_A ) , ['''labels'''] )
self.assertEqual(find_labels(_A ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_A ) , ['''start_positions''', '''end_positions'''] )
class lowerCAmelCase_ ( a__ ):
pass
self.assertEqual(find_labels(_A ) , ['''labels'''] )
@require_tf
def UpperCamelCase_ ( self : Dict ):
self.assertEqual(find_labels(_A ) , ['''labels'''] )
self.assertEqual(find_labels(_A ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_A ) , ['''start_positions''', '''end_positions'''] )
class lowerCAmelCase_ ( a__ ):
pass
self.assertEqual(find_labels(_A ) , ['''labels'''] )
@require_flax
def UpperCamelCase_ ( self : str ):
self.assertEqual(find_labels(_A ) , [] )
self.assertEqual(find_labels(_A ) , [] )
self.assertEqual(find_labels(_A ) , [] )
class lowerCAmelCase_ ( a__ ):
pass
self.assertEqual(find_labels(_A ) , [] )
| 716 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Any ):
_UpperCamelCase = tempfile.mkdtemp()
# fmt: off
_UpperCamelCase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
_UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
_UpperCamelCase = {
'''do_resize''': True,
'''size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
_UpperCamelCase = os.path.join(self.tmpdirname , _A )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_A , _A )
def UpperCamelCase_ ( self : Tuple , **_A : Optional[Any] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_A )
def UpperCamelCase_ ( self : List[Any] , **_A : Union[str, Any] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_A )
def UpperCamelCase_ ( self : int ):
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_UpperCamelCase = [Image.fromarray(np.moveaxis(_A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCamelCase_ ( self : Optional[int] ):
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = self.get_image_processor()
_UpperCamelCase = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
processor.save_pretrained(self.tmpdirname )
_UpperCamelCase = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def UpperCamelCase_ ( self : Optional[Any] ):
_UpperCamelCase = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCamelCase = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_UpperCamelCase = self.get_image_processor(do_normalize=_A , padding_value=1.0 )
_UpperCamelCase = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.get_image_processor()
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
_UpperCamelCase = self.prepare_image_inputs()
_UpperCamelCase = image_processor(_A , return_tensors='''np''' )
_UpperCamelCase = processor(images=_A , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCamelCase_ ( self : Dict ):
_UpperCamelCase = self.get_image_processor()
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
_UpperCamelCase = '''lower newer'''
_UpperCamelCase = processor(text=_A )
_UpperCamelCase = tokenizer(_A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase_ ( self : Union[str, Any] ):
_UpperCamelCase = self.get_image_processor()
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
_UpperCamelCase = '''lower newer'''
_UpperCamelCase = self.prepare_image_inputs()
_UpperCamelCase = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_A ):
processor()
def UpperCamelCase_ ( self : List[Any] ):
_UpperCamelCase = self.get_image_processor()
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
_UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCamelCase = processor.batch_decode(_A )
_UpperCamelCase = tokenizer.batch_decode(_A )
self.assertListEqual(_A , _A )
def UpperCamelCase_ ( self : List[str] ):
_UpperCamelCase = self.get_image_processor()
_UpperCamelCase = self.get_tokenizer()
_UpperCamelCase = VisionTextDualEncoderProcessor(tokenizer=_A , image_processor=_A )
_UpperCamelCase = '''lower newer'''
_UpperCamelCase = self.prepare_image_inputs()
_UpperCamelCase = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 71 | 0 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( _snake_case :Dict ) -> Optional[int]:
_A = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
_A = 128
elif "12-12" in model_name:
_A = 12
_A = 12
elif "14-14" in model_name:
_A = 14
_A = 14
elif "16-16" in model_name:
_A = 16
_A = 16
else:
raise ValueError('''Model not supported''' )
_A = '''huggingface/label-files'''
if "speech-commands" in model_name:
_A = 35
_A = '''speech-commands-v2-id2label.json'''
else:
_A = 527
_A = '''audioset-id2label.json'''
_A = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type='''dataset''' ) , '''r''' ) )
_A = {int(_snake_case ): v for k, v in idalabel.items()}
_A = idalabel
_A = {v: k for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE_ ( _snake_case :List[Any] ) -> Tuple:
if "module.v" in name:
_A = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
_A = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
_A = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
_A = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
_A = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
_A = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
_A = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
_A = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
_A = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
_A = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
_A = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
_A = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
_A = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
_A = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
_A = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def SCREAMING_SNAKE_CASE_ ( _snake_case :Any , _snake_case :List[str] ) -> List[Any]:
for key in orig_state_dict.copy().keys():
_A = orig_state_dict.pop(_snake_case )
if "qkv" in key:
_A = key.split('''.''' )
_A = int(key_split[3] )
_A = config.hidden_size
if "weight" in key:
_A = val[:dim, :]
_A = val[dim : dim * 2, :]
_A = val[-dim:, :]
else:
_A = val[:dim]
_A = val[dim : dim * 2]
_A = val[-dim:]
else:
_A = val
return orig_state_dict
def SCREAMING_SNAKE_CASE_ ( _snake_case :List[str] ) -> Dict:
_A = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( _snake_case :Tuple , _snake_case :Dict , _snake_case :List[Any]=False ) -> Optional[Any]:
_A = get_audio_spectrogram_transformer_config(_snake_case )
_A = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
_A = model_name_to_url[model_name]
_A = torch.hub.load_state_dict_from_url(_snake_case , map_location='''cpu''' )
# remove some keys
remove_keys(_snake_case )
# rename some keys
_A = convert_state_dict(_snake_case , _snake_case )
# load 🤗 model
_A = ASTForAudioClassification(_snake_case )
model.eval()
model.load_state_dict(_snake_case )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
_A = -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
_A = 4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
_A = 1_024 if '''speech-commands''' not in model_name else 128
_A = ASTFeatureExtractor(mean=_snake_case , std=_snake_case , max_length=_snake_case )
if "speech-commands" in model_name:
_A = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
_A = dataset[0]['''audio''']['''array''']
else:
_A = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
_A , _A = torchaudio.load(_snake_case )
_A = waveform.squeeze().numpy()
_A = feature_extractor(_snake_case , sampling_rate=16_000 , return_tensors='''pt''' )
# forward pass
_A = model(**_snake_case )
_A = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
_A = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
_A = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
_A = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
_A = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
_A = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
_A = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
_A = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
_A = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , _snake_case , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
print(F'''Saving feature extractor to {pytorch_dump_folder_path}''' )
feature_extractor.save_pretrained(_snake_case )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(F'''MIT/{model_name}''' )
feature_extractor.push_to_hub(F'''MIT/{model_name}''' )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""ast-finetuned-audioset-10-10-0.4593""",
type=str,
help="""Name of the Audio Spectrogram Transformer model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
UpperCAmelCase_ = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 2 |
UpperCAmelCase_ = 0 # The first color of the flag.
UpperCAmelCase_ = 1 # The second color of the flag.
UpperCAmelCase_ = 2 # The third color of the flag.
UpperCAmelCase_ = (red, white, blue)
def SCREAMING_SNAKE_CASE_ ( _snake_case :list ) -> list:
if not sequence:
return []
if len(_snake_case ) == 1:
return list(_snake_case )
_A = 0
_A = len(_snake_case ) - 1
_A = 0
while mid <= high:
if sequence[mid] == colors[0]:
_A , _A = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_A , _A = sequence[high], sequence[mid]
high -= 1
else:
_A = F'''The elements inside the sequence must contains only {colors} values'''
raise ValueError(_snake_case )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ = input("""Enter numbers separated by commas:\n""").strip()
UpperCAmelCase_ = [int(item.strip()) for item in user_input.split(""",""")]
print(f'{dutch_national_flag_sort(unsorted)}')
| 2 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
),
}
class a__ ( __lowerCAmelCase ):
__magic_name__ : str = '''xlm-prophetnet'''
__magic_name__ : Tuple = ['''past_key_values''']
__magic_name__ : Any = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__(self : Optional[Any], __UpperCAmelCase : int = 0.1, __UpperCAmelCase : Tuple = "gelu", __UpperCAmelCase : Union[str, Any] = 30522, __UpperCAmelCase : Optional[int] = 1024, __UpperCAmelCase : Tuple = 4096, __UpperCAmelCase : Union[str, Any] = 12, __UpperCAmelCase : int = 16, __UpperCAmelCase : Dict = 4096, __UpperCAmelCase : Optional[Any] = 12, __UpperCAmelCase : Optional[int] = 16, __UpperCAmelCase : Union[str, Any] = 0.1, __UpperCAmelCase : Tuple = 0.1, __UpperCAmelCase : int = 512, __UpperCAmelCase : List[str] = 0.02, __UpperCAmelCase : Tuple = True, __UpperCAmelCase : Optional[Any] = True, __UpperCAmelCase : Union[str, Any] = 0, __UpperCAmelCase : List[Any] = 2, __UpperCAmelCase : Tuple = 32, __UpperCAmelCase : int = 128, __UpperCAmelCase : Any = False, __UpperCAmelCase : Any = 0.0, __UpperCAmelCase : Optional[int] = True, __UpperCAmelCase : List[str] = 0, __UpperCAmelCase : Tuple = 1, __UpperCAmelCase : Union[str, Any] = 2, **__UpperCAmelCase : Tuple, ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : List[str] = encoder_ffn_dim
SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_layers
SCREAMING_SNAKE_CASE : Tuple = num_encoder_attention_heads
SCREAMING_SNAKE_CASE : Any = decoder_ffn_dim
SCREAMING_SNAKE_CASE : str = num_decoder_layers
SCREAMING_SNAKE_CASE : Any = num_decoder_attention_heads
SCREAMING_SNAKE_CASE : int = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
SCREAMING_SNAKE_CASE : Dict = activation_function
# parameters for xlmprophetnet
SCREAMING_SNAKE_CASE : List[Any] = ngram
SCREAMING_SNAKE_CASE : List[Any] = num_buckets
SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
SCREAMING_SNAKE_CASE : int = disable_ngram_loss
SCREAMING_SNAKE_CASE : Union[str, Any] = eps
# 3 Types of Dropout
SCREAMING_SNAKE_CASE : Union[str, Any] = attention_dropout
SCREAMING_SNAKE_CASE : Dict = activation_dropout
SCREAMING_SNAKE_CASE : Any = dropout
SCREAMING_SNAKE_CASE : str = use_cache
super().__init__(
pad_token_id=lowerCAmelCase_, bos_token_id=lowerCAmelCase_, eos_token_id=lowerCAmelCase_, is_encoder_decoder=lowerCAmelCase_, add_cross_attention=lowerCAmelCase_, decoder_start_token_id=lowerCAmelCase_, **lowerCAmelCase_, )
@property
def lowercase__ (self : Any ) -> int:
"""simple docstring"""
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def lowercase__ (self : List[Any], __UpperCAmelCase : List[Any] ) -> Optional[Any]:
"""simple docstring"""
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 717 |
'''simple docstring'''
def __lowercase (_SCREAMING_SNAKE_CASE :int ):
SCREAMING_SNAKE_CASE : Tuple = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def __lowercase (_SCREAMING_SNAKE_CASE :int ):
SCREAMING_SNAKE_CASE : List[Any] = 0
while number > 0:
SCREAMING_SNAKE_CASE : List[str] = number % 10
sum_of_digits += last_digit
SCREAMING_SNAKE_CASE : List[str] = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def __lowercase (_SCREAMING_SNAKE_CASE :int = 1_00 ):
SCREAMING_SNAKE_CASE : List[Any] = factorial(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : List[str] = split_and_add(_SCREAMING_SNAKE_CASE )
return result
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 355 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'gpt_neox_japanese'
def __init__( self : Any , a_ : Optional[int]=3_2000 , a_ : List[Any]=2560 , a_ : Any=32 , a_ : Union[str, Any]=32 , a_ : Any=4 , a_ : List[Any]="gelu" , a_ : Optional[int]=1.00 , a_ : int=1_0000 , a_ : Optional[int]=2048 , a_ : str=0.02 , a_ : Any=1e-5 , a_ : Dict=True , a_ : Tuple=3_1996 , a_ : Any=3_1999 , a_ : str=0.1 , a_ : Optional[int]=0.0 , **a_ : List[Any] , )-> List[str]:
"""simple docstring"""
super().__init__(bos_token_id=a_ , eos_token_id=a_ , **a_ )
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Any = hidden_size
SCREAMING_SNAKE_CASE__ : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] = intermediate_multiple_size
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE__ : int = rotary_pct
SCREAMING_SNAKE_CASE__ : Tuple = rotary_emb_base
SCREAMING_SNAKE_CASE__ : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Tuple = use_cache
SCREAMING_SNAKE_CASE__ : str = attention_dropout
SCREAMING_SNAKE_CASE__ : int = hidden_dropout
| 85 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Any = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
a_ : Union[str, Any] = {
'vocab_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
},
'merges_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
},
'tokenizer_config_file': {
'facebook/blenderbot_small-90M': (
'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
)
},
}
a_ : List[Any] = {'facebook/blenderbot_small-90M': 5_12}
def __a ( __UpperCAmelCase ):
a__ = set()
a__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
a__ = char
a__ = set(__UpperCAmelCase )
return pairs
class __UpperCamelCase ( _lowercase ):
"""simple docstring"""
_lowercase : Union[str, Any] = VOCAB_FILES_NAMES
_lowercase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : int = ['''input_ids''', '''attention_mask''']
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="__start__" , SCREAMING_SNAKE_CASE="__end__" , SCREAMING_SNAKE_CASE="__unk__" , SCREAMING_SNAKE_CASE="__null__" , **SCREAMING_SNAKE_CASE , ) -> List[str]:
super().__init__(unk_token=SCREAMING_SNAKE_CASE , bos_token=SCREAMING_SNAKE_CASE , eos_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as vocab_handle:
a__ = json.load(SCREAMING_SNAKE_CASE )
a__ = {v: k for k, v in self.encoder.items()}
with open(SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as merges_handle:
a__ = merges_handle.read().split('''\n''' )[1:-1]
a__ = [tuple(merge.split() ) for merge in merges]
a__ = dict(zip(SCREAMING_SNAKE_CASE , range(len(SCREAMING_SNAKE_CASE ) ) ) )
a__ = {}
@property
def _UpperCAmelCase ( self ) -> int:
return len(self.encoder )
def _UpperCAmelCase ( self ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> str:
if token in self.cache:
return self.cache[token]
a__ = re.sub('''([.,!?()])''' , R''' \1''' , SCREAMING_SNAKE_CASE )
a__ = re.sub('''(\')''' , R''' \1 ''' , SCREAMING_SNAKE_CASE )
a__ = re.sub(R'''\s{2,}''' , ''' ''' , SCREAMING_SNAKE_CASE )
if "\n" in token:
a__ = token.replace('''\n''' , ''' __newln__''' )
a__ = token.split(''' ''' )
a__ = []
for token in tokens:
if not len(SCREAMING_SNAKE_CASE ):
continue
a__ = token.lower()
a__ = tuple(SCREAMING_SNAKE_CASE )
a__ = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
a__ = get_pairs(SCREAMING_SNAKE_CASE )
if not pairs:
words.append(SCREAMING_SNAKE_CASE )
continue
while True:
a__ = min(SCREAMING_SNAKE_CASE , key=lambda SCREAMING_SNAKE_CASE : self.bpe_ranks.get(SCREAMING_SNAKE_CASE , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
a__ , a__ = bigram
a__ = []
a__ = 0
while i < len(SCREAMING_SNAKE_CASE ):
try:
a__ = word.index(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
new_word.extend(word[i:j] )
a__ = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(SCREAMING_SNAKE_CASE ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
a__ = tuple(SCREAMING_SNAKE_CASE )
a__ = new_word
if len(SCREAMING_SNAKE_CASE ) == 1:
break
else:
a__ = get_pairs(SCREAMING_SNAKE_CASE )
a__ = '''@@ '''.join(SCREAMING_SNAKE_CASE )
a__ = word[:-4]
a__ = word
words.append(SCREAMING_SNAKE_CASE )
return " ".join(SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[str]:
a__ = []
a__ = re.findall(R'''\S+\n?''' , SCREAMING_SNAKE_CASE )
for token in words:
split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE ).split(''' ''' ) ) )
return split_tokens
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> int:
a__ = token.lower()
return self.encoder.get(SCREAMING_SNAKE_CASE , self.encoder.get(self.unk_token ) )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> str:
return self.decoder.get(SCREAMING_SNAKE_CASE , self.unk_token )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> str:
a__ = ''' '''.join(SCREAMING_SNAKE_CASE ).replace('''@@ ''' , '''''' ).strip()
return out_string
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
a__ = os.path.join(
SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
a__ = os.path.join(
SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=SCREAMING_SNAKE_CASE , ensure_ascii=SCREAMING_SNAKE_CASE ) + '''\n''' )
a__ = 0
with open(SCREAMING_SNAKE_CASE , '''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 SCREAMING_SNAKE_CASE : 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!''' )
a__ = token_index
writer.write(''' '''.join(SCREAMING_SNAKE_CASE ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 194 | 0 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCamelCase_ ( lowerCamelCase : ndarray ) -> float:
"""simple docstring"""
return np.dot(lowerCamelCase , lowerCamelCase )
class _UpperCamelCase :
"""simple docstring"""
def __init__( self : Any , *,
snake_case : float = np.inf , snake_case : str = "linear" , snake_case : float = 0.0 , ) -> None:
'''simple docstring'''
__magic_name__ : Optional[int] = regularization
__magic_name__ : Dict = gamma
if kernel == "linear":
__magic_name__ : Dict = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('''rbf kernel requires gamma''' )
if not isinstance(self.gamma , (float, int) ):
raise ValueError('''gamma must be float or int''' )
if not self.gamma > 0:
raise ValueError('''gamma must be > 0''' )
__magic_name__ : int = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
__magic_name__ : List[str] = f"""Unknown kernel: {kernel}"""
raise ValueError(snake_case )
def _UpperCAmelCase ( self : str , snake_case : ndarray , snake_case : ndarray ) -> float:
'''simple docstring'''
return np.dot(snake_case , snake_case )
def _UpperCAmelCase ( self : List[str] , snake_case : ndarray , snake_case : ndarray ) -> float:
'''simple docstring'''
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def _UpperCAmelCase ( self : Tuple , snake_case : list[ndarray] , snake_case : ndarray ) -> None:
'''simple docstring'''
__magic_name__ : List[str] = observations
__magic_name__ : List[Any] = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((__magic_name__) , ) : Union[str, Any] = np.shape(snake_case )
def to_minimize(snake_case : ndarray ) -> float:
__magic_name__ : Union[str, Any] = 0
((__magic_name__) , ) : Tuple = np.shape(snake_case )
for i in range(snake_case ):
for j in range(snake_case ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(snake_case )
__magic_name__ : Optional[Any] = LinearConstraint(snake_case , 0 , 0 )
__magic_name__ : Union[str, Any] = Bounds(0 , self.regularization )
__magic_name__ : Optional[Any] = minimize(
snake_case , np.ones(snake_case ) , bounds=snake_case , constraints=[ly_contraint] ).x
__magic_name__ : str = l_star
# calculating mean offset of separation plane to points
__magic_name__ : Any = 0
for i in range(snake_case ):
for j in range(snake_case ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
__magic_name__ : str = s / n
def _UpperCAmelCase ( self : str , snake_case : ndarray ) -> int:
'''simple docstring'''
__magic_name__ : str = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , snake_case )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 147 |
"""simple docstring"""
from math import loga
def UpperCamelCase_ ( lowerCamelCase : int ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(lowerCamelCase , lowerCamelCase ):
raise TypeError('''Input value must be a \'int\' type''' )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 147 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
lowercase_ : List[Any] = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
lowercase_ : Dict = 128
elif "12-12" in model_name:
lowercase_ : Optional[int] = 12
lowercase_ : Any = 12
elif "14-14" in model_name:
lowercase_ : Tuple = 14
lowercase_ : List[str] = 14
elif "16-16" in model_name:
lowercase_ : List[str] = 16
lowercase_ : Optional[Any] = 16
else:
raise ValueError("Model not supported" )
lowercase_ : Optional[Any] = 'huggingface/label-files'
if "speech-commands" in model_name:
lowercase_ : Optional[Any] = 35
lowercase_ : Optional[Any] = 'speech-commands-v2-id2label.json'
else:
lowercase_ : Union[str, Any] = 527
lowercase_ : Tuple = 'audioset-id2label.json'
lowercase_ : Optional[Any] = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type="dataset" ) , "r" ) )
lowercase_ : str = {int(__lowercase ): v for k, v in idalabel.items()}
lowercase_ : Dict = idalabel
lowercase_ : List[Any] = {v: k for k, v in idalabel.items()}
return config
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
if "module.v" in name:
lowercase_ : List[str] = name.replace("module.v" , "audio_spectrogram_transformer" )
if "cls_token" in name:
lowercase_ : Tuple = name.replace("cls_token" , "embeddings.cls_token" )
if "dist_token" in name:
lowercase_ : Tuple = name.replace("dist_token" , "embeddings.distillation_token" )
if "pos_embed" in name:
lowercase_ : Optional[Any] = name.replace("pos_embed" , "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
lowercase_ : Optional[int] = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
# transformer blocks
if "blocks" in name:
lowercase_ : str = name.replace("blocks" , "encoder.layer" )
if "attn.proj" in name:
lowercase_ : Optional[Any] = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
lowercase_ : Optional[Any] = name.replace("attn" , "attention.self" )
if "norm1" in name:
lowercase_ : Optional[int] = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
lowercase_ : str = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
lowercase_ : List[Any] = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
lowercase_ : Dict = name.replace("mlp.fc2" , "output.dense" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
lowercase_ : List[Any] = name.replace("audio_spectrogram_transformer.norm" , "audio_spectrogram_transformer.layernorm" )
# classifier head
if "module.mlp_head.0" in name:
lowercase_ : int = name.replace("module.mlp_head.0" , "classifier.layernorm" )
if "module.mlp_head.1" in name:
lowercase_ : Optional[int] = name.replace("module.mlp_head.1" , "classifier.dense" )
return name
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowercase_ : Any = orig_state_dict.pop(__lowercase )
if "qkv" in key:
lowercase_ : Optional[Any] = key.split("." )
lowercase_ : List[str] = int(key_split[3] )
lowercase_ : Optional[int] = config.hidden_size
if "weight" in key:
lowercase_ : Optional[int] = val[:dim, :]
lowercase_ : Union[str, Any] = val[dim : dim * 2, :]
lowercase_ : Dict = val[-dim:, :]
else:
lowercase_ : Optional[int] = val[:dim]
lowercase_ : Optional[int] = val[dim : dim * 2]
lowercase_ : Optional[int] = val[-dim:]
else:
lowercase_ : Any = val
return orig_state_dict
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Tuple = [
'module.v.head.weight',
'module.v.head.bias',
'module.v.head_dist.weight',
'module.v.head_dist.bias',
]
for k in ignore_keys:
state_dict.pop(__lowercase , __lowercase )
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=False ):
"""simple docstring"""
lowercase_ : Any = get_audio_spectrogram_transformer_config(__lowercase )
lowercase_ : Optional[int] = {
'ast-finetuned-audioset-10-10-0.4593': (
'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.450': (
'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448': (
'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
),
'ast-finetuned-audioset-10-10-0.448-v2': (
'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
),
'ast-finetuned-audioset-12-12-0.447': (
'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
),
'ast-finetuned-audioset-14-14-0.443': (
'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
),
'ast-finetuned-audioset-16-16-0.442': (
'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
),
'ast-finetuned-speech-commands-v2': (
'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
),
}
# load original state_dict
lowercase_ : Union[str, Any] = model_name_to_url[model_name]
lowercase_ : str = torch.hub.load_state_dict_from_url(__lowercase , map_location="cpu" )
# remove some keys
remove_keys(__lowercase )
# rename some keys
lowercase_ : Dict = convert_state_dict(__lowercase , __lowercase )
# load 🤗 model
lowercase_ : Optional[Any] = ASTForAudioClassification(__lowercase )
model.eval()
model.load_state_dict(__lowercase )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
lowercase_ : List[Any] = -4.2677393 if 'speech-commands' not in model_name else -6.845978
lowercase_ : Optional[Any] = 4.5689974 if 'speech-commands' not in model_name else 5.5654526
lowercase_ : Union[str, Any] = 1024 if 'speech-commands' not in model_name else 128
lowercase_ : Tuple = ASTFeatureExtractor(mean=__lowercase , std=__lowercase , max_length=__lowercase )
if "speech-commands" in model_name:
lowercase_ : Any = load_dataset("speech_commands" , "v0.02" , split="validation" )
lowercase_ : Optional[int] = dataset[0]['audio']['array']
else:
lowercase_ : Any = hf_hub_download(
repo_id="nielsr/audio-spectogram-transformer-checkpoint" , filename="sample_audio.flac" , repo_type="dataset" , )
lowercase_ : int = torchaudio.load(__lowercase )
lowercase_ : Any = waveform.squeeze().numpy()
lowercase_ : Dict = feature_extractor(__lowercase , sampling_rate=1_6000 , return_tensors="pt" )
# forward pass
lowercase_ : Optional[Any] = model(**__lowercase )
lowercase_ : List[Any] = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
lowercase_ : List[Any] = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
lowercase_ : Dict = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
lowercase_ : Any = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
lowercase_ : Optional[Any] = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
lowercase_ : int = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
lowercase_ : List[str] = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
lowercase_ : List[Any] = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
lowercase_ : int = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError("Unknown model name" )
if not torch.allclose(logits[0, :3] , __lowercase , atol=1e-4 ):
raise ValueError("Logits don\'t match" )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(__lowercase ).mkdir(exist_ok=__lowercase )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowercase )
print(F"""Saving feature extractor to {pytorch_dump_folder_path}""" )
feature_extractor.save_pretrained(__lowercase )
if push_to_hub:
print("Pushing model and feature extractor to the hub..." )
model.push_to_hub(F"""MIT/{model_name}""" )
feature_extractor.push_to_hub(F"""MIT/{model_name}""" )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
UpperCamelCase__ = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 620 | import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase = pytest.mark.integration
@pytest.mark.parametrize('path' ,['paws', 'csv'] )
def UpperCamelCase ( __lowercase : Optional[Any] ,__lowercase : Tuple ):
'''simple docstring'''
inspect_dataset(__lowercase ,__lowercase )
A_ : Optional[Any] = path + '.py'
assert script_name in os.listdir(__lowercase )
assert "__pycache__" not in os.listdir(__lowercase )
@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.parametrize('path' ,['accuracy'] )
def UpperCamelCase ( __lowercase : Any ,__lowercase : Union[str, Any] ):
'''simple docstring'''
inspect_metric(__lowercase ,__lowercase )
A_ : Optional[Any] = path + '.py'
assert script_name in os.listdir(__lowercase )
assert "__pycache__" not in os.listdir(__lowercase )
@pytest.mark.parametrize(
'path, config_name, expected_splits' ,[
('squad', 'plain_text', ['train', 'validation']),
('dalle-mini/wit', 'dalle-mini--wit', ['train']),
('paws', 'labeled_final', ['train', 'test', 'validation']),
] ,)
def UpperCamelCase ( __lowercase : List[str] ,__lowercase : Dict ,__lowercase : Dict ):
'''simple docstring'''
A_ : List[Any] = get_dataset_config_info(__lowercase ,config_name=__lowercase )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'path, config_name, expected_exception' ,[
('paws', None, ValueError),
] ,)
def UpperCamelCase ( __lowercase : Dict ,__lowercase : List[Any] ,__lowercase : int ):
'''simple docstring'''
with pytest.raises(__lowercase ):
get_dataset_config_info(__lowercase ,config_name=__lowercase )
@pytest.mark.parametrize(
'path, expected' ,[
('squad', 'plain_text'),
('acronym_identification', 'default'),
('lhoestq/squad', 'plain_text'),
('lhoestq/test', 'default'),
('lhoestq/demo1', 'lhoestq--demo1'),
('dalle-mini/wit', 'dalle-mini--wit'),
] ,)
def UpperCamelCase ( __lowercase : str ,__lowercase : str ):
'''simple docstring'''
A_ : Any = get_dataset_config_names(__lowercase )
assert expected in config_names
@pytest.mark.parametrize(
'path, expected_configs, expected_splits_in_first_config' ,[
('squad', ['plain_text'], ['train', 'validation']),
('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
] ,)
def UpperCamelCase ( __lowercase : Tuple ,__lowercase : str ,__lowercase : Optional[Any] ):
'''simple docstring'''
A_ : Optional[int] = get_dataset_infos(__lowercase )
assert list(infos.keys() ) == expected_configs
A_ : Any = expected_configs[0]
assert expected_config in infos
A_ : Tuple = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
'path, expected_config, expected_splits' ,[
('squad', 'plain_text', ['train', 'validation']),
('dalle-mini/wit', 'dalle-mini--wit', ['train']),
('paws', 'labeled_final', ['train', 'test', 'validation']),
] ,)
def UpperCamelCase ( __lowercase : Any ,__lowercase : Optional[Any] ,__lowercase : Dict ):
'''simple docstring'''
A_ : Optional[Any] = get_dataset_infos(__lowercase )
assert expected_config in infos
A_ : str = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'path, config_name, expected_exception' ,[
('paws', None, ValueError),
] ,)
def UpperCamelCase ( __lowercase : Tuple ,__lowercase : Tuple ,__lowercase : str ):
'''simple docstring'''
with pytest.raises(__lowercase ):
get_dataset_split_names(__lowercase ,config_name=__lowercase )
| 558 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
A_ = {"target_lang": "fi", "source_lang": "en"}
A_ = ">>zh<<"
A_ = "Helsinki-NLP/"
if is_torch_available():
A_ = "pt"
elif is_tf_available():
A_ = "tf"
else:
A_ = "jax"
@require_sentencepiece
class _snake_case ( _a , unittest.TestCase ):
_A : Any = MarianTokenizer
_A : int = False
_A : Union[str, Any] = True
def __UpperCamelCase ( self : Union[str, Any] ):
super().setUp()
SCREAMING_SNAKE_CASE:Any = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
SCREAMING_SNAKE_CASE:Union[str, Any] = dict(zip(SCREAMING_SNAKE_CASE__ ,range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
SCREAMING_SNAKE_CASE:str = Path(self.tmpdirname )
save_json(SCREAMING_SNAKE_CASE__ ,save_dir / VOCAB_FILES_NAMES["vocab"] )
save_json(SCREAMING_SNAKE_CASE__ ,save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(SCREAMING_SNAKE_CASE__ ,save_dir / VOCAB_FILES_NAMES["source_spm"] )
copyfile(SCREAMING_SNAKE_CASE__ ,save_dir / VOCAB_FILES_NAMES["target_spm"] )
SCREAMING_SNAKE_CASE:Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCamelCase ( self : Any ,**SCREAMING_SNAKE_CASE__ : List[str] ):
return MarianTokenizer.from_pretrained(self.tmpdirname ,**SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : List[Any] ):
return (
"This is a test",
"This is a test",
)
def __UpperCamelCase ( self : Dict ):
SCREAMING_SNAKE_CASE:Optional[int] = "</s>"
SCREAMING_SNAKE_CASE:Tuple = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__ ) ,SCREAMING_SNAKE_CASE__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__ ) ,SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Tuple ):
SCREAMING_SNAKE_CASE:List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"</s>" )
self.assertEqual(vocab_keys[1] ,"<unk>" )
self.assertEqual(vocab_keys[-1] ,"<pad>" )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) ,9 )
def __UpperCamelCase ( self : int ):
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __UpperCamelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE:Optional[Any] = MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de''' )
SCREAMING_SNAKE_CASE:Union[str, Any] = en_de_tokenizer(["I am a small frog"] ,return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Any = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(SCREAMING_SNAKE_CASE__ ,batch.input_ids[0] )
SCREAMING_SNAKE_CASE:Tuple = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Optional[Any] = [x.name for x in Path(SCREAMING_SNAKE_CASE__ ).glob("*" )]
self.assertIn("source.spm" ,SCREAMING_SNAKE_CASE__ )
MarianTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Dict ):
SCREAMING_SNAKE_CASE:List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE:Dict = tok(
["I am a small frog" * 1_000, "I am a small frog"] ,padding=SCREAMING_SNAKE_CASE__ ,truncation=SCREAMING_SNAKE_CASE__ ,return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __UpperCamelCase ( self : Optional[int] ):
SCREAMING_SNAKE_CASE:Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE:Tuple = tok(["I am a tiny frog", "I am a small frog"] ,padding=SCREAMING_SNAKE_CASE__ ,return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __UpperCamelCase ( self : str ):
# fmt: off
SCREAMING_SNAKE_CASE:List[str] = {"input_ids": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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], [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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE__ ,model_name="Helsinki-NLP/opus-mt-en-de" ,revision="1a8c2263da11e68e50938f97e10cd57820bd504c" ,decode_kwargs={"use_source_tokenizer": True} ,)
def __UpperCamelCase ( self : List[str] ):
SCREAMING_SNAKE_CASE:Union[str, Any] = MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs" )
SCREAMING_SNAKE_CASE:int = "Tämä on testi"
SCREAMING_SNAKE_CASE:str = "This is a test"
SCREAMING_SNAKE_CASE:int = [76, 7, 2_047, 2]
SCREAMING_SNAKE_CASE:Dict = [69, 12, 11, 940, 2]
SCREAMING_SNAKE_CASE:Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids
self.assertListEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Dict = tokenizer(text_target=SCREAMING_SNAKE_CASE__ ).input_ids
self.assertListEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Dict = tokenizer.decode(SCREAMING_SNAKE_CASE__ ,skip_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
| 715 |
'''simple docstring'''
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
A_ = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
"models/graphormer/algos_graphormer.pyx",
]
def A_ ( snake_case ):
# Test all the extensions added in the setup
for file in FILES_TO_FIND:
if not (transformers_path / file).exists():
return False
return True
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
A_ = parser.parse_args()
if args.check_lib:
A_ = importlib.import_module("transformers")
A_ = Path(transformers_module.__file__).parent
else:
A_ = Path.cwd() / "build/lib/transformers"
if not test_custom_files_are_present(transformers_path):
raise ValueError("The built release does not contain the custom files. Fix this before going further!")
| 465 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE_ = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 523 | '''simple docstring'''
def UpperCamelCase__ ( _lowercase : list ) -> list:
if len(_lowercase ) < 2:
return collection
def circle_sort_util(_lowercase : list , _lowercase : int , _lowercase : int ) -> bool:
__UpperCAmelCase: Tuple = False
if low == high:
return swapped
__UpperCAmelCase: int = low
__UpperCAmelCase: int = high
while left < right:
if collection[left] > collection[right]:
__UpperCAmelCase, __UpperCAmelCase: List[str] = (
collection[right],
collection[left],
)
__UpperCAmelCase: Optional[int] = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
__UpperCAmelCase, __UpperCAmelCase: str = (
collection[right + 1],
collection[left],
)
__UpperCAmelCase: Union[str, Any] = True
__UpperCAmelCase: List[str] = low + int((high - low) / 2 )
__UpperCAmelCase: Optional[int] = circle_sort_util(_lowercase , _lowercase , _lowercase )
__UpperCAmelCase: List[Any] = circle_sort_util(_lowercase , mid + 1 , _lowercase )
return swapped or left_swap or right_swap
__UpperCAmelCase: str = True
while is_not_sorted is True:
__UpperCAmelCase: Dict = circle_sort_util(_lowercase , 0 , len(_lowercase ) - 1 )
return collection
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE_ = [int(item) for item in user_input.split(',')]
print(circle_sort(unsorted)) | 523 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase__ ( __lowercase ):
'''simple docstring'''
UpperCamelCase = ["""image_processor"""]
UpperCamelCase = """SamImageProcessor"""
def __init__( self : List[str] , a_ : int ):
'''simple docstring'''
super().__init__(_A )
__UpperCAmelCase : Dict = self.image_processor
__UpperCAmelCase : List[str] = -10
__UpperCAmelCase : Any = self.image_processor.size['''longest_edge''']
def __call__( self : Optional[Any] , a_ : Tuple=None , a_ : List[Any]=None , a_ : Dict=None , a_ : Any=None , a_ : Optional[Union[str, TensorType]] = None , **a_ : List[str] , ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.image_processor(
_A , return_tensors=_A , **_A , )
# pop arguments that are not used in the foward but used nevertheless
__UpperCAmelCase : Optional[Any] = encoding_image_processor['''original_sizes''']
if hasattr(_A , '''numpy''' ): # Checks if Torch or TF tensor
__UpperCAmelCase : str = original_sizes.numpy()
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Dict = self._check_and_preprocess_points(
input_points=_A , input_labels=_A , input_boxes=_A , )
__UpperCAmelCase : Dict = self._normalize_and_convert(
_A , _A , input_points=_A , input_labels=_A , input_boxes=_A , return_tensors=_A , )
return encoding_image_processor
def snake_case__ ( self : str , a_ : List[Any] , a_ : Optional[int] , a_ : Union[str, Any]=None , a_ : Optional[int]=None , a_ : Optional[Any]=None , a_ : Dict="pt" , ):
'''simple docstring'''
if input_points is not None:
if len(_A ) != len(_A ):
__UpperCAmelCase : Dict = [
self._normalize_coordinates(self.target_size , _A , original_sizes[0] ) for point in input_points
]
else:
__UpperCAmelCase : int = [
self._normalize_coordinates(self.target_size , _A , _A )
for point, original_size in zip(_A , _A )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
__UpperCAmelCase , __UpperCAmelCase : List[Any] = self._pad_points_and_labels(_A , _A )
__UpperCAmelCase : Optional[Any] = np.array(_A )
if input_labels is not None:
__UpperCAmelCase : Any = np.array(_A )
if input_boxes is not None:
if len(_A ) != len(_A ):
__UpperCAmelCase : List[str] = [
self._normalize_coordinates(self.target_size , _A , original_sizes[0] , is_bounding_box=_A )
for box in input_boxes
]
else:
__UpperCAmelCase : Optional[Any] = [
self._normalize_coordinates(self.target_size , _A , _A , is_bounding_box=_A )
for box, original_size in zip(_A , _A )
]
__UpperCAmelCase : List[str] = np.array(_A )
if input_boxes is not None:
if return_tensors == "pt":
__UpperCAmelCase : Optional[Any] = torch.from_numpy(_A )
# boxes batch size of 1 by default
__UpperCAmelCase : Optional[Any] = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
__UpperCAmelCase : List[str] = tf.convert_to_tensor(_A )
# boxes batch size of 1 by default
__UpperCAmelCase : Optional[int] = tf.expand_dims(_A , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'''input_boxes''': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
__UpperCAmelCase : Optional[Any] = torch.from_numpy(_A )
# point batch size of 1 by default
__UpperCAmelCase : Any = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
__UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor(_A )
# point batch size of 1 by default
__UpperCAmelCase : List[str] = tf.expand_dims(_A , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'''input_points''': input_points} )
if input_labels is not None:
if return_tensors == "pt":
__UpperCAmelCase : Any = torch.from_numpy(_A )
# point batch size of 1 by default
__UpperCAmelCase : Any = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
__UpperCAmelCase : int = tf.convert_to_tensor(_A )
# point batch size of 1 by default
__UpperCAmelCase : List[str] = tf.expand_dims(_A , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'''input_labels''': input_labels} )
return encoding_image_processor
def snake_case__ ( self : Any , a_ : str , a_ : Dict ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = max([point.shape[0] for point in input_points] )
__UpperCAmelCase : int = []
for i, point in enumerate(_A ):
if point.shape[0] != expected_nb_points:
__UpperCAmelCase : int = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
__UpperCAmelCase : Union[str, Any] = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(_A )
__UpperCAmelCase : Tuple = processed_input_points
return input_points, input_labels
def snake_case__ ( self : List[Any] , a_ : int , a_ : np.ndarray , a_ : Any , a_ : Dict=False ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : List[str] = original_size
__UpperCAmelCase , __UpperCAmelCase : int = self.image_processor._get_preprocess_shape(_A , longest_edge=_A )
__UpperCAmelCase : Dict = deepcopy(_A ).astype(_A )
if is_bounding_box:
__UpperCAmelCase : Union[str, Any] = coords.reshape(-1 , 2 , 2 )
__UpperCAmelCase : Union[str, Any] = coords[..., 0] * (new_w / old_w)
__UpperCAmelCase : str = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
__UpperCAmelCase : str = coords.reshape(-1 , 4 )
return coords
def snake_case__ ( self : Any , a_ : Optional[int]=None , a_ : List[str]=None , a_ : Optional[int]=None , ):
'''simple docstring'''
if input_points is not None:
if hasattr(_A , '''numpy''' ): # Checks for TF or Torch tensor
__UpperCAmelCase : Tuple = input_points.numpy().tolist()
if not isinstance(_A , _A ) or not isinstance(input_points[0] , _A ):
raise ValueError('''Input points must be a list of list of floating points.''' )
__UpperCAmelCase : Optional[Any] = [np.array(_A ) for input_point in input_points]
else:
__UpperCAmelCase : List[str] = None
if input_labels is not None:
if hasattr(_A , '''numpy''' ):
__UpperCAmelCase : str = input_labels.numpy().tolist()
if not isinstance(_A , _A ) or not isinstance(input_labels[0] , _A ):
raise ValueError('''Input labels must be a list of list integers.''' )
__UpperCAmelCase : Tuple = [np.array(_A ) for label in input_labels]
else:
__UpperCAmelCase : Union[str, Any] = None
if input_boxes is not None:
if hasattr(_A , '''numpy''' ):
__UpperCAmelCase : List[str] = input_boxes.numpy().tolist()
if (
not isinstance(_A , _A )
or not isinstance(input_boxes[0] , _A )
or not isinstance(input_boxes[0][0] , _A )
):
raise ValueError('''Input boxes must be a list of list of list of floating points.''' )
__UpperCAmelCase : Tuple = [np.array(_A ).astype(np.floataa ) for box in input_boxes]
else:
__UpperCAmelCase : Optional[Any] = None
return input_points, input_labels, input_boxes
@property
def snake_case__ ( self : List[str] ):
'''simple docstring'''
__UpperCAmelCase : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(_A ) )
def snake_case__ ( self : List[Any] , *a_ : Union[str, Any] , **a_ : Optional[Any] ):
'''simple docstring'''
return self.image_processor.post_process_masks(*_A , **_A )
| 705 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__A =logging.get_logger(__name__)
def a ( ):
'''simple docstring'''
__UpperCAmelCase : str = os.getenv('''SM_HP_MP_PARAMETERS''' , '''{}''' )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
__UpperCAmelCase : Any = json.loads(_UpperCAmelCase )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
__UpperCAmelCase : Tuple = os.getenv('''SM_FRAMEWORK_PARAMS''' , '''{}''' )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
__UpperCAmelCase : List[Any] = json.loads(_UpperCAmelCase )
if not mpi_options.get('''sagemaker_mpi_enabled''' , _UpperCAmelCase ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec('''smdistributed''' ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = field(
default="""""" ,metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""} ,)
def snake_case__ ( self : List[Any] ):
'''simple docstring'''
super().__post_init__()
warnings.warn(
'''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
'''`TrainingArguments` instead.''' , a_ , )
@cached_property
def snake_case__ ( self : Optional[int] ):
'''simple docstring'''
logger.info('''PyTorch: setting up devices''' )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
'''torch.distributed process group is initialized, but local_rank == -1. '''
'''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch''' )
if self.no_cuda:
__UpperCAmelCase : List[str] = torch.device('''cpu''' )
__UpperCAmelCase : Dict = 0
elif is_sagemaker_model_parallel_available():
__UpperCAmelCase : Optional[Any] = smp.local_rank()
__UpperCAmelCase : int = torch.device('''cuda''' , a_ )
__UpperCAmelCase : List[str] = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend='''smddp''' , timeout=self.ddp_timeout_delta )
__UpperCAmelCase : List[str] = int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK''' ) )
__UpperCAmelCase : Optional[int] = torch.device('''cuda''' , self.local_rank )
__UpperCAmelCase : Union[str, Any] = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
__UpperCAmelCase : Any = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
__UpperCAmelCase : Dict = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend='''nccl''' , timeout=self.ddp_timeout_delta )
__UpperCAmelCase : Optional[Any] = torch.device('''cuda''' , self.local_rank )
__UpperCAmelCase : Tuple = 1
if device.type == "cuda":
torch.cuda.set_device(a_ )
return device
@property
def snake_case__ ( self : str ):
'''simple docstring'''
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def snake_case__ ( self : List[str] ):
'''simple docstring'''
return not is_sagemaker_model_parallel_available()
@property
def snake_case__ ( self : Dict ):
'''simple docstring'''
return False
| 241 | 0 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __snake_case :
def __init__( self : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = ''''''
_lowerCamelCase : str = ''''''
_lowerCamelCase : Any = []
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase : Tuple = 2_5_6
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : str = 0
_lowerCamelCase : Any = 0
_lowerCamelCase : Dict = 0
def SCREAMING_SNAKE_CASE ( self : int , __lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = cva.imread(__lowerCAmelCase , 0 )
_lowerCamelCase : Tuple = copy.deepcopy(self.img )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Tuple = plt.hist(self.img.ravel() , 2_5_6 , [0, 2_5_6] , label='''x''' )
_lowerCamelCase : Dict = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
_lowerCamelCase : Tuple = x[i] / self.k
self.sk += prk
_lowerCamelCase : str = (self.L - 1) * self.sk
if self.rem != 0:
_lowerCamelCase : List[Any] = int(last % last )
_lowerCamelCase : Optional[Any] = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = int(np.ma.count(self.img ) / self.img[1].size )
_lowerCamelCase : Optional[int] = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
_lowerCamelCase : int = self.img[j][i]
if num != self.last_list[num]:
_lowerCamelCase : Optional[int] = self.last_list[num]
cva.imwrite('''output_data/output.jpg''' , self.img )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
plt.hist(self.img.ravel() , 2_5_6 , [0, 2_5_6] )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
cva.imshow('''Output-Image''' , self.img )
cva.imshow('''Input-Image''' , self.original_image )
cva.waitKey(5_0_0_0 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowerCAmelCase__ = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
lowerCAmelCase__ = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 83 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
a_ = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
class A_:
"""simple docstring"""
a_ : str
a_ : Optional[str] = None
a_ : Optional[Union[str, int]] = None
a_ : Optional[Union[str, int]] = None
a_ : Optional[Union[str, int]] = None
def _lowerCAmelCase ( self ):
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : List[str] = _str_to_version_tuple(self.version_str )
def __repr__( self ):
return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
@property
def _lowerCAmelCase ( self ):
return self.major, self.minor, self.patch
def _lowerCAmelCase ( self , A ):
if isinstance(A , A ):
return Version(A )
elif isinstance(A , A ):
return other
raise TypeError(F"{other} (type {type(A )}) cannot be compared to version." )
def __eq__( self , A ):
try:
_lowerCamelCase : Dict = self._validate_operand(A )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self , A ):
_lowerCamelCase : Any = self._validate_operand(A )
return self.tuple < other.tuple
def __hash__( self ):
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def _lowerCAmelCase ( cls , A ):
_lowerCamelCase : Optional[Any] = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def _lowerCAmelCase ( self ):
return self.version_str
def UpperCAmelCase_ ( __a : Any ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = _VERSION_REG.match(__a )
if not res:
raise ValueError(f"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits." )
return tuple(int(__a ) for v in [res.group('major' ), res.group('minor' ), res.group('patch' )] )
def UpperCAmelCase_ ( __a : List[str] ):
'''simple docstring'''
return ".".join(str(__a ) for v in version_tuple )
| 437 | 0 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
__snake_case , __snake_case , __snake_case =False, False, False
@dataclass
class UpperCAmelCase_ :
lowerCamelCase : Optional[int] = None
lowerCamelCase : bool = True
lowerCamelCase : bool = True
lowerCamelCase : Optional[str] = None
# Automatically constructed
lowerCamelCase : ClassVar[str] = "dict"
lowerCamelCase : ClassVar[Any] = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
lowerCamelCase : str = field(default='''Audio''' , init=__lowercase , repr=__lowercase )
def __call__( self : Dict ) -> Dict:
return self.pa_type
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Union[str, bytes, dict] ) -> dict:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('To support encoding audio data, please install \'soundfile\'.' ) from err
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return {"bytes": None, "path": value}
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
lowerCAmelCase = BytesIO()
sf.write(UpperCAmelCase__ , value['array'] , value['sampling_rate'] , format='wav' )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get('path' ) is not None and os.path.isfile(value['path'] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith('pcm' ):
# "PCM" only has raw audio bytes
if value.get('sampling_rate' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object' )
if value.get('bytes' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
lowerCAmelCase = np.frombuffer(value['bytes'] , dtype=np.intaa ).astype(np.floataa ) / 3_2_7_6_7
else:
lowerCAmelCase = np.memmap(value['path'] , dtype='h' , mode='r' ).astype(np.floataa ) / 3_2_7_6_7
lowerCAmelCase = BytesIO(bytes() )
sf.write(UpperCAmelCase__ , UpperCAmelCase__ , value['sampling_rate'] , format='wav' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('path' )}
elif value.get('bytes' ) is not None or value.get('path' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('bytes' ), "path": value.get('path' )}
else:
raise ValueError(
F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def __UpperCAmelCase ( self : Tuple , UpperCAmelCase__ : dict , UpperCAmelCase__ : Optional[Dict[str, Union[str, bool, None]]] = None ) -> dict:
if not self.decode:
raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.' )
lowerCAmelCase , lowerCAmelCase = (value['path'], BytesIO(value['bytes'] )) if value['bytes'] is not None else (value['path'], None)
if path is None and file is None:
raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.' ) from err
lowerCAmelCase = xsplitext(UpperCAmelCase__ )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ' )
if file is None:
lowerCAmelCase = token_per_repo_id or {}
lowerCAmelCase = path.split('::' )[-1]
try:
lowerCAmelCase = string_to_dict(UpperCAmelCase__ , config.HUB_DATASETS_URL )['repo_id']
lowerCAmelCase = token_per_repo_id[repo_id]
except (ValueError, KeyError):
lowerCAmelCase = None
with xopen(UpperCAmelCase__ , 'rb' , use_auth_token=UpperCAmelCase__ ) as f:
lowerCAmelCase , lowerCAmelCase = sf.read(UpperCAmelCase__ )
else:
lowerCAmelCase , lowerCAmelCase = sf.read(UpperCAmelCase__ )
lowerCAmelCase = array.T
if self.mono:
lowerCAmelCase = librosa.to_mono(UpperCAmelCase__ )
if self.sampling_rate and self.sampling_rate != sampling_rate:
lowerCAmelCase = librosa.resample(UpperCAmelCase__ , orig_sr=UpperCAmelCase__ , target_sr=self.sampling_rate )
lowerCAmelCase = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def __UpperCAmelCase ( self : Any ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
if self.decode:
raise ValueError('Cannot flatten a decoded Audio feature.' )
return {
"bytes": Value('binary' ),
"path": Value('string' ),
}
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Union[pa.StringArray, pa.StructArray] ) -> pa.StructArray:
if pa.types.is_string(storage.type ):
lowerCAmelCase = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.binary() )
lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, storage] , ['bytes', 'path'] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowerCAmelCase = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.string() )
lowerCAmelCase = pa.StructArray.from_arrays([storage, path_array] , ['bytes', 'path'] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('array' ):
lowerCAmelCase = pa.array([Audio().encode_example(UpperCAmelCase__ ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('bytes' ) >= 0:
lowerCAmelCase = storage.field('bytes' )
else:
lowerCAmelCase = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.binary() )
if storage.type.get_field_index('path' ) >= 0:
lowerCAmelCase = storage.field('path' )
else:
lowerCAmelCase = pa.array([None] * len(UpperCAmelCase__ ) , type=pa.string() )
lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ['bytes', 'path'] , mask=storage.is_null() )
return array_cast(UpperCAmelCase__ , self.pa_type )
def __UpperCAmelCase ( self : List[Any] , UpperCAmelCase__ : pa.StructArray ) -> pa.StructArray:
@no_op_if_value_is_null
def path_to_bytes(UpperCAmelCase__ : Any ):
with xopen(UpperCAmelCase__ , 'rb' ) as f:
lowerCAmelCase = f.read()
return bytes_
lowerCAmelCase = pa.array(
[
(path_to_bytes(x['path'] ) if x['bytes'] is None else x['bytes']) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowerCAmelCase = pa.array(
[os.path.basename(UpperCAmelCase__ ) if path is not None else None for path in storage.field('path' ).to_pylist()] , type=pa.string() , )
lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ['bytes', 'path'] , mask=bytes_array.is_null() )
return array_cast(UpperCAmelCase__ , self.pa_type )
| 703 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : int = '''canine'''
def __init__( self : Union[str, Any] , UpperCAmelCase__ : int=7_6_8 , UpperCAmelCase__ : Dict=1_2 , UpperCAmelCase__ : Optional[int]=1_2 , UpperCAmelCase__ : List[str]=3_0_7_2 , UpperCAmelCase__ : Any="gelu" , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : List[Any]=0.1 , UpperCAmelCase__ : Union[str, Any]=1_6_3_8_4 , UpperCAmelCase__ : int=1_6 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : Dict=1E-12 , UpperCAmelCase__ : List[Any]=0 , UpperCAmelCase__ : List[str]=0XE_0_0_0 , UpperCAmelCase__ : Union[str, Any]=0XE_0_0_1 , UpperCAmelCase__ : Union[str, Any]=4 , UpperCAmelCase__ : Union[str, Any]=4 , UpperCAmelCase__ : List[Any]=8 , UpperCAmelCase__ : List[str]=1_6_3_8_4 , UpperCAmelCase__ : Union[str, Any]=1_2_8 , **UpperCAmelCase__ : Dict , ) -> Tuple:
super().__init__(pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = initializer_range
lowerCAmelCase = type_vocab_size
lowerCAmelCase = layer_norm_eps
# Character config:
lowerCAmelCase = downsampling_rate
lowerCAmelCase = upsampling_kernel_size
lowerCAmelCase = num_hash_functions
lowerCAmelCase = num_hash_buckets
lowerCAmelCase = local_transformer_stride
| 513 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
class UpperCAmelCase_ ( _UpperCamelCase ):
'''simple docstring'''
A : Union[str, Any] = 'timm_backbone'
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ) -> Dict:
super().__init__(**lowerCAmelCase_ )
snake_case_ : Tuple = backbone
snake_case_ : str = num_channels
snake_case_ : Tuple = features_only
snake_case_ : Optional[Any] = use_pretrained_backbone
snake_case_ : Union[str, Any] = True
snake_case_ : List[Any] = out_indices if out_indices is not None else (-1,)
| 568 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Union[str, Any] ) -> List[str]:
__lowerCAmelCase = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCAmelCase_ ) )
def lowercase ( self : str ) -> Any:
__lowerCAmelCase = [
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCAmelCase_ ) )
def lowercase ( self : Tuple ) -> Optional[int]:
__lowerCAmelCase = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowerCAmelCase_ ) )
def lowercase ( self : List[Any] ) -> List[str]:
__lowerCAmelCase = [
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
self.assertTrue(is_safetensors_compatible(lowerCAmelCase_ ) )
def lowercase ( self : List[Any] ) -> int:
__lowerCAmelCase = [
'safety_checker/pytorch_model.bin',
'safety_checker/model.safetensors',
'vae/diffusion_pytorch_model.bin',
'vae/diffusion_pytorch_model.safetensors',
'text_encoder/pytorch_model.bin',
# Removed: 'text_encoder/model.safetensors',
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowerCAmelCase_ ) )
def lowercase ( self : str ) -> str:
__lowerCAmelCase = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
__lowerCAmelCase = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCAmelCase_ , variant=lowerCAmelCase_ ) )
def lowercase ( self : str ) -> List[Any]:
__lowerCAmelCase = [
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
__lowerCAmelCase = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCAmelCase_ , variant=lowerCAmelCase_ ) )
def lowercase ( self : str ) -> List[str]:
# pass variant but use the non-variant filenames
__lowerCAmelCase = [
'unet/diffusion_pytorch_model.bin',
'unet/diffusion_pytorch_model.safetensors',
]
__lowerCAmelCase = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCAmelCase_ , variant=lowerCAmelCase_ ) )
def lowercase ( self : str ) -> Union[str, Any]:
__lowerCAmelCase = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
__lowerCAmelCase = 'fp16'
self.assertFalse(is_safetensors_compatible(lowerCAmelCase_ , variant=lowerCAmelCase_ ) )
def lowercase ( self : str ) -> List[Any]:
__lowerCAmelCase = [
'text_encoder/pytorch_model.fp16.bin',
'text_encoder/model.fp16.safetensors',
]
__lowerCAmelCase = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCAmelCase_ , variant=lowerCAmelCase_ ) )
def lowercase ( self : List[str] ) -> List[Any]:
# pass variant but use the non-variant filenames
__lowerCAmelCase = [
'text_encoder/pytorch_model.bin',
'text_encoder/model.safetensors',
]
__lowerCAmelCase = 'fp16'
self.assertTrue(is_safetensors_compatible(lowerCAmelCase_ , variant=lowerCAmelCase_ ) )
def lowercase ( self : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase = [
'safety_checker/pytorch_model.fp16.bin',
'safety_checker/model.fp16.safetensors',
'vae/diffusion_pytorch_model.fp16.bin',
'vae/diffusion_pytorch_model.fp16.safetensors',
'text_encoder/pytorch_model.fp16.bin',
# 'text_encoder/model.fp16.safetensors',
'unet/diffusion_pytorch_model.fp16.bin',
'unet/diffusion_pytorch_model.fp16.safetensors',
]
__lowerCAmelCase = 'fp16'
self.assertFalse(is_safetensors_compatible(lowerCAmelCase_ , variant=lowerCAmelCase_ ) )
| 53 | 0 |
'''simple docstring'''
def _a ( __lowerCAmelCase : int ):
"""simple docstring"""
return str(__lowerCAmelCase ) == str(__lowerCAmelCase )[::-1]
def _a ( __lowerCAmelCase : int ):
"""simple docstring"""
return int(__lowerCAmelCase ) + int(str(__lowerCAmelCase )[::-1] )
def _a ( __lowerCAmelCase : int = 1_00_00 ):
"""simple docstring"""
snake_case__ : Union[str, Any] = []
for num in range(1 , __lowerCAmelCase ):
snake_case__ : Optional[int] = 0
snake_case__ : Dict = num
while iterations < 50:
snake_case__ : List[Any] = sum_reverse(__lowerCAmelCase )
iterations += 1
if is_palindrome(__lowerCAmelCase ):
break
else:
lychrel_nums.append(__lowerCAmelCase )
return len(__lowerCAmelCase )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 502 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] ):
"""simple docstring"""
snake_case__ : Any = BigBirdConfig.from_json_file(__lowerCAmelCase )
print(F"""Building PyTorch model from configuration: {config}""" )
if is_trivia_qa:
snake_case__ : str = BigBirdForQuestionAnswering(__lowerCAmelCase )
else:
snake_case__ : Tuple = BigBirdForPreTraining(__lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(__lowerCAmelCase , __lowerCAmelCase , is_trivia_qa=__lowerCAmelCase )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
lowerCAmelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--big_bird_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
)
lowerCAmelCase__ : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 502 | 1 |
def lowerCamelCase_ ( UpperCAmelCase__ = 100 ):
"""simple docstring"""
a_ = n * (n + 1) * (2 * n + 1) / 6
a_ = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''') | 483 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return " ".join(
''.join(word[::-1] ) if len(_snake_case ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 7 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
_snake_case : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ =['''pixel_values''']
def __init__( self, _a = True, _a = None, _a = PILImageResampling.BILINEAR, _a = True, _a = None, _a = True, _a = 1 / 2_55, _a = True, _a = None, _a = None, **_a, ) -> None:
super().__init__(**A_ )
__SCREAMING_SNAKE_CASE = size if size is not None else {"shortest_edge": 2_56}
__SCREAMING_SNAKE_CASE = get_size_dict(A_, default_to_square=A_ )
__SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
__SCREAMING_SNAKE_CASE = get_size_dict(A_, param_name="crop_size" )
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = size
__SCREAMING_SNAKE_CASE = resample
__SCREAMING_SNAKE_CASE = do_center_crop
__SCREAMING_SNAKE_CASE = crop_size
__SCREAMING_SNAKE_CASE = do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__SCREAMING_SNAKE_CASE = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowerCAmelCase ( self, _a, _a, _a = PILImageResampling.BICUBIC, _a = None, **_a, ) -> np.ndarray:
__SCREAMING_SNAKE_CASE = get_size_dict(A_, default_to_square=A_ )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
__SCREAMING_SNAKE_CASE = get_resize_output_image_size(A_, size=size["shortest_edge"], default_to_square=A_ )
return resize(A_, size=A_, resample=A_, data_format=A_, **A_ )
def __lowerCAmelCase ( self, _a, _a, _a = None, **_a, ) -> np.ndarray:
__SCREAMING_SNAKE_CASE = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''' )
return center_crop(A_, size=(size["height"], size["width"]), data_format=A_, **A_ )
def __lowerCAmelCase ( self, _a, _a, _a = None, **_a ) -> np.ndarray:
return rescale(A_, scale=A_, data_format=A_, **A_ )
def __lowerCAmelCase ( self, _a, _a, _a, _a = None, **_a, ) -> np.ndarray:
return normalize(A_, mean=A_, std=A_, data_format=A_, **A_ )
def __lowerCAmelCase ( self, _a, _a = None, _a = None, _a = None, _a = None, _a = None, _a = None, _a = None, _a = None, _a = None, _a = None, _a = None, _a = ChannelDimension.FIRST, **_a, ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
__SCREAMING_SNAKE_CASE = size if size is not None else self.size
__SCREAMING_SNAKE_CASE = get_size_dict(A_, default_to_square=A_ )
__SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
__SCREAMING_SNAKE_CASE = do_center_crop if do_center_crop is not None else self.do_center_crop
__SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else self.crop_size
__SCREAMING_SNAKE_CASE = get_size_dict(A_, param_name="crop_size" )
__SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
__SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
__SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
__SCREAMING_SNAKE_CASE = make_list_of_images(A_ )
if not valid_images(A_ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
__SCREAMING_SNAKE_CASE = [to_numpy_array(A_ ) for image in images]
if do_resize:
__SCREAMING_SNAKE_CASE = [self.resize(image=A_, size=A_, resample=A_ ) for image in images]
if do_center_crop:
__SCREAMING_SNAKE_CASE = [self.center_crop(image=A_, size=A_ ) for image in images]
if do_rescale:
__SCREAMING_SNAKE_CASE = [self.rescale(image=A_, scale=A_ ) for image in images]
if do_normalize:
__SCREAMING_SNAKE_CASE = [self.normalize(image=A_, mean=A_, std=A_ ) for image in images]
__SCREAMING_SNAKE_CASE = [to_channel_dimension_format(A_, A_ ) for image in images]
__SCREAMING_SNAKE_CASE = {"pixel_values": images}
return BatchFeature(data=A_, tensor_type=A_ )
def __lowerCAmelCase ( self, _a, _a = None ) -> Any:
__SCREAMING_SNAKE_CASE = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(A_ ) != len(A_ ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(A_ ):
__SCREAMING_SNAKE_CASE = target_sizes.numpy()
__SCREAMING_SNAKE_CASE = []
for idx in range(len(A_ ) ):
__SCREAMING_SNAKE_CASE = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ), size=target_sizes[idx], mode="bilinear", align_corners=A_ )
__SCREAMING_SNAKE_CASE = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(A_ )
else:
__SCREAMING_SNAKE_CASE = logits.argmax(dim=1 )
__SCREAMING_SNAKE_CASE = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 705 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class __SCREAMING_SNAKE_CASE :
SCREAMING_SNAKE_CASE__ =LEDConfig
SCREAMING_SNAKE_CASE__ ={}
SCREAMING_SNAKE_CASE__ ="""gelu"""
def __init__( self, _a, _a=13, _a=7, _a=True, _a=False, _a=99, _a=32, _a=2, _a=4, _a=37, _a=0.1, _a=0.1, _a=20, _a=2, _a=1, _a=0, _a=4, ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = seq_length
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = eos_token_id
__SCREAMING_SNAKE_CASE = pad_token_id
__SCREAMING_SNAKE_CASE = bos_token_id
__SCREAMING_SNAKE_CASE = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
__SCREAMING_SNAKE_CASE = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
__SCREAMING_SNAKE_CASE = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def __lowerCAmelCase ( self ) -> List[str]:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size )
__SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ), 1 )
__SCREAMING_SNAKE_CASE = tf.concat([input_ids, eos_tensor], axis=1 )
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
__SCREAMING_SNAKE_CASE = self.config_cls(
vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, attention_window=self.attention_window, **self.config_updates, )
__SCREAMING_SNAKE_CASE = prepare_led_inputs_dict(_a, _a, _a )
__SCREAMING_SNAKE_CASE = tf.concat(
[tf.zeros_like(_a )[:, :-1], tf.ones_like(_a )[:, -1:]], axis=-1, )
__SCREAMING_SNAKE_CASE = global_attention_mask
return config, inputs_dict
def __lowerCAmelCase ( self, _a, _a ) -> List[str]:
__SCREAMING_SNAKE_CASE = TFLEDModel(config=_a ).get_decoder()
__SCREAMING_SNAKE_CASE = inputs_dict["input_ids"]
__SCREAMING_SNAKE_CASE = input_ids[:1, :]
__SCREAMING_SNAKE_CASE = inputs_dict["attention_mask"][:1, :]
__SCREAMING_SNAKE_CASE = 1
# first forward pass
__SCREAMING_SNAKE_CASE = model(_a, attention_mask=_a, use_cache=_a )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3), config.vocab_size )
__SCREAMING_SNAKE_CASE = tf.cast(ids_tensor((self.batch_size, 3), 2 ), tf.inta )
# append to next input_ids and
__SCREAMING_SNAKE_CASE = tf.concat([input_ids, next_tokens], axis=-1 )
__SCREAMING_SNAKE_CASE = tf.concat([attention_mask, next_attn_mask], axis=-1 )
__SCREAMING_SNAKE_CASE = model(_a, attention_mask=_a )[0]
__SCREAMING_SNAKE_CASE = model(_a, attention_mask=_a, past_key_values=_a )[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1] )
# select random slice
__SCREAMING_SNAKE_CASE = int(ids_tensor((1,), output_from_past.shape[-1] ) )
__SCREAMING_SNAKE_CASE = output_from_no_past[:, -3:, random_slice_idx]
__SCREAMING_SNAKE_CASE = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_a, _a, rtol=1E-3 )
def _A ( __snake_case :Any , __snake_case :Dict , __snake_case :List[Any] , __snake_case :List[Any]=None , __snake_case :Optional[Any]=None , __snake_case :Any=None , __snake_case :List[str]=None , ) -> List[str]:
"""simple docstring"""
if attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.cast(tf.math.not_equal(__snake_case , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ =(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
SCREAMING_SNAKE_CASE__ =(TFLEDForConditionalGeneration,) if is_tf_available() else ()
SCREAMING_SNAKE_CASE__ =(
{
"""conversational""": TFLEDForConditionalGeneration,
"""feature-extraction""": TFLEDModel,
"""summarization""": TFLEDForConditionalGeneration,
"""text2text-generation""": TFLEDForConditionalGeneration,
"""translation""": TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE__ =True
SCREAMING_SNAKE_CASE__ =False
SCREAMING_SNAKE_CASE__ =False
SCREAMING_SNAKE_CASE__ =False
def __lowerCAmelCase ( self ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = TFLEDModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self, config_class=_a )
def __lowerCAmelCase ( self ) -> Any:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Any:
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_a )
def __lowerCAmelCase ( self ) -> List[str]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = tf.zeros_like(inputs_dict["attention_mask"] )
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices, 1, inputs_dict["global_attention_mask"], )
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = self.model_tester.seq_length
__SCREAMING_SNAKE_CASE = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(_a ):
__SCREAMING_SNAKE_CASE = outputs.decoder_attentions
self.assertEqual(len(_a ), self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, seq_length, seq_length], )
def check_encoder_attentions_output(_a ):
__SCREAMING_SNAKE_CASE = [t.numpy() for t in outputs.encoder_attentions]
__SCREAMING_SNAKE_CASE = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(_a ), self.model_tester.num_hidden_layers )
self.assertEqual(len(_a ), self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, seq_length, seq_length], )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices], )
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = model_class(_a )
__SCREAMING_SNAKE_CASE = model(self._prepare_for_class(_a, _a ) )
__SCREAMING_SNAKE_CASE = len(_a )
self.assertEqual(config.output_hidden_states, _a )
check_encoder_attentions_output(_a )
if self.is_encoder_decoder:
__SCREAMING_SNAKE_CASE = model_class(_a )
__SCREAMING_SNAKE_CASE = model(self._prepare_for_class(_a, _a ) )
self.assertEqual(config.output_hidden_states, _a )
check_decoder_attentions_output(_a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = model_class(_a )
__SCREAMING_SNAKE_CASE = model(self._prepare_for_class(_a, _a ) )
self.assertEqual(config.output_hidden_states, _a )
check_encoder_attentions_output(_a )
# Check attention is always last and order is fine
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = model_class(_a )
__SCREAMING_SNAKE_CASE = model(self._prepare_for_class(_a, _a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(_a ) )
self.assertEqual(model.config.output_hidden_states, _a )
check_encoder_attentions_output(_a )
@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing." )
def __lowerCAmelCase ( self ) -> Tuple:
pass
def __lowerCAmelCase ( self ) -> Optional[int]:
# TODO: Head-masking not yet implement
pass
def _A ( __snake_case :Optional[int] ) -> List[Any]:
"""simple docstring"""
return tf.constant(__snake_case , dtype=tf.intaa )
_snake_case : int = 1e-4
@slow
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> List[Any]:
__SCREAMING_SNAKE_CASE = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" ).led
# change to intended input here
__SCREAMING_SNAKE_CASE = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
__SCREAMING_SNAKE_CASE = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
__SCREAMING_SNAKE_CASE = prepare_led_inputs_dict(model.config, _a, _a )
__SCREAMING_SNAKE_CASE = model(**_a )[0]
__SCREAMING_SNAKE_CASE = (1, 10_24, 7_68)
self.assertEqual(output.shape, _a )
# change to expected output here
__SCREAMING_SNAKE_CASE = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]], )
tf.debugging.assert_near(output[:, :3, :3], _a, atol=1E-3 )
def __lowerCAmelCase ( self ) -> List[str]:
__SCREAMING_SNAKE_CASE = TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384" )
# change to intended input here
__SCREAMING_SNAKE_CASE = _long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
__SCREAMING_SNAKE_CASE = _long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]] )
__SCREAMING_SNAKE_CASE = prepare_led_inputs_dict(model.config, _a, _a )
__SCREAMING_SNAKE_CASE = model(**_a )[0]
__SCREAMING_SNAKE_CASE = (1, 10_24, model.config.vocab_size)
self.assertEqual(output.shape, _a )
# change to expected output here
__SCREAMING_SNAKE_CASE = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]], )
tf.debugging.assert_near(output[:, :3, :3], _a, atol=1E-3, rtol=1E-3 )
| 214 | 0 |
from math import factorial
def __snake_case ( _lowerCAmelCase : int = 100 ) -> int:
return sum(map(_lowerCAmelCase , str(factorial(_lowerCAmelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 454 |
from timeit import timeit
_lowerCAmelCase : Tuple = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def __snake_case ( _lowerCAmelCase : str ) -> bool:
A_ : List[str] = 0
A_ : str = len(_lowerCAmelCase ) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def __snake_case ( _lowerCAmelCase : str ) -> bool:
A_ : int = len(_lowerCAmelCase ) // 2
A_ : Union[str, Any] = len(_lowerCAmelCase )
# We need to traverse till half of the length of string
# as we can get access of the i'th last element from
# i'th index.
# eg: [0,1,2,3,4,5] => 4th index can be accessed
# with the help of 1st index (i==n-i-1)
# where n is length of string
return all(s[i] == s[n - i - 1] for i in range(_lowerCAmelCase ) )
def __snake_case ( _lowerCAmelCase : str ) -> bool:
if len(_lowerCAmelCase ) <= 2:
return True
if s[0] == s[len(_lowerCAmelCase ) - 1]:
return is_palindrome_recursive(s[1:-1] )
else:
return False
def __snake_case ( _lowerCAmelCase : str ) -> bool:
return s == s[::-1]
def __snake_case ( _lowerCAmelCase : str ) -> None:
A_ : int = f"all({name}(key) is value for key, value in test_data.items())"
A_ : List[str] = f"from __main__ import test_data, {name}"
A_ : str = 500000
A_ : List[str] = timeit(stmt=_lowerCAmelCase , setup=_lowerCAmelCase , number=_lowerCAmelCase )
print(f"{name:<35} finished {number:,} runs in {result:.5f} seconds" )
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(F'''{key:21} {value}''')
print('''a man a plan a canal panama''')
# finished 500,000 runs in 0.46793 seconds
benchmark_function('''is_palindrome_slice''')
# finished 500,000 runs in 0.85234 seconds
benchmark_function('''is_palindrome''')
# finished 500,000 runs in 1.32028 seconds
benchmark_function('''is_palindrome_recursive''')
# finished 500,000 runs in 2.08679 seconds
benchmark_function('''is_palindrome_traversal''')
| 454 | 1 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
lowerCAmelCase__ = logging.get_logger(__name__)
@add_end_docstrings(__lowercase )
class snake_case ( __lowercase ):
def __init__(self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
requires_backends(self , '''vision''' )
self.check_model_type(SCREAMING_SNAKE_CASE_ )
def __call__(self , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
return super().__call__(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def _lowercase (self , **SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
return {}, {}, {}
def _lowercase (self , SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = load_image(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = image.size
SCREAMING_SNAKE_CASE_ = self.image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors=self.framework )
return model_inputs
def _lowercase (self , SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.model(**SCREAMING_SNAKE_CASE_ )
return model_outputs
def _lowercase (self , SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = model_outputs.predicted_depth
SCREAMING_SNAKE_CASE_ = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = prediction.squeeze().cpu().numpy()
SCREAMING_SNAKE_CASE_ = (output * 2_55 / np.max(SCREAMING_SNAKE_CASE_ )).astype('''uint8''' )
SCREAMING_SNAKE_CASE_ = Image.fromarray(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = predicted_depth
SCREAMING_SNAKE_CASE_ = depth
return output_dict | 628 |
"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class snake_case ( __lowercase ):
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = 8
# DPR tok
SCREAMING_SNAKE_CASE_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = os.path.join(SCREAMING_SNAKE_CASE_ , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
SCREAMING_SNAKE_CASE_ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
SCREAMING_SNAKE_CASE_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
SCREAMING_SNAKE_CASE_ = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
SCREAMING_SNAKE_CASE_ = {'''unk_token''': '''<unk>'''}
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = os.path.join(SCREAMING_SNAKE_CASE_ , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
SCREAMING_SNAKE_CASE_ = os.path.join(SCREAMING_SNAKE_CASE_ , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_ ) )
def _lowercase (self ):
"""simple docstring"""
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def _lowercase (self ):
"""simple docstring"""
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def _lowercase (self ):
"""simple docstring"""
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def _lowercase (self ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.get_dummy_dataset()
SCREAMING_SNAKE_CASE_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
SCREAMING_SNAKE_CASE_ = dataset
SCREAMING_SNAKE_CASE_ = RagRetriever(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def _lowercase (self , SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.get_dummy_dataset()
SCREAMING_SNAKE_CASE_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , '''dataset''' )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
SCREAMING_SNAKE_CASE_ = RagRetriever(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
SCREAMING_SNAKE_CASE_ = RagRetriever(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , SCREAMING_SNAKE_CASE_ ) , )
return retriever
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
SCREAMING_SNAKE_CASE_ = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(SCREAMING_SNAKE_CASE_ , open(SCREAMING_SNAKE_CASE_ , '''wb''' ) )
SCREAMING_SNAKE_CASE_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
SCREAMING_SNAKE_CASE_ = RagRetriever(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = self.get_dummy_canonical_hf_index_retriever()
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
SCREAMING_SNAKE_CASE_ = self.get_dummy_dataset()
retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=1 )
self.assertTrue(out is not None )
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=1 )
self.assertTrue(out is not None )
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=1 )
self.assertTrue(out is not None )
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = self.get_dummy_legacy_index_retriever()
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def _lowercase (self ):
"""simple docstring"""
import torch
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = self.get_dummy_canonical_hf_index_retriever()
SCREAMING_SNAKE_CASE_ = [[5, 7], [10, 11]]
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = retriever(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
SCREAMING_SNAKE_CASE_ = retriever(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' , )
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def _lowercase (self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self.get_dpr_ctx_encoder_tokenizer()
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = [[5, 7], [10, 11]]
SCREAMING_SNAKE_CASE_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
SCREAMING_SNAKE_CASE_ = retriever(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(
len(SCREAMING_SNAKE_CASE_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , SCREAMING_SNAKE_CASE_ ) # check for doc token related keys in dictionary. | 628 | 1 |
"""simple docstring"""
from __future__ import annotations
a = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
a = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def lowercase (snake_case__ : list[float] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase = []
lowerCAmelCase = len(__A )
for i in range(__A ):
lowerCAmelCase = -1
for j in range(i + 1 , __A ):
if arr[i] < arr[j]:
lowerCAmelCase = arr[j]
break
result.append(__A )
return result
def lowercase (snake_case__ : list[float] ) -> str:
'''simple docstring'''
lowerCAmelCase = []
for i, outer in enumerate(__A ):
lowerCAmelCase = -1
for inner in arr[i + 1 :]:
if outer < inner:
lowerCAmelCase = inner
break
result.append(__A )
return result
def lowercase (snake_case__ : list[float] ) -> str:
'''simple docstring'''
lowerCAmelCase = len(__A )
lowerCAmelCase = []
lowerCAmelCase = [-1] * arr_size
for index in reversed(range(__A ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
lowerCAmelCase = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
a = (
'from __main__ import arr, next_greatest_element_slow, '
'next_greatest_element_fast, next_greatest_element'
)
print(
'next_greatest_element_slow():',
timeit('next_greatest_element_slow(arr)', setup=setup),
)
print(
'next_greatest_element_fast():',
timeit('next_greatest_element_fast(arr)', setup=setup),
)
print(
' next_greatest_element():',
timeit('next_greatest_element(arr)', setup=setup),
)
| 169 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ConsistencyModelPipeline
__UpperCamelCase = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
__UpperCamelCase = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
__UpperCamelCase = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
] )
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[Any] = UNetaDModel.from_pretrained(
'diffusers/consistency-models-test' , subfolder='test_unet' , )
return unet
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = UNetaDModel.from_pretrained(
'diffusers/consistency-models-test' , subfolder='test_unet_class_cond' , )
return unet
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=False ):
'''simple docstring'''
if class_cond:
snake_case: Optional[int] = self.dummy_cond_unet
else:
snake_case: List[str] = self.dummy_uncond_unet
# Default to CM multistep sampler
snake_case: Dict = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_02 , sigma_max=80.0 , )
snake_case: int = {
'unet': unet,
'scheduler': scheduler,
}
return components
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ):
'''simple docstring'''
if str(SCREAMING_SNAKE_CASE__ ).startswith('mps' ):
snake_case: Optional[int] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
snake_case: Union[str, Any] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = {
'batch_size': 1,
'num_inference_steps': None,
'timesteps': [22, 0],
'generator': generator,
'output_type': 'np',
}
return inputs
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case: Any = self.get_dummy_components()
snake_case: List[str] = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
snake_case: List[Any] = image[0, -3:, -3:, -1]
snake_case: List[Any] = np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case: Optional[Any] = self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
snake_case: str = 0
snake_case: List[Any] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
snake_case: Dict = image[0, -3:, -3:, -1]
snake_case: int = np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case: Optional[Any] = self.get_dummy_components()
snake_case: Optional[int] = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
snake_case: Any = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
snake_case: str = 1
snake_case: Dict = None
snake_case: int = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
snake_case: Dict = image[0, -3:, -3:, -1]
snake_case: Tuple = np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
snake_case: Dict = self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__ )
snake_case: Any = pipe.to(SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = 1
snake_case: List[str] = None
snake_case: Optional[Any] = 0
snake_case: Union[str, Any] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 32, 32, 3)
snake_case: str = image[0, -3:, -3:, -1]
snake_case: str = np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__="cpu" , SCREAMING_SNAKE_CASE__=torch.floataa , SCREAMING_SNAKE_CASE__=(1, 3, 64, 64) ):
'''simple docstring'''
snake_case: Optional[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = {
'num_inference_steps': None,
'timesteps': [22, 0],
'class_labels': 0,
'generator': generator,
'output_type': 'np',
}
if get_fixed_latents:
snake_case: str = self.get_fixed_latents(seed=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ , dtype=SCREAMING_SNAKE_CASE__ , shape=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = latents
return inputs
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__="cpu" , SCREAMING_SNAKE_CASE__=torch.floataa , SCREAMING_SNAKE_CASE__=(1, 3, 64, 64) ):
'''simple docstring'''
if type(SCREAMING_SNAKE_CASE__ ) == str:
snake_case: Dict = torch.device(SCREAMING_SNAKE_CASE__ )
snake_case: Dict = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
snake_case: Union[str, Any] = randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ , dtype=SCREAMING_SNAKE_CASE__ )
return latents
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = UNetaDModel.from_pretrained('diffusers/consistency_models' , subfolder='diffusers_cd_imagenet64_l2' )
snake_case: str = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_02 , sigma_max=80.0 , )
snake_case: Union[str, Any] = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.get_inputs()
snake_case: List[str] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
snake_case: Optional[int] = image[0, -3:, -3:, -1]
snake_case: List[Any] = np.array([0.08_88, 0.08_81, 0.06_66, 0.04_79, 0.02_92, 0.01_95, 0.02_01, 0.01_63, 0.02_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = UNetaDModel.from_pretrained('diffusers/consistency_models' , subfolder='diffusers_cd_imagenet64_l2' )
snake_case: Any = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_02 , sigma_max=80.0 , )
snake_case: Any = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
snake_case: List[str] = self.get_inputs()
snake_case: List[Any] = 1
snake_case: Union[str, Any] = None
snake_case: str = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
snake_case: Dict = image[0, -3:, -3:, -1]
snake_case: int = np.array([0.03_40, 0.01_52, 0.00_63, 0.02_67, 0.02_21, 0.01_07, 0.04_16, 0.01_86, 0.02_17] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
@require_torch_a
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = UNetaDModel.from_pretrained('diffusers/consistency_models' , subfolder='diffusers_cd_imagenet64_l2' )
snake_case: Optional[Any] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_02 , sigma_max=80.0 , )
snake_case: Tuple = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
snake_case: str = self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__ , enable_math=SCREAMING_SNAKE_CASE__ , enable_mem_efficient=SCREAMING_SNAKE_CASE__ ):
snake_case: Union[str, Any] = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
snake_case: Optional[Any] = image[0, -3:, -3:, -1]
snake_case: Optional[Any] = np.array([0.18_75, 0.14_28, 0.12_89, 0.21_51, 0.20_92, 0.14_77, 0.18_77, 0.16_41, 0.13_53] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@require_torch_a
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = UNetaDModel.from_pretrained('diffusers/consistency_models' , subfolder='diffusers_cd_imagenet64_l2' )
snake_case: Optional[int] = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.0_02 , sigma_max=80.0 , )
snake_case: Tuple = ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__ , scheduler=SCREAMING_SNAKE_CASE__ )
pipe.to(torch_device=SCREAMING_SNAKE_CASE__ , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
snake_case: int = 1
snake_case: Optional[Any] = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__ , enable_math=SCREAMING_SNAKE_CASE__ , enable_mem_efficient=SCREAMING_SNAKE_CASE__ ):
snake_case: Dict = pipe(**SCREAMING_SNAKE_CASE__ ).images
assert image.shape == (1, 64, 64, 3)
snake_case: Tuple = image[0, -3:, -3:, -1]
snake_case: Union[str, Any] = np.array([0.16_63, 0.19_48, 0.22_75, 0.16_80, 0.12_04, 0.12_45, 0.18_58, 0.13_38, 0.20_95] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 | 329 | 0 |
from typing import Any
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : list ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
a_ = [input_list.count(UpperCamelCase ) for value in input_list]
a_ = max(UpperCamelCase ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(UpperCamelCase ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod() | 703 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCamelCase_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=224 , _SCREAMING_SNAKE_CASE=1000 , _SCREAMING_SNAKE_CASE=[3, 3, 6, 4] , _SCREAMING_SNAKE_CASE=[48, 56, 112, 220] , ):
a_ = parent
a_ = batch_size
a_ = num_channels
a_ = is_training
a_ = use_labels
a_ = hidden_dropout_prob
a_ = attention_probs_dropout_prob
a_ = num_labels
a_ = image_size
a_ = layer_depths
a_ = embed_dims
def __magic_name__ ( self ):
a_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a_ = None
if self.use_labels:
a_ = ids_tensor([self.batch_size] , self.num_labels )
a_ = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self ):
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act="""gelu""" , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_SCREAMING_SNAKE_CASE , layer_scale_init_value=1E-5 , )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = SwiftFormerModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
a_ = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = self.num_labels
a_ = SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
a_ = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
a_ = SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
a_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a_ = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self ):
((a_) , (a_) , (a_)) = self.prepare_config_and_inputs()
a_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
_lowerCamelCase : List[Any] = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
_lowerCamelCase : Optional[Any] = (
{"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Dict = False
_lowerCamelCase : Any = False
_lowerCamelCase : Tuple = False
_lowerCamelCase : List[Any] = False
def __magic_name__ ( self ):
a_ = SwiftFormerModelTester(self )
a_ = ConfigTester(
self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def __magic_name__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""" )
def __magic_name__ ( self ):
pass
def __magic_name__ ( self ):
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = model_class(_SCREAMING_SNAKE_CASE )
a_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE , nn.Linear ) )
def __magic_name__ ( self ):
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = model_class(_SCREAMING_SNAKE_CASE )
a_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a_ = [*signature.parameters.keys()]
a_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE )
@slow
def __magic_name__ ( self ):
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ = SwiftFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@unittest.skip(reason="""SwiftFormer does not output attentions""" )
def __magic_name__ ( self ):
pass
def __magic_name__ ( self ):
def check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
a_ = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
a_ = outputs.hidden_states
a_ = 8
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = True
check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a_ = True
check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
def _config_zero_init(_SCREAMING_SNAKE_CASE ):
a_ = copy.deepcopy(_SCREAMING_SNAKE_CASE )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 1E-10 )
if isinstance(getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ):
a_ = _config_zero_init(getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return configs_no_init
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
a_ = _config_zero_init(_SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
a_ = model_class(config=_SCREAMING_SNAKE_CASE )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __magic_name__ ( self ):
pass
def __SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
"""simple docstring"""
a_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self ):
return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs""" ) if is_vision_available() else None
@slow
def __magic_name__ ( self ):
a_ = SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""" ).to(_SCREAMING_SNAKE_CASE )
a_ = self.default_image_processor
a_ = prepare_img()
a_ = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" ).to(_SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
a_ = model(**_SCREAMING_SNAKE_CASE )
# verify the logits
a_ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE )
a_ = torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]] ).to(_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) ) | 403 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class A ( unittest.TestCase ):
def lowerCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_lowerCamelCase : Any ='laion/clap-htsat-unfused'
_lowerCamelCase : str =tempfile.mkdtemp()
def lowerCamelCase ( self : Union[str, Any] , **lowercase_ : List[Any] ) -> Tuple:
"""simple docstring"""
return RobertaTokenizer.from_pretrained(self.checkpoint , **lowercase_ )
def lowerCamelCase ( self : List[str] , **lowercase_ : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **lowercase_ )
def lowerCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : str =self.get_tokenizer()
_lowerCamelCase : Optional[Any] =self.get_feature_extractor()
_lowerCamelCase : Tuple =ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
processor.save_pretrained(self.tmpdirname )
_lowerCamelCase : int =ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowercase_ )
def lowerCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_lowerCamelCase : Optional[Any] =ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
_lowerCamelCase : Dict =self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
_lowerCamelCase : List[str] =self.get_feature_extractor(do_normalize=lowercase_ , padding_value=1.0 )
_lowerCamelCase : Optional[int] =ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=lowercase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowercase_ )
def lowerCamelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase : List[str] =self.get_feature_extractor()
_lowerCamelCase : List[Any] =self.get_tokenizer()
_lowerCamelCase : str =ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
_lowerCamelCase : Optional[int] =floats_list((3, 1000) )
_lowerCamelCase : Union[str, Any] =feature_extractor(lowercase_ , return_tensors='np' )
_lowerCamelCase : List[str] =processor(audios=lowercase_ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
_lowerCamelCase : Any =self.get_feature_extractor()
_lowerCamelCase : Any =self.get_tokenizer()
_lowerCamelCase : int =ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
_lowerCamelCase : int ='This is a test string'
_lowerCamelCase : Optional[int] =processor(text=lowercase_ )
_lowerCamelCase : str =tokenizer(lowercase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase ( self : Tuple ) -> Any:
"""simple docstring"""
_lowerCamelCase : List[str] =self.get_feature_extractor()
_lowerCamelCase : int =self.get_tokenizer()
_lowerCamelCase : int =ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
_lowerCamelCase : Dict =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowerCamelCase : Dict =processor.batch_decode(lowercase_ )
_lowerCamelCase : Optional[Any] =tokenizer.batch_decode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def lowerCamelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
_lowerCamelCase : Tuple =self.get_feature_extractor()
_lowerCamelCase : Tuple =self.get_tokenizer()
_lowerCamelCase : Tuple =ClapProcessor(tokenizer=lowercase_ , feature_extractor=lowercase_ )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
| 464 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}
class A ( UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase__ : str ='focalnet'
def __init__( self : Optional[int] , lowercase_ : List[Any]=224 , lowercase_ : str=4 , lowercase_ : Any=3 , lowercase_ : Dict=96 , lowercase_ : List[str]=False , lowercase_ : Any=[192, 384, 768, 768] , lowercase_ : Dict=[2, 2, 6, 2] , lowercase_ : Optional[int]=[2, 2, 2, 2] , lowercase_ : List[Any]=[3, 3, 3, 3] , lowercase_ : Any="gelu" , lowercase_ : str=4.0 , lowercase_ : List[Any]=0.0 , lowercase_ : Tuple=0.1 , lowercase_ : Optional[Any]=False , lowercase_ : Optional[int]=1E-4 , lowercase_ : Dict=False , lowercase_ : Optional[Any]=False , lowercase_ : Dict=False , lowercase_ : List[Any]=0.02 , lowercase_ : Any=1E-5 , lowercase_ : Any=32 , lowercase_ : List[str]=None , lowercase_ : int=None , **lowercase_ : Optional[Any] , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**lowercase_ )
_lowerCamelCase : Union[str, Any] =image_size
_lowerCamelCase : str =patch_size
_lowerCamelCase : str =num_channels
_lowerCamelCase : Tuple =embed_dim
_lowerCamelCase : Dict =use_conv_embed
_lowerCamelCase : int =hidden_sizes
_lowerCamelCase : str =depths
_lowerCamelCase : int =focal_levels
_lowerCamelCase : List[Any] =focal_windows
_lowerCamelCase : List[Any] =hidden_act
_lowerCamelCase : int =mlp_ratio
_lowerCamelCase : Any =hidden_dropout_prob
_lowerCamelCase : Optional[Any] =drop_path_rate
_lowerCamelCase : int =use_layerscale
_lowerCamelCase : str =layerscale_value
_lowerCamelCase : Dict =use_post_layernorm
_lowerCamelCase : str =use_post_layernorm_in_modulation
_lowerCamelCase : List[Any] =normalize_modulator
_lowerCamelCase : Optional[Any] =initializer_range
_lowerCamelCase : List[str] =layer_norm_eps
_lowerCamelCase : Optional[int] =encoder_stride
_lowerCamelCase : List[str] =['stem'] + [F'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
_lowerCamelCase , _lowerCamelCase : Union[str, Any] =get_aligned_output_features_output_indices(
out_features=lowercase_ , out_indices=lowercase_ , stage_names=self.stage_names )
| 464 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : str , lowerCAmelCase__ : int = 1_6 , lowerCAmelCase__ : int = 8_8 , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : float = 0.0 , lowerCAmelCase__ : int = 3_2 , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : str = "geglu" , lowerCAmelCase__ : Optional[int] = None , ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[int] = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=lowerCAmelCase__ , attention_head_dim=lowerCAmelCase__ , in_channels=lowerCAmelCase__ , num_layers=lowerCAmelCase__ , dropout=lowerCAmelCase__ , norm_num_groups=lowerCAmelCase__ , cross_attention_dim=lowerCAmelCase__ , attention_bias=lowerCAmelCase__ , sample_size=lowerCAmelCase__ , num_vector_embeds=lowerCAmelCase__ , activation_fn=lowerCAmelCase__ , num_embeds_ada_norm=lowerCAmelCase__ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
__SCREAMING_SNAKE_CASE : int = [7_7, 2_5_7]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
__SCREAMING_SNAKE_CASE : Tuple = [1, 0]
def UpperCamelCase__ ( self : Any , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple=None , lowerCAmelCase__ : Tuple=None , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : bool = True , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = hidden_states
__SCREAMING_SNAKE_CASE : List[Any] = []
__SCREAMING_SNAKE_CASE : str = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
__SCREAMING_SNAKE_CASE : Tuple = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
__SCREAMING_SNAKE_CASE : Optional[Any] = self.transformer_index_for_condition[i]
__SCREAMING_SNAKE_CASE : Optional[int] = self.transformers[transformer_index](
lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , timestep=lowerCAmelCase__ , cross_attention_kwargs=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
__SCREAMING_SNAKE_CASE : str = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
__SCREAMING_SNAKE_CASE : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=lowerCAmelCase__ )
| 706 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def lowerCAmelCase_ ( _lowerCamelCase: Dict , _lowerCamelCase: List[Any] ):
# ===== initialization =====
__SCREAMING_SNAKE_CASE : Optional[int] = Mock()
__SCREAMING_SNAKE_CASE : Optional[int] = conn, Mock()
__SCREAMING_SNAKE_CASE : str = iter([1, None] )
__SCREAMING_SNAKE_CASE : Any = lambda _lowerCamelCase : next(_lowerCamelCase )
# ===== invoke =====
send_file(filename="""mytext.txt""" , testing=_lowerCamelCase )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once() | 178 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Tuple , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple=1_3 , _lowerCAmelCase : Any=3_2 , _lowerCAmelCase : Dict=3 , _lowerCAmelCase : Tuple=4 , _lowerCAmelCase : Dict=[1_0, 2_0, 3_0, 4_0] , _lowerCAmelCase : Optional[int]=[2, 2, 3, 2] , _lowerCAmelCase : List[Any]=True , _lowerCAmelCase : Any=True , _lowerCAmelCase : str=3_7 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : List[Any]=1_0 , _lowerCAmelCase : str=0.02 , _lowerCAmelCase : Any=["stage2", "stage3", "stage4"] , _lowerCAmelCase : Union[str, Any]=[2, 3, 4] , _lowerCAmelCase : Optional[int]=None , ) -> Optional[int]:
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = num_channels
snake_case_ = num_stages
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_labels
snake_case_ = initializer_range
snake_case_ = out_features
snake_case_ = out_indices
snake_case_ = scope
def lowerCAmelCase__ ( self : List[Any] ) -> int:
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCAmelCase__ ( self : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
snake_case_ = ConvNextModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
snake_case_ = model(_lowerCAmelCase )
# 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 // 3_2, self.image_size // 3_2) , )
def lowerCAmelCase__ ( self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ = ConvNextForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
snake_case_ = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self : Dict , _lowerCAmelCase : str , _lowerCAmelCase : Any , _lowerCAmelCase : str ) -> List[Any]:
"""simple docstring"""
snake_case_ = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
snake_case_ = model(_lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
snake_case_ = None
snake_case_ = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
snake_case_ = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def lowerCAmelCase__ ( self : Tuple ) -> int:
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( a , a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def lowerCAmelCase__ ( self : Optional[int] ) -> int:
"""simple docstring"""
snake_case_ = ConvNextModelTester(self )
snake_case_ = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=3_7 )
def lowerCAmelCase__ ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNext does not use inputs_embeds" )
def lowerCAmelCase__ ( self : List[Any] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNext does not support input and output embeddings" )
def lowerCAmelCase__ ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNext does not use feedforward chunking" )
def lowerCAmelCase__ ( self : Dict ) -> int:
"""simple docstring"""
pass
def lowerCAmelCase__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(_lowerCAmelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def lowerCAmelCase__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def lowerCAmelCase__ ( self : List[Any] ) -> int:
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
def lowerCAmelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : int ):
snake_case_ = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
snake_case_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case_ = self.model_tester.num_stages
self.assertEqual(len(_lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase__ ( self : Dict ) -> Dict:
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def lowerCAmelCase__ ( self : Any ) -> Dict:
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = ConvNextModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _lowerCAmelCase ( )->List[Any]:
'''simple docstring'''
snake_case_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCAmelCase__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224" ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
snake_case_ = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224" ).to(_lowerCAmelCase )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=_lowerCAmelCase , return_tensors="pt" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**_lowerCAmelCase )
# verify the logits
snake_case_ = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
snake_case_ = torch.tensor([-0.0_260, -0.4_739, 0.1_911] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@require_torch
class __lowerCAmelCase ( unittest.TestCase , a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (ConvNextBackbone,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE = ConvNextConfig
_SCREAMING_SNAKE_CASE = False
def lowerCAmelCase__ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ = ConvNextModelTester(self )
| 283 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __lowerCAmelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCAmelCase : float , _lowerCAmelCase : Callable , _lowerCAmelCase : int , _lowerCAmelCase : float = 1.0 , _lowerCAmelCase : str = None , ) -> str:
"""simple docstring"""
super().__init__()
snake_case_ = initial_learning_rate
snake_case_ = warmup_steps
snake_case_ = power
snake_case_ = decay_schedule_fn
snake_case_ = name
def __call__( self : Any , _lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
with tf.name_scope(self.name or "WarmUp" ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
snake_case_ = tf.cast(_lowerCAmelCase , tf.floataa )
snake_case_ = tf.cast(self.warmup_steps , tf.floataa )
snake_case_ = global_step_float / warmup_steps_float
snake_case_ = self.initial_learning_rate * tf.math.pow(_lowerCAmelCase , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=_lowerCAmelCase , )
def lowerCAmelCase__ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def _lowerCAmelCase ( lowerCAmelCase_ :float , lowerCAmelCase_ :int , lowerCAmelCase_ :int , lowerCAmelCase_ :float = 0.0 , lowerCAmelCase_ :float = 0.9 , lowerCAmelCase_ :float = 0.9_9_9 , lowerCAmelCase_ :float = 1e-8 , lowerCAmelCase_ :Optional[float] = None , lowerCAmelCase_ :Optional[float] = None , lowerCAmelCase_ :float = 0.0 , lowerCAmelCase_ :float = 1.0 , lowerCAmelCase_ :Optional[List[str]] = None , )->Optional[Any]:
'''simple docstring'''
snake_case_ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=lowerCAmelCase_ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=lowerCAmelCase_ , )
if num_warmup_steps:
snake_case_ = WarmUp(
initial_learning_rate=lowerCAmelCase_ , decay_schedule_fn=lowerCAmelCase_ , warmup_steps=lowerCAmelCase_ , )
if weight_decay_rate > 0.0:
snake_case_ = AdamWeightDecay(
learning_rate=lowerCAmelCase_ , weight_decay_rate=lowerCAmelCase_ , beta_a=lowerCAmelCase_ , beta_a=lowerCAmelCase_ , epsilon=lowerCAmelCase_ , clipnorm=lowerCAmelCase_ , global_clipnorm=lowerCAmelCase_ , exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"] , include_in_weight_decay=lowerCAmelCase_ , )
else:
snake_case_ = tf.keras.optimizers.Adam(
learning_rate=lowerCAmelCase_ , beta_a=lowerCAmelCase_ , beta_a=lowerCAmelCase_ , epsilon=lowerCAmelCase_ , clipnorm=lowerCAmelCase_ , global_clipnorm=lowerCAmelCase_ , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class __lowerCAmelCase ( a ):
"""simple docstring"""
def __init__( self : Dict , _lowerCAmelCase : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , _lowerCAmelCase : float = 0.9 , _lowerCAmelCase : float = 0.999 , _lowerCAmelCase : float = 1e-7 , _lowerCAmelCase : bool = False , _lowerCAmelCase : float = 0.0 , _lowerCAmelCase : Optional[List[str]] = None , _lowerCAmelCase : Optional[List[str]] = None , _lowerCAmelCase : str = "AdamWeightDecay" , **_lowerCAmelCase : Union[str, Any] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
snake_case_ = weight_decay_rate
snake_case_ = include_in_weight_decay
snake_case_ = exclude_from_weight_decay
@classmethod
def lowerCAmelCase__ ( cls : Dict , _lowerCAmelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
snake_case_ = {"WarmUp": WarmUp}
return super(_lowerCAmelCase , cls ).from_config(_lowerCAmelCase , custom_objects=_lowerCAmelCase )
def lowerCAmelCase__ ( self : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
super(_lowerCAmelCase , self )._prepare_local(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
snake_case_ = tf.constant(
self.weight_decay_rate , name="adam_weight_decay_rate" )
def lowerCAmelCase__ ( self : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ) -> int:
"""simple docstring"""
snake_case_ = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"] , use_locking=self._use_locking , )
return tf.no_op()
def lowerCAmelCase__ ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int]=None , **_lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
snake_case_ , snake_case_ = list(zip(*_lowerCAmelCase ) )
return super(_lowerCAmelCase , self ).apply_gradients(zip(_lowerCAmelCase , _lowerCAmelCase ) , name=_lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase__ ( self : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
snake_case_ = apply_state or {}
snake_case_ = apply_state.get((var_device, var_dtype) )
if coefficients is None:
snake_case_ = self._fallback_apply_state(_lowerCAmelCase , _lowerCAmelCase )
snake_case_ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def lowerCAmelCase__ ( self : int , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple=None ) -> List[str]:
"""simple docstring"""
snake_case_ , snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , _lowerCAmelCase )
snake_case_ = self._decay_weights_op(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
with tf.control_dependencies([decay] ):
return super(_lowerCAmelCase , self )._resource_apply_dense(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase__ ( self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str , _lowerCAmelCase : Any , _lowerCAmelCase : Tuple=None ) -> List[str]:
"""simple docstring"""
snake_case_ , snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , _lowerCAmelCase )
snake_case_ = self._decay_weights_op(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
with tf.control_dependencies([decay] ):
return super(_lowerCAmelCase , self )._resource_apply_sparse(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
snake_case_ = super().get_config()
config.update({"weight_decay_rate": self.weight_decay_rate} )
return config
def lowerCAmelCase__ ( self : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(_lowerCAmelCase , _lowerCAmelCase ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(_lowerCAmelCase , _lowerCAmelCase ) is not None:
return False
return True
class __lowerCAmelCase ( a ):
"""simple docstring"""
def __init__( self : str ) -> Dict:
"""simple docstring"""
snake_case_ = []
snake_case_ = None
@property
def lowerCAmelCase__ ( self : Dict ) -> List[Any]:
"""simple docstring"""
if self._accum_steps is None:
snake_case_ = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=_lowerCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def lowerCAmelCase__ ( self : Tuple ) -> List[str]:
"""simple docstring"""
if not self._gradients:
raise ValueError("The accumulator should be called first to initialize the gradients" )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : List[Any] , _lowerCAmelCase : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
if not self._gradients:
snake_case_ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(_lowerCAmelCase ) , trainable=_lowerCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(_lowerCAmelCase ) != len(self._gradients ):
raise ValueError(F'''Expected {len(self._gradients )} gradients, but got {len(_lowerCAmelCase )}''' )
for accum_gradient, gradient in zip(self._gradients , _lowerCAmelCase ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(_lowerCAmelCase )
self._accum_steps.assign_add(1 )
def lowerCAmelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(_lowerCAmelCase ) )
| 283 | 1 |
import os
import pytest
from attr import dataclass
_SCREAMING_SNAKE_CASE = 'us-east-1' # defaults region
@dataclass
class a :
"""simple docstring"""
lowerCamelCase :List[Any] = 42
lowerCamelCase :Union[str, Any] = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
lowerCamelCase :str = {
'''task_name''': '''mnli''',
'''per_device_train_batch_size''': 16,
'''per_device_eval_batch_size''': 16,
'''do_train''': True,
'''do_eval''': True,
'''do_predict''': True,
'''output_dir''': '''/opt/ml/model''',
'''overwrite_output_dir''': True,
'''max_steps''': 500,
'''save_steps''': 5500,
}
lowerCamelCase :Optional[Any] = {**hyperparameters, '''max_steps''': 1000}
@property
def UpperCAmelCase ( self ) -> Any:
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def UpperCAmelCase ( self ) -> str:
return F'''{self.framework}-transfromers-test'''
@property
def UpperCAmelCase ( self ) -> List[str]:
return F'''./tests/sagemaker/scripts/{self.framework}'''
@property
def UpperCAmelCase ( self ) -> List[Any]:
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""")
def snake_case ( snake_case__ :Dict) -> str:
_A = SageMakerTestEnvironment(framework=request.cls.framework)
| 712 | from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
@add_end_docstrings(__lowerCAmelCase )
class a ( __lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ) -> Optional[Any]:
super().__init__(*lowerCAmelCase_ , **lowerCAmelCase_ )
self.check_model_type(lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ ) -> Tuple:
_A , _A = {}, {}
if padding is not None:
_A = padding
if truncation is not None:
_A = truncation
if top_k is not None:
_A = top_k
return preprocess_params, {}, postprocess_params
def __call__( self , lowerCAmelCase_ , lowerCAmelCase_ = None , **lowerCAmelCase_ ) -> Union[str, Any]:
if isinstance(lowerCAmelCase_ , (Image.Image, str) ) and isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_A = {"""image""": image, """question""": question}
else:
_A = image
_A = super().__call__(lowerCAmelCase_ , **lowerCAmelCase_ )
return results
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=False , lowerCAmelCase_=False ) -> Any:
_A = load_image(inputs["""image"""] )
_A = self.tokenizer(
inputs["""question"""] , return_tensors=self.framework , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ )
_A = self.image_processor(images=lowerCAmelCase_ , return_tensors=self.framework )
model_inputs.update(lowerCAmelCase_ )
return model_inputs
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> Tuple:
_A = self.model(**lowerCAmelCase_ )
return model_outputs
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=5 ) -> Union[str, Any]:
if top_k > self.model.config.num_labels:
_A = self.model.config.num_labels
if self.framework == "pt":
_A = model_outputs.logits.sigmoid()[0]
_A , _A = probs.topk(lowerCAmelCase_ )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
_A = scores.tolist()
_A = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_ , lowerCAmelCase_ )]
| 83 | 0 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCamelCase_ : int = 16
UpperCamelCase_ : Any = 32
def A_ (__a , __a = 16 ):
'''simple docstring'''
A_ = AutoTokenizer.from_pretrained("bert-base-cased" )
A_ = load_dataset("glue" , "mrpc" )
def tokenize_function(__a ):
# max_length=None => use the model max length (it's actually the default)
A_ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=__a , max_length=__a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A_ = datasets.map(
__a , batched=__a , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A_ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__a ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A_ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A_ = 16
elif accelerator.mixed_precision != "no":
A_ = 8
else:
A_ = None
return tokenizer.pad(
__a , padding="longest" , max_length=__a , pad_to_multiple_of=__a , return_tensors="pt" , )
# Instantiate dataloaders.
A_ = DataLoader(
tokenized_datasets["train"] , shuffle=__a , collate_fn=__a , batch_size=__a )
A_ = DataLoader(
tokenized_datasets["validation"] , shuffle=__a , collate_fn=__a , batch_size=__a )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
UpperCamelCase_ : str = mocked_dataloaders # noqa: F811
def A_ (__a , __a ):
'''simple docstring'''
if os.environ.get("TESTING_MOCKED_DATALOADERS" , __a ) == "1":
A_ = 2
# New Code #
A_ = int(args.gradient_accumulation_steps )
# Initialize accelerator
A_ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=__a )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A_ = config["lr"]
A_ = int(config["num_epochs"] )
A_ = int(config["seed"] )
A_ = int(config["batch_size"] )
A_ = evaluate.load("glue" , "mrpc" )
set_seed(__a )
A_ , A_ = get_dataloaders(__a , __a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A_ = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=__a )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A_ = model.to(accelerator.device )
# Instantiate optimizer
A_ = AdamW(params=model.parameters() , lr=__a )
# Instantiate scheduler
A_ = get_linear_schedule_with_warmup(
optimizer=__a , num_warmup_steps=100 , num_training_steps=(len(__a ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A_ , A_ , A_ , A_ , A_ = accelerator.prepare(
__a , __a , __a , __a , __a )
# Now we train the model
for epoch in range(__a ):
model.train()
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(__a ):
A_ = model(**__a )
A_ = output.loss
accelerator.backward(__a )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A_ = model(**__a )
A_ = outputs.logits.argmax(dim=-1 )
A_ , A_ = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__a , references=__a , )
A_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , __a )
def A_ ():
'''simple docstring'''
A_ = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=__a , default=__a , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=__a , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
A_ = parser.parse_args()
A_ = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__a , __a )
if __name__ == "__main__":
main()
| 115 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ : Tuple = [
'''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SEWForCTC''',
'''SEWForSequenceClassification''',
'''SEWModel''',
'''SEWPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
UpperCamelCase_ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 115 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __lowerCAmelCase ( __a ):
snake_case : Any = """dandelin/vilt-b32-finetuned-vqa"""
snake_case : Optional[Any] = (
"""This is a tool that answers a question about an image. It takes an input named `image` which should be the """
"""image containing the information, as well as a `question` which should be the question in English. It """
"""returns a text that is the answer to the question."""
)
snake_case : str = """image_qa"""
snake_case : Union[str, Any] = AutoProcessor
snake_case : Tuple = AutoModelForVisualQuestionAnswering
snake_case : Union[str, Any] = ["""image""", """text"""]
snake_case : Optional[int] = ["""text"""]
def __init__(self , *lowerCAmelCase__ , **lowerCAmelCase__ ):
requires_backends(self , ["""vision"""] )
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
return self.pre_processor(lowerCAmelCase__ , lowerCAmelCase__ , return_tensors="""pt""" )
def snake_case_ (self , lowerCAmelCase__ ):
with torch.no_grad():
return self.model(**lowerCAmelCase__ ).logits
def snake_case_ (self , lowerCAmelCase__ ):
_UpperCAmelCase : Union[str, Any] = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 156 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ : List[str] = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
'''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
'''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : Optional[Any] = ['''LayoutLMv2TokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : Dict = ['''LayoutLMv2FeatureExtractor''']
lowerCAmelCase_ : str = ['''LayoutLMv2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : Any = [
'''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv2ForQuestionAnswering''',
'''LayoutLMv2ForSequenceClassification''',
'''LayoutLMv2ForTokenClassification''',
'''LayoutLMv2Layer''',
'''LayoutLMv2Model''',
'''LayoutLMv2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 156 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def lowerCamelCase ( UpperCAmelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :List[str] = botoa.client('iam' )
SCREAMING_SNAKE_CASE__ :str = {
'Version': '2012-10-17',
'Statement': [
{'Effect': 'Allow', 'Principal': {'Service': 'sagemaker.amazonaws.com'}, 'Action': 'sts:AssumeRole'}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=UpperCAmelCase__ , AssumeRolePolicyDocument=json.dumps(UpperCAmelCase__ , indent=2 ) )
SCREAMING_SNAKE_CASE__ :int = {
'Version': '2012-10-17',
'Statement': [
{
'Effect': 'Allow',
'Action': [
'sagemaker:*',
'ecr:GetDownloadUrlForLayer',
'ecr:BatchGetImage',
'ecr:BatchCheckLayerAvailability',
'ecr:GetAuthorizationToken',
'cloudwatch:PutMetricData',
'cloudwatch:GetMetricData',
'cloudwatch:GetMetricStatistics',
'cloudwatch:ListMetrics',
'logs:CreateLogGroup',
'logs:CreateLogStream',
'logs:DescribeLogStreams',
'logs:PutLogEvents',
'logs:GetLogEvents',
's3:CreateBucket',
's3:ListBucket',
's3:GetBucketLocation',
's3:GetObject',
's3:PutObject',
],
'Resource': '*',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=UpperCAmelCase__ , PolicyName=F'''{role_name}_policy_permission''' , PolicyDocument=json.dumps(UpperCAmelCase__ , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(F'''role {role_name} already exists. Using existing one''' )
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Any = botoa.client('iam' )
return iam_client.get_role(RoleName=UpperCAmelCase__ )["Role"]["Arn"]
def lowerCamelCase ( ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Optional[int] = _ask_options(
'How do you want to authorize?' , ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '] , UpperCAmelCase__ , )
SCREAMING_SNAKE_CASE__ :int = None
if credentials_configuration == 0:
SCREAMING_SNAKE_CASE__ :Optional[int] = _ask_field('Enter your AWS Profile name: [default] ' , default='default' )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = aws_profile
else:
print(
'Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'
'`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`' )
SCREAMING_SNAKE_CASE__ :Optional[Any] = _ask_field('AWS Access Key ID: ' )
SCREAMING_SNAKE_CASE__ :List[Any] = aws_access_key_id
SCREAMING_SNAKE_CASE__ :int = _ask_field('AWS Secret Access Key: ' )
SCREAMING_SNAKE_CASE__ :int = aws_secret_access_key
SCREAMING_SNAKE_CASE__ :Union[str, Any] = _ask_field('Enter your AWS Region: [us-east-1]' , default='us-east-1' )
SCREAMING_SNAKE_CASE__ :Tuple = aws_region
SCREAMING_SNAKE_CASE__ :List[Any] = _ask_options(
'Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?' , ['Provide IAM Role name', 'Create new IAM role using credentials'] , UpperCAmelCase__ , )
if role_management == 0:
SCREAMING_SNAKE_CASE__ :Tuple = _ask_field('Enter your IAM role name: ' )
else:
SCREAMING_SNAKE_CASE__ :Optional[Any] = 'accelerate_sagemaker_execution_role'
print(F'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials''' )
_create_iam_role_for_sagemaker(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = _ask_field(
'Do you want to use custom Docker image? [yes/NO]: ' , _convert_yes_no_to_bool , default=UpperCAmelCase__ , error_message='Please enter yes or no.' , )
SCREAMING_SNAKE_CASE__ :List[str] = None
if is_custom_docker_image:
SCREAMING_SNAKE_CASE__ :List[Any] = _ask_field('Enter your Docker image: ' , lambda UpperCAmelCase__ : str(UpperCAmelCase__ ).lower() )
SCREAMING_SNAKE_CASE__ :Optional[int] = _ask_field(
'Do you want to provide SageMaker input channels with data locations? [yes/NO]: ' , _convert_yes_no_to_bool , default=UpperCAmelCase__ , error_message='Please enter yes or no.' , )
SCREAMING_SNAKE_CASE__ :Any = None
if is_sagemaker_inputs_enabled:
SCREAMING_SNAKE_CASE__ :int = _ask_field(
'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ' , lambda UpperCAmelCase__ : str(UpperCAmelCase__ ).lower() , )
SCREAMING_SNAKE_CASE__ :List[str] = _ask_field(
'Do you want to enable SageMaker metrics? [yes/NO]: ' , _convert_yes_no_to_bool , default=UpperCAmelCase__ , error_message='Please enter yes or no.' , )
SCREAMING_SNAKE_CASE__ :Optional[Any] = None
if is_sagemaker_metrics_enabled:
SCREAMING_SNAKE_CASE__ :List[str] = _ask_field(
'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ' , lambda UpperCAmelCase__ : str(UpperCAmelCase__ ).lower() , )
SCREAMING_SNAKE_CASE__ :str = _ask_options(
'What is the distributed mode?' , ['No distributed training', 'Data parallelism'] , _convert_sagemaker_distributed_mode , )
SCREAMING_SNAKE_CASE__ :Dict = {}
SCREAMING_SNAKE_CASE__ :List[Any] = _ask_field(
'Do you wish to optimize your script with torch dynamo?[yes/NO]:' , _convert_yes_no_to_bool , default=UpperCAmelCase__ , error_message='Please enter yes or no.' , )
if use_dynamo:
SCREAMING_SNAKE_CASE__ :int = 'dynamo_'
SCREAMING_SNAKE_CASE__ :List[Any] = _ask_options(
'Which dynamo backend would you like to use?' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = _ask_field(
'Do you want to customize the defaults sent to torch.compile? [yes/NO]: ' , _convert_yes_no_to_bool , default=UpperCAmelCase__ , error_message='Please enter yes or no.' , )
if use_custom_options:
SCREAMING_SNAKE_CASE__ :int = _ask_options(
'Which mode do you want to use?' , UpperCAmelCase__ , lambda UpperCAmelCase__ : TORCH_DYNAMO_MODES[int(UpperCAmelCase__ )] , default='default' , )
SCREAMING_SNAKE_CASE__ :Any = _ask_field(
'Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ' , _convert_yes_no_to_bool , default=UpperCAmelCase__ , error_message='Please enter yes or no.' , )
SCREAMING_SNAKE_CASE__ :List[str] = _ask_field(
'Do you want to enable dynamic shape tracing? [yes/NO]: ' , _convert_yes_no_to_bool , default=UpperCAmelCase__ , error_message='Please enter yes or no.' , )
SCREAMING_SNAKE_CASE__ :Any = 'Which EC2 instance type you want to use for your training?'
if distributed_type != SageMakerDistributedType.NO:
SCREAMING_SNAKE_CASE__ :str = _ask_options(
UpperCAmelCase__ , UpperCAmelCase__ , lambda UpperCAmelCase__ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCAmelCase__ )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
SCREAMING_SNAKE_CASE__ :List[Any] = _ask_field(UpperCAmelCase__ , lambda UpperCAmelCase__ : str(UpperCAmelCase__ ).lower() , default='ml.p3.2xlarge' )
SCREAMING_SNAKE_CASE__ :List[str] = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
SCREAMING_SNAKE_CASE__ :Dict = _ask_field(
'How many machines do you want use? [1]: ' , UpperCAmelCase__ , default=1 , )
SCREAMING_SNAKE_CASE__ :str = _ask_options(
'Do you wish to use FP16 or BF16 (mixed precision)?' , ['no', 'fp16', 'bf16', 'fp8'] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.' )
return SageMakerConfig(
image_uri=UpperCAmelCase__ , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=UpperCAmelCase__ , use_cpu=UpperCAmelCase__ , dynamo_config=UpperCAmelCase__ , eca_instance_type=UpperCAmelCase__ , profile=UpperCAmelCase__ , region=UpperCAmelCase__ , iam_role_name=UpperCAmelCase__ , mixed_precision=UpperCAmelCase__ , num_machines=UpperCAmelCase__ , sagemaker_inputs_file=UpperCAmelCase__ , sagemaker_metrics_file=UpperCAmelCase__ , )
| 209 | '''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
UpperCamelCase_ = '''\
@inproceedings{lin-2004-rouge,
title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",
author = "Lin, Chin-Yew",
booktitle = "Text Summarization Branches Out",
month = jul,
year = "2004",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W04-1013",
pages = "74--81",
}
'''
UpperCamelCase_ = '''\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
'''
UpperCamelCase_ = '''
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
`"rougeL"`: Longest common subsequence based scoring.
`"rougeLSum"`: rougeLsum splits text using `"\n"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric(\'rouge\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
[\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']
>>> print(results["rouge1"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results["rouge1"].mid.fmeasure)
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _SCREAMING_SNAKE_CASE( datasets.Metric ):
def __lowerCamelCase ( self : Any ) -> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge'] , reference_urls=[
'https://en.wikipedia.org/wiki/ROUGE_(metric)',
'https://github.com/google-research/google-research/tree/master/rouge',
] , )
def __lowerCamelCase ( self : Optional[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int , UpperCamelCase_ : Tuple=None , UpperCamelCase_ : Tuple=True , UpperCamelCase_ : str=False ) -> Optional[Any]:
if rouge_types is None:
SCREAMING_SNAKE_CASE__ :Optional[int] = ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
SCREAMING_SNAKE_CASE__ :Union[str, Any] = rouge_scorer.RougeScorer(rouge_types=UpperCamelCase_ , use_stemmer=UpperCamelCase_ )
if use_aggregator:
SCREAMING_SNAKE_CASE__ :Tuple = scoring.BootstrapAggregator()
else:
SCREAMING_SNAKE_CASE__ :Any = []
for ref, pred in zip(UpperCamelCase_ , UpperCamelCase_ ):
SCREAMING_SNAKE_CASE__ :Tuple = scorer.score(UpperCamelCase_ , UpperCamelCase_ )
if use_aggregator:
aggregator.add_scores(UpperCamelCase_ )
else:
scores.append(UpperCamelCase_ )
if use_aggregator:
SCREAMING_SNAKE_CASE__ :Tuple = aggregator.aggregate()
else:
SCREAMING_SNAKE_CASE__ :List[str] = {}
for key in scores[0]:
SCREAMING_SNAKE_CASE__ :int = [score[key] for score in scores]
return result
| 209 | 1 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class a :
def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[Any]=13 , SCREAMING_SNAKE_CASE_ : Any=7 , SCREAMING_SNAKE_CASE_ : List[Any]=True , SCREAMING_SNAKE_CASE_ : int=True , SCREAMING_SNAKE_CASE_ : int=True , SCREAMING_SNAKE_CASE_ : int=True , SCREAMING_SNAKE_CASE_ : List[str]=99 , SCREAMING_SNAKE_CASE_ : List[Any]=32 , SCREAMING_SNAKE_CASE_ : Optional[int]=5 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=4 , SCREAMING_SNAKE_CASE_ : List[str]=37 , SCREAMING_SNAKE_CASE_ : str="gelu" , SCREAMING_SNAKE_CASE_ : Any=0.1 , SCREAMING_SNAKE_CASE_ : Dict=0.1 , SCREAMING_SNAKE_CASE_ : int=512 , SCREAMING_SNAKE_CASE_ : Any=16 , SCREAMING_SNAKE_CASE_ : Optional[int]=2 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.02 , SCREAMING_SNAKE_CASE_ : int=3 , SCREAMING_SNAKE_CASE_ : Optional[Any]=4 , SCREAMING_SNAKE_CASE_ : Dict=None , ):
__lowerCamelCase: Tuple = parent
__lowerCamelCase: Dict = batch_size
__lowerCamelCase: List[str] = seq_length
__lowerCamelCase: str = is_training
__lowerCamelCase: Optional[Any] = use_input_mask
__lowerCamelCase: Optional[Any] = use_token_type_ids
__lowerCamelCase: int = use_labels
__lowerCamelCase: Optional[Any] = vocab_size
__lowerCamelCase: Tuple = hidden_size
__lowerCamelCase: int = num_hidden_layers
__lowerCamelCase: int = num_attention_heads
__lowerCamelCase: Optional[Any] = intermediate_size
__lowerCamelCase: List[str] = hidden_act
__lowerCamelCase: int = hidden_dropout_prob
__lowerCamelCase: List[Any] = attention_probs_dropout_prob
__lowerCamelCase: int = max_position_embeddings
__lowerCamelCase: Tuple = type_vocab_size
__lowerCamelCase: str = type_sequence_label_size
__lowerCamelCase: Dict = initializer_range
__lowerCamelCase: Tuple = num_labels
__lowerCamelCase: List[Any] = num_choices
__lowerCamelCase: int = scope
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
__lowerCamelCase: List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCamelCase: str = None
if self.use_input_mask:
__lowerCamelCase: Tuple = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCamelCase: List[str] = None
if self.use_token_type_ids:
__lowerCamelCase: Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCamelCase: Union[str, Any] = None
__lowerCamelCase: List[Any] = None
__lowerCamelCase: int = None
if self.use_labels:
__lowerCamelCase: int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCamelCase: Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCamelCase: Dict = ids_tensor([self.batch_size] , self.num_choices )
__lowerCamelCase: str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self : Any ):
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCamelCase: List[str] = NystromformerModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__lowerCamelCase: str = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Any = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: List[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
__lowerCamelCase: Tuple = NystromformerForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__lowerCamelCase: Any = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] ):
__lowerCamelCase: List[Any] = NystromformerForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__lowerCamelCase: Union[str, Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self : Any , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCamelCase: Optional[Any] = self.num_labels
__lowerCamelCase: int = NystromformerForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__lowerCamelCase: Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : str ):
__lowerCamelCase: Optional[Any] = self.num_labels
__lowerCamelCase: List[str] = NystromformerForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__lowerCamelCase: Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
__lowerCamelCase: Union[str, Any] = self.num_choices
__lowerCamelCase: str = NystromformerForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__lowerCamelCase: Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase: Any = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase: List[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCamelCase: List[str] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self : str ):
__lowerCamelCase: List[Any] = self.prepare_config_and_inputs()
(
(
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) , (
__lowerCamelCase
) ,
): Union[str, Any] = config_and_inputs
__lowerCamelCase: str = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a ( _UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase ):
UpperCAmelCase__ : Optional[Any] = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCAmelCase__ : int = (
{
"feature-extraction": NystromformerModel,
"fill-mask": NystromformerForMaskedLM,
"question-answering": NystromformerForQuestionAnswering,
"text-classification": NystromformerForSequenceClassification,
"token-classification": NystromformerForTokenClassification,
"zero-shot": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = False
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
__lowerCamelCase: Any = NystromformerModelTester(self )
__lowerCamelCase: Any = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self : str ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
__lowerCamelCase: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
__lowerCamelCase: int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCamelCase: int = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
__lowerCamelCase: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
__lowerCamelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
__lowerCamelCase: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
__lowerCamelCase: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
__lowerCamelCase: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase: Tuple = NystromformerModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class a ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
__lowerCamelCase: Optional[int] = NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""" )
__lowerCamelCase: Optional[Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
__lowerCamelCase: List[Any] = model(SCREAMING_SNAKE_CASE_ )[0]
__lowerCamelCase: str = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Union[str, Any] = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ):
__lowerCamelCase: Tuple = """the [MASK] of Belgium is Brussels"""
__lowerCamelCase: Optional[Any] = AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""" )
__lowerCamelCase: Optional[int] = NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""" )
__lowerCamelCase: str = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" )
with torch.no_grad():
__lowerCamelCase: Any = model(encoding.input_ids ).logits
__lowerCamelCase: Any = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , """capital""" )
| 189 |
class a :
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : str = "" , SCREAMING_SNAKE_CASE_ : bool = False ):
# Mapping from the first character of the prefix of the node
__lowerCamelCase: dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
__lowerCamelCase: str = is_leaf
__lowerCamelCase: Optional[int] = prefix
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str ):
__lowerCamelCase: Optional[Any] = 0
for q, w in zip(self.prefix , SCREAMING_SNAKE_CASE_ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def SCREAMING_SNAKE_CASE__ ( self : Any , SCREAMING_SNAKE_CASE_ : list[str] ):
for word in words:
self.insert(SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , SCREAMING_SNAKE_CASE_ : str ):
# Case 1: If the word is the prefix of the node
# Solution: We set the current node as leaf
if self.prefix == word:
__lowerCamelCase: Union[str, Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
__lowerCamelCase: Any = RadixNode(prefix=SCREAMING_SNAKE_CASE_ , is_leaf=SCREAMING_SNAKE_CASE_ )
else:
__lowerCamelCase: Union[str, Any] = self.nodes[word[0]]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase: List[str] = incoming_node.match(
SCREAMING_SNAKE_CASE_ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
__lowerCamelCase: List[Any] = remaining_prefix
__lowerCamelCase: Optional[Any] = self.nodes[matching_string[0]]
__lowerCamelCase: Optional[int] = RadixNode(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Union[str, Any] = aux_node
if remaining_word == "":
__lowerCamelCase: Optional[int] = True
else:
self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : int , SCREAMING_SNAKE_CASE_ : str ):
__lowerCamelCase: int = self.nodes.get(word[0] , SCREAMING_SNAKE_CASE_ )
if not incoming_node:
return False
else:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase: Any = incoming_node.match(
SCREAMING_SNAKE_CASE_ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : str ):
__lowerCamelCase: str = self.nodes.get(word[0] , SCREAMING_SNAKE_CASE_ )
if not incoming_node:
return False
else:
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase: Dict = incoming_node.match(
SCREAMING_SNAKE_CASE_ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(SCREAMING_SNAKE_CASE_ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
__lowerCamelCase: List[Any] = list(self.nodes.values() )[0]
__lowerCamelCase: Any = merging_node.is_leaf
self.prefix += merging_node.prefix
__lowerCamelCase: Tuple = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
__lowerCamelCase: int = False
# If there is 1 edge, we merge it with its child
else:
__lowerCamelCase: Union[str, Any] = list(incoming_node.nodes.values() )[0]
__lowerCamelCase: List[str] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
__lowerCamelCase: Union[str, Any] = merging_node.nodes
return True
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : int = 0 ):
if self.prefix != "":
print("""-""" * height , self.prefix , """ (leaf)""" if self.is_leaf else """""" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def __lowerCAmelCase ( ) -> bool:
__lowerCamelCase: Optional[int] = """banana bananas bandana band apple all beast""".split()
__lowerCamelCase: Optional[Any] = RadixNode()
root.insert_many(snake_case )
assert all(root.find(snake_case ) for word in words )
assert not root.find("""bandanas""" )
assert not root.find("""apps""" )
root.delete("""all""" )
assert not root.find("""all""" )
root.delete("""banana""" )
assert not root.find("""banana""" )
assert root.find("""bananas""" )
return True
def __lowerCAmelCase ( ) -> None:
assert test_trie()
def __lowerCAmelCase ( ) -> None:
__lowerCamelCase: int = RadixNode()
__lowerCamelCase: str = """banana bananas bandanas bandana band apple all beast""".split()
root.insert_many(snake_case )
print("""Words:""" , snake_case )
print("""Tree:""" )
root.print_tree()
if __name__ == "__main__":
main()
| 189 | 1 |
"""simple docstring"""
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self ) -> int:
a_ : List[str] = 0
a_ : Dict = 0
a_ : int = {}
def A ( self , _SCREAMING_SNAKE_CASE ) -> Any:
if vertex not in self.adjacency:
a_ : Union[str, Any] = {}
self.num_vertices += 1
def A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
self.add_vertex(_SCREAMING_SNAKE_CASE )
self.add_vertex(_SCREAMING_SNAKE_CASE )
if head == tail:
return
a_ : Union[str, Any] = weight
a_ : Dict = weight
def A ( self ) -> Optional[int]:
a_ : Optional[int] = self.get_edges()
for edge in edges:
a_ , a_ , a_ : List[str] = edge
edges.remove((tail, head, weight) )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
a_ : Union[str, Any] = list(edges[i] )
edges.sort(key=lambda _SCREAMING_SNAKE_CASE : e[2] )
for i in range(len(_SCREAMING_SNAKE_CASE ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
a_ : str = edges[i][2] + 1
for edge in edges:
a_ , a_ , a_ : List[str] = edge
a_ : Dict = weight
a_ : Any = weight
def __str__( self ) -> int:
a_ : List[Any] = ""
for tail in self.adjacency:
for head in self.adjacency[tail]:
a_ : Tuple = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("\n" )
def A ( self ) -> Tuple:
a_ : int = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def A ( self ) -> str:
return self.adjacency.keys()
@staticmethod
def A ( _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> int:
a_ : str = Graph()
if vertices is None:
a_ : Any = []
if edges is None:
a_ : Optional[int] = []
for vertex in vertices:
g.add_vertex(_SCREAMING_SNAKE_CASE )
for edge in edges:
g.add_edge(*_SCREAMING_SNAKE_CASE )
return g
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self ) -> Optional[int]:
a_ : List[Any] = {}
a_ : int = {}
def __len__( self ) -> Optional[Any]:
return len(self.parent )
def A ( self , _SCREAMING_SNAKE_CASE ) -> str:
if item in self.parent:
return self.find(_SCREAMING_SNAKE_CASE )
a_ : str = item
a_ : List[Any] = 0
return item
def A ( self , _SCREAMING_SNAKE_CASE ) -> Any:
if item not in self.parent:
return self.make_set(_SCREAMING_SNAKE_CASE )
if item != self.parent[item]:
a_ : Any = self.find(self.parent[item] )
return self.parent[item]
def A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
a_ : Any = self.find(_SCREAMING_SNAKE_CASE )
a_ : Dict = self.find(_SCREAMING_SNAKE_CASE )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
a_ : str = roota
return roota
if self.rank[roota] < self.rank[roota]:
a_ : List[str] = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
a_ : Optional[Any] = roota
return roota
return None
@staticmethod
def A ( _SCREAMING_SNAKE_CASE ) -> int:
a_ : Optional[int] = graph.num_vertices
a_ : Optional[int] = Graph.UnionFind()
a_ : List[str] = []
while num_components > 1:
a_ : List[str] = {}
for vertex in graph.get_vertices():
a_ : str = -1
a_ : Optional[int] = graph.get_edges()
for edge in edges:
a_ , a_ , a_ : str = edge
edges.remove((tail, head, weight) )
for edge in edges:
a_ , a_ , a_ : List[Any] = edge
a_ : Optional[Any] = union_find.find(_SCREAMING_SNAKE_CASE )
a_ : int = union_find.find(_SCREAMING_SNAKE_CASE )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
a_ : Dict = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
a_ : str = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
a_ , a_ , a_ : List[Any] = cheap_edge[vertex]
if union_find.find(_SCREAMING_SNAKE_CASE ) != union_find.find(_SCREAMING_SNAKE_CASE ):
union_find.union(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
mst_edges.append(cheap_edge[vertex] )
a_ : int = num_components - 1
a_ : Optional[Any] = Graph.build(edges=_SCREAMING_SNAKE_CASE )
return mst
| 473 | """simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def lowerCAmelCase_ (_SCREAMING_SNAKE_CASE :float , _SCREAMING_SNAKE_CASE :float ) -> tuple:
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise ValueError("Capacitance cannot be 0 or negative" )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 473 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
a__ = logging.get_logger(__name__)
@dataclass
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Dict = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : str , **UpperCamelCase__ : Dict):
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
snake_case__ = deprecated_arg[3:]
setattr(self , UpperCamelCase__ , not kwargs.pop(UpperCamelCase__))
logger.warning(
F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
F''' {positive_arg}={kwargs[positive_arg]}''')
snake_case__ = kwargs.pop("""torchscript""" , self.torchscript)
snake_case__ = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics)
snake_case__ = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level)
super().__init__(**UpperCamelCase__)
_lowercase : bool = field(default=lowercase_ , metadata={'''help''': '''Trace the models using torchscript'''} )
_lowercase : bool = field(default=lowercase_ , metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''} )
_lowercase : str = field(
default='''O1''' , metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
} , )
@cached_property
def __magic_name__ ( self : Tuple):
'''simple docstring'''
requires_backends(self , ["""torch"""])
logger.info("""PyTorch: setting up devices""")
if not self.cuda:
snake_case__ = torch.device("""cpu""")
snake_case__ = 0
elif is_torch_tpu_available():
snake_case__ = xm.xla_device()
snake_case__ = 0
else:
snake_case__ = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""")
snake_case__ = torch.cuda.device_count()
return device, n_gpu
@property
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
return is_torch_tpu_available() and self.tpu
@property
def __magic_name__ ( self : List[str]):
'''simple docstring'''
requires_backends(self , ["""torch"""])
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
requires_backends(self , ["""torch"""])
return self._setup_devices[0]
@property
def __magic_name__ ( self : str):
'''simple docstring'''
requires_backends(self , ["""torch"""])
return self._setup_devices[1]
@property
def __magic_name__ ( self : str):
'''simple docstring'''
return self.n_gpu > 0
| 99 |
import random
from typing import Any
def _UpperCAmelCase ( a : list ):
for _ in range(len(a ) ):
snake_case__ = random.randint(0 , len(a ) - 1 )
snake_case__ = random.randint(0 , len(a ) - 1 )
snake_case__ , snake_case__ = data[b], data[a]
return data
if __name__ == "__main__":
a__ = [0, 1, 2, 3, 4, 5, 6, 7]
a__ = ["""python""", """says""", """hello""", """!"""]
print("""Fisher-Yates Shuffle:""")
print("""List""", integers, strings)
print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 99 | 1 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
UpperCAmelCase = StableDiffusionPipeline.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
UpperCAmelCase = load_file(_lowerCAmelCase )
UpperCAmelCase = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
UpperCAmelCase = key.split("." )[0].split(LORA_PREFIX_TEXT_ENCODER + "_" )[-1].split("_" )
UpperCAmelCase = pipeline.text_encoder
else:
UpperCAmelCase = key.split("." )[0].split(LORA_PREFIX_UNET + "_" )[-1].split("_" )
UpperCAmelCase = pipeline.unet
# find the target layer
UpperCAmelCase = layer_infos.pop(0 )
while len(_lowerCAmelCase ) > -1:
try:
UpperCAmelCase = curr_layer.__getattr__(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
UpperCAmelCase = layer_infos.pop(0 )
elif len(_lowerCAmelCase ) == 0:
break
except Exception:
if len(_lowerCAmelCase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
UpperCAmelCase = layer_infos.pop(0 )
UpperCAmelCase = []
if "lora_down" in key:
pair_keys.append(key.replace("lora_down" , "lora_up" ) )
pair_keys.append(_lowerCAmelCase )
else:
pair_keys.append(_lowerCAmelCase )
pair_keys.append(key.replace("lora_up" , "lora_down" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
UpperCAmelCase = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
UpperCAmelCase = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_lowerCAmelCase , _lowerCAmelCase ).unsqueeze(2 ).unsqueeze(3 )
else:
UpperCAmelCase = state_dict[pair_keys[0]].to(torch.floataa )
UpperCAmelCase = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_lowerCAmelCase , _lowerCAmelCase )
# update visited list
for item in pair_keys:
visited.append(_lowerCAmelCase )
return pipeline
if __name__ == "__main__":
__lowerCAmelCase =argparse.ArgumentParser()
parser.add_argument(
"--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
)
parser.add_argument(
"--lora_prefix_text_encoder",
default="lora_te",
type=str,
help="The prefix of text encoder weight in safetensors",
)
parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
parser.add_argument(
"--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
)
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
__lowerCAmelCase =parser.parse_args()
__lowerCAmelCase =args.base_model_path
__lowerCAmelCase =args.checkpoint_path
__lowerCAmelCase =args.dump_path
__lowerCAmelCase =args.lora_prefix_unet
__lowerCAmelCase =args.lora_prefix_text_encoder
__lowerCAmelCase =args.alpha
__lowerCAmelCase =convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
__lowerCAmelCase =pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 333 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase =logging.get_logger(__name__)
__lowerCAmelCase ={
"microsoft/beit-base-patch16-224-pt22k": (
"https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __magic_name__ ( _a):
_UpperCAmelCase : int = 'beit'
def __init__( self : Union[str, Any] ,__SCREAMING_SNAKE_CASE : int=8_1_9_2 ,__SCREAMING_SNAKE_CASE : List[Any]=7_6_8 ,__SCREAMING_SNAKE_CASE : Any=1_2 ,__SCREAMING_SNAKE_CASE : List[str]=1_2 ,__SCREAMING_SNAKE_CASE : Optional[Any]=3_0_7_2 ,__SCREAMING_SNAKE_CASE : Dict="gelu" ,__SCREAMING_SNAKE_CASE : Tuple=0.0 ,__SCREAMING_SNAKE_CASE : int=0.0 ,__SCREAMING_SNAKE_CASE : int=0.02 ,__SCREAMING_SNAKE_CASE : Optional[int]=1e-12 ,__SCREAMING_SNAKE_CASE : Union[str, Any]=2_2_4 ,__SCREAMING_SNAKE_CASE : List[str]=1_6 ,__SCREAMING_SNAKE_CASE : Any=3 ,__SCREAMING_SNAKE_CASE : Optional[Any]=False ,__SCREAMING_SNAKE_CASE : int=False ,__SCREAMING_SNAKE_CASE : List[str]=False ,__SCREAMING_SNAKE_CASE : List[str]=False ,__SCREAMING_SNAKE_CASE : Optional[int]=0.1 ,__SCREAMING_SNAKE_CASE : Tuple=0.1 ,__SCREAMING_SNAKE_CASE : Optional[int]=True ,__SCREAMING_SNAKE_CASE : str=[3, 5, 7, 1_1] ,__SCREAMING_SNAKE_CASE : int=[1, 2, 3, 6] ,__SCREAMING_SNAKE_CASE : Dict=True ,__SCREAMING_SNAKE_CASE : Any=0.4 ,__SCREAMING_SNAKE_CASE : List[Any]=2_5_6 ,__SCREAMING_SNAKE_CASE : List[Any]=1 ,__SCREAMING_SNAKE_CASE : Tuple=False ,__SCREAMING_SNAKE_CASE : Any=2_5_5 ,**__SCREAMING_SNAKE_CASE : List[str] ,):
super().__init__(**__SCREAMING_SNAKE_CASE )
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = image_size
UpperCAmelCase = patch_size
UpperCAmelCase = num_channels
UpperCAmelCase = use_mask_token
UpperCAmelCase = use_absolute_position_embeddings
UpperCAmelCase = use_relative_position_bias
UpperCAmelCase = use_shared_relative_position_bias
UpperCAmelCase = layer_scale_init_value
UpperCAmelCase = drop_path_rate
UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
UpperCAmelCase = out_indices
UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
UpperCAmelCase = use_auxiliary_head
UpperCAmelCase = auxiliary_loss_weight
UpperCAmelCase = auxiliary_channels
UpperCAmelCase = auxiliary_num_convs
UpperCAmelCase = auxiliary_concat_input
UpperCAmelCase = semantic_loss_ignore_index
class __magic_name__ ( _a):
_UpperCAmelCase : List[str] = version.parse('1.11')
@property
def _UpperCAmelCase ( self : Any ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _UpperCAmelCase ( self : int ):
return 1e-4
| 333 | 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 __a ( _lowerCAmelCase ):
UpperCamelCase_ : List[str] = ['''image_processor''', '''tokenizer''']
UpperCamelCase_ : List[Any] = '''BlipImageProcessor'''
UpperCamelCase_ : int = '''AutoTokenizer'''
def __init__( self : Optional[int] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : str )-> Optional[Any]:
"""simple docstring"""
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
# add QFormer tokenizer
UpperCamelCase = qformer_tokenizer
def __call__( self : Optional[Any] , UpperCAmelCase_ : ImageInput = None , UpperCAmelCase_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase_ : Union[bool, str, TruncationStrategy] = None , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : int = 0 , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : Optional[Union[str, TensorType]] = None , **UpperCAmelCase_ : int , )-> BatchFeature:
"""simple docstring"""
if images is None and text is None:
raise ValueError("You have to specify at least images or text." )
UpperCamelCase = BatchFeature()
if text is not None:
UpperCamelCase = 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_ )
UpperCamelCase = 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_ , )
UpperCamelCase = qformer_text_encoding.pop("input_ids" )
UpperCamelCase = qformer_text_encoding.pop("attention_mask" )
if images is not None:
UpperCamelCase = self.image_processor(UpperCAmelCase_ , return_tensors=UpperCAmelCase_ )
encoding.update(UpperCAmelCase_ )
return encoding
def _SCREAMING_SNAKE_CASE ( self : List[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : Dict )-> Optional[int]:
"""simple docstring"""
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _SCREAMING_SNAKE_CASE ( self : Tuple )-> List[str]:
"""simple docstring"""
UpperCamelCase = self.tokenizer.model_input_names
UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , UpperCAmelCase_ : Any , **UpperCAmelCase_ : Dict )-> int:
"""simple docstring"""
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_ )
UpperCamelCase = os.path.join(UpperCAmelCase_ , "qformer_tokenizer" )
self.qformer_tokenizer.save_pretrained(UpperCAmelCase_ )
return super().save_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Union[str, Any] , UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : int )-> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = AutoTokenizer.from_pretrained(UpperCAmelCase_ , subfolder="qformer_tokenizer" )
UpperCamelCase = cls._get_arguments_from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
args.append(UpperCAmelCase_ )
return cls(*UpperCAmelCase_ )
| 556 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
SCREAMING_SNAKE_CASE = 4
SCREAMING_SNAKE_CASE = 3
class __a ( _lowerCAmelCase ):
pass
def lowerCamelCase__ ( UpperCAmelCase_ )-> List[str]:
"""simple docstring"""
for shard in shards:
for i in range(UpperCAmelCase_ ):
yield {"i": i, "shard": shard}
def lowerCamelCase__ ( )-> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = int(os.environ["RANK"] )
UpperCamelCase = int(os.environ["WORLD_SIZE"] )
UpperCamelCase = ArgumentParser()
parser.add_argument("--streaming" , type=UpperCAmelCase_ )
parser.add_argument("--local_rank" , type=UpperCAmelCase_ )
parser.add_argument("--num_workers" , type=UpperCAmelCase_ , default=0 )
UpperCamelCase = parser.parse_args()
UpperCamelCase = args.streaming
UpperCamelCase = args.num_workers
UpperCamelCase = {"shards": [F"shard_{shard_idx}" for shard_idx in range(UpperCAmelCase_ )]}
UpperCamelCase = IterableDataset.from_generator(UpperCAmelCase_ , gen_kwargs=UpperCAmelCase_ )
if not streaming:
UpperCamelCase = Dataset.from_list(list(UpperCAmelCase_ ) )
UpperCamelCase = split_dataset_by_node(UpperCAmelCase_ , rank=UpperCAmelCase_ , world_size=UpperCAmelCase_ )
UpperCamelCase = torch.utils.data.DataLoader(UpperCAmelCase_ , num_workers=UpperCAmelCase_ )
UpperCamelCase = NUM_SHARDS * NUM_ITEMS_PER_SHARD
UpperCamelCase = full_size // world_size
expected_local_size += int(rank < (full_size % world_size) )
UpperCamelCase = sum(1 for _ in dataloader )
if local_size != expected_local_size:
raise FailedTestError(F"local_size {local_size} != expected_local_size {expected_local_size}" )
if __name__ == "__main__":
main()
| 556 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class __lowerCamelCase (unittest.TestCase ):
_lowercase = MODEL_FOR_CAUSAL_LM_MAPPING
_lowercase = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def snake_case_ ( self: Optional[int] ):
'''simple docstring'''
__UpperCamelCase = pipeline(task='text-generation',model='sshleifer/tiny-ctrl',framework='pt' )
# Using `do_sample=False` to force deterministic output
__UpperCamelCase = text_generator('This is a test',do_sample=_UpperCamelCase )
self.assertEqual(
_UpperCamelCase,[
{
'generated_text': (
'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
' oscope. FiliFili@@'
)
}
],)
__UpperCamelCase = text_generator(['This is a test', 'This is a second test'] )
self.assertEqual(
_UpperCamelCase,[
[
{
'generated_text': (
'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
' oscope. FiliFili@@'
)
}
],
[
{
'generated_text': (
'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
' oscope. oscope. FiliFili@@'
)
}
],
],)
__UpperCamelCase = text_generator('This is a test',do_sample=_UpperCamelCase,num_return_sequences=2,return_tensors=_UpperCamelCase )
self.assertEqual(
_UpperCamelCase,[
{'generated_token_ids': ANY(_UpperCamelCase )},
{'generated_token_ids': ANY(_UpperCamelCase )},
],)
__UpperCamelCase = text_generator.model.config.eos_token_id
__UpperCamelCase = "<pad>"
__UpperCamelCase = text_generator(
['This is a test', 'This is a second test'],do_sample=_UpperCamelCase,num_return_sequences=2,batch_size=2,return_tensors=_UpperCamelCase,)
self.assertEqual(
_UpperCamelCase,[
[
{'generated_token_ids': ANY(_UpperCamelCase )},
{'generated_token_ids': ANY(_UpperCamelCase )},
],
[
{'generated_token_ids': ANY(_UpperCamelCase )},
{'generated_token_ids': ANY(_UpperCamelCase )},
],
],)
@require_tf
def snake_case_ ( self: Optional[int] ):
'''simple docstring'''
__UpperCamelCase = pipeline(task='text-generation',model='sshleifer/tiny-ctrl',framework='tf' )
# Using `do_sample=False` to force deterministic output
__UpperCamelCase = text_generator('This is a test',do_sample=_UpperCamelCase )
self.assertEqual(
_UpperCamelCase,[
{
'generated_text': (
'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
' please,'
)
}
],)
__UpperCamelCase = text_generator(['This is a test', 'This is a second test'],do_sample=_UpperCamelCase )
self.assertEqual(
_UpperCamelCase,[
[
{
'generated_text': (
'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
' please,'
)
}
],
[
{
'generated_text': (
'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
)
}
],
],)
def snake_case_ ( self: Dict,A_: Dict,A_: List[Any],A_: Any ):
'''simple docstring'''
__UpperCamelCase = TextGenerationPipeline(model=_UpperCamelCase,tokenizer=_UpperCamelCase )
return text_generator, ["This is a test", "Another test"]
def snake_case_ ( self: str ):
'''simple docstring'''
__UpperCamelCase = "Hello I believe in"
__UpperCamelCase = pipeline('text-generation',model='hf-internal-testing/tiny-random-gpt2' )
__UpperCamelCase = text_generator(_UpperCamelCase )
self.assertEqual(
_UpperCamelCase,[{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}],)
__UpperCamelCase = text_generator(_UpperCamelCase,stop_sequence=' fe' )
self.assertEqual(_UpperCamelCase,[{'generated_text': 'Hello I believe in fe'}] )
def snake_case_ ( self: Optional[int],A_: List[Any],A_: Optional[int] ):
'''simple docstring'''
__UpperCamelCase = text_generator.model
__UpperCamelCase = text_generator.tokenizer
__UpperCamelCase = text_generator('This is a test' )
self.assertEqual(_UpperCamelCase,[{'generated_text': ANY(_UpperCamelCase )}] )
self.assertTrue(outputs[0]['generated_text'].startswith('This is a test' ) )
__UpperCamelCase = text_generator('This is a test',return_full_text=_UpperCamelCase )
self.assertEqual(_UpperCamelCase,[{'generated_text': ANY(_UpperCamelCase )}] )
self.assertNotIn('This is a test',outputs[0]['generated_text'] )
__UpperCamelCase = pipeline(task='text-generation',model=_UpperCamelCase,tokenizer=_UpperCamelCase,return_full_text=_UpperCamelCase )
__UpperCamelCase = text_generator('This is a test' )
self.assertEqual(_UpperCamelCase,[{'generated_text': ANY(_UpperCamelCase )}] )
self.assertNotIn('This is a test',outputs[0]['generated_text'] )
__UpperCamelCase = text_generator('This is a test',return_full_text=_UpperCamelCase )
self.assertEqual(_UpperCamelCase,[{'generated_text': ANY(_UpperCamelCase )}] )
self.assertTrue(outputs[0]['generated_text'].startswith('This is a test' ) )
__UpperCamelCase = text_generator(['This is great !', 'Something else'],num_return_sequences=2,do_sample=_UpperCamelCase )
self.assertEqual(
_UpperCamelCase,[
[{'generated_text': ANY(_UpperCamelCase )}, {'generated_text': ANY(_UpperCamelCase )}],
[{'generated_text': ANY(_UpperCamelCase )}, {'generated_text': ANY(_UpperCamelCase )}],
],)
if text_generator.tokenizer.pad_token is not None:
__UpperCamelCase = text_generator(
['This is great !', 'Something else'],num_return_sequences=2,batch_size=2,do_sample=_UpperCamelCase )
self.assertEqual(
_UpperCamelCase,[
[{'generated_text': ANY(_UpperCamelCase )}, {'generated_text': ANY(_UpperCamelCase )}],
[{'generated_text': ANY(_UpperCamelCase )}, {'generated_text': ANY(_UpperCamelCase )}],
],)
with self.assertRaises(_UpperCamelCase ):
__UpperCamelCase = text_generator('test',return_full_text=_UpperCamelCase,return_text=_UpperCamelCase )
with self.assertRaises(_UpperCamelCase ):
__UpperCamelCase = text_generator('test',return_full_text=_UpperCamelCase,return_tensors=_UpperCamelCase )
with self.assertRaises(_UpperCamelCase ):
__UpperCamelCase = text_generator('test',return_text=_UpperCamelCase,return_tensors=_UpperCamelCase )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
__UpperCamelCase = text_generator('' )
self.assertEqual(_UpperCamelCase,[{'generated_text': ANY(_UpperCamelCase )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
__UpperCamelCase = text_generator('' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
__UpperCamelCase = ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
if (
tokenizer.model_max_length < 1_0000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('This is a test' * 500,max_new_tokens=20 )
__UpperCamelCase = text_generator('This is a test' * 500,handle_long_generation='hole',max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(_UpperCamelCase ):
text_generator(
'This is a test' * 500,handle_long_generation='hole',max_new_tokens=tokenizer.model_max_length + 10,)
@require_torch
@require_accelerate
@require_torch_gpu
def snake_case_ ( self: List[Any] ):
'''simple docstring'''
import torch
# Classic `model_kwargs`
__UpperCamelCase = pipeline(
model='hf-internal-testing/tiny-random-bloom',model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa},)
self.assertEqual(pipe.model.device,torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype,torch.bfloataa )
__UpperCamelCase = pipe('This is a test' )
self.assertEqual(
_UpperCamelCase,[
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
],)
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
__UpperCamelCase = pipeline(model='hf-internal-testing/tiny-random-bloom',device_map='auto',torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device,torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype,torch.bfloataa )
__UpperCamelCase = pipe('This is a test' )
self.assertEqual(
_UpperCamelCase,[
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
],)
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
__UpperCamelCase = pipeline(model='hf-internal-testing/tiny-random-bloom',device_map='auto' )
self.assertEqual(pipe.model.device,torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype,torch.floataa )
__UpperCamelCase = pipe('This is a test' )
self.assertEqual(
_UpperCamelCase,[
{
'generated_text': (
'This is a test test test test test test test test test test test test test test test test'
' test'
)
}
],)
@require_torch
@require_torch_gpu
def snake_case_ ( self: Any ):
'''simple docstring'''
import torch
__UpperCamelCase = pipeline(model='hf-internal-testing/tiny-random-bloom',device=0,torch_dtype=torch.floataa )
pipe('This is a test' )
@require_torch
@require_accelerate
@require_torch_gpu
def snake_case_ ( self: Optional[Any] ):
'''simple docstring'''
import torch
__UpperCamelCase = pipeline(model='hf-internal-testing/tiny-random-bloom',device_map='auto',torch_dtype=torch.floataa )
pipe('This is a test',do_sample=_UpperCamelCase,top_p=0.5 )
def snake_case_ ( self: List[str] ):
'''simple docstring'''
__UpperCamelCase = "Hello world"
__UpperCamelCase = pipeline('text-generation',model='hf-internal-testing/tiny-random-gpt2' )
if text_generator.model.framework == "tf":
__UpperCamelCase = logging.get_logger('transformers.generation.tf_utils' )
else:
__UpperCamelCase = logging.get_logger('transformers.generation.utils' )
__UpperCamelCase = "Both `max_new_tokens`" # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(_UpperCamelCase ) as cl:
__UpperCamelCase = text_generator(_UpperCamelCase,max_length=10,max_new_tokens=1 )
self.assertIn(_UpperCamelCase,cl.out )
# The user only sets one -> no warning
with CaptureLogger(_UpperCamelCase ) as cl:
__UpperCamelCase = text_generator(_UpperCamelCase,max_new_tokens=1 )
self.assertNotIn(_UpperCamelCase,cl.out )
with CaptureLogger(_UpperCamelCase ) as cl:
__UpperCamelCase = text_generator(_UpperCamelCase,max_length=10 )
self.assertNotIn(_UpperCamelCase,cl.out )
| 1 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class a__ ( lowerCamelCase_ ):
def __init__( self , _UpperCamelCase="" , _UpperCamelCase="train" ):
"""simple docstring"""
assert os.path.isdir(_UpperCamelCase )
_lowercase : Optional[Any] = []
_lowercase : List[Any] = os.listdir(_UpperCamelCase )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
_lowercase : Union[str, Any] = os.path.join(_UpperCamelCase , _UpperCamelCase )
if not os.path.isfile(_UpperCamelCase ):
continue
self.documents.append(_UpperCamelCase )
def __len__( self ):
"""simple docstring"""
return len(self.documents )
def __getitem__( self , _UpperCamelCase ):
"""simple docstring"""
_lowercase : Dict = self.documents[idx]
_lowercase : Optional[Any] = document_path.split("/" )[-1]
with open(_UpperCamelCase , encoding="utf-8" ) as source:
_lowercase : Dict = source.read()
_lowercase , _lowercase : List[str] = process_story(_UpperCamelCase )
return document_name, story_lines, summary_lines
def _A ( snake_case ) -> Optional[Any]:
_lowercase : Optional[int] = list(filter(lambda snake_case : len(snake_case ) != 0 , [line.strip() for line in raw_story.split("\n" )] ) )
# for some unknown reason some lines miss a period, add it
_lowercase : Optional[Any] = [_add_missing_period(snake_case ) for line in nonempty_lines]
# gather article lines
_lowercase : Union[str, Any] = []
_lowercase : Union[str, Any] = deque(snake_case )
while True:
try:
_lowercase : Dict = lines.popleft()
if element.startswith("@highlight" ):
break
story_lines.append(snake_case )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
_lowercase : int = list(filter(lambda snake_case : not t.startswith("@highlight" ) , snake_case ) )
return story_lines, summary_lines
def _A ( snake_case ) -> List[Any]:
_lowercase : str = [".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
if line.startswith("@highlight" ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def _A ( snake_case , snake_case , snake_case ) -> Any:
if len(snake_case ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(snake_case )) )
return sequence
def _A ( snake_case , snake_case ) -> Union[str, Any]:
_lowercase : Union[str, Any] = torch.ones_like(snake_case )
_lowercase : Tuple = sequence == pad_token_id
_lowercase : Any = 0
return mask
def _A ( snake_case , snake_case , snake_case ) -> Tuple:
_lowercase : List[str] = [tokenizer.encode(snake_case ) for line in story_lines]
_lowercase : Optional[int] = [token for sentence in story_lines_token_ids for token in sentence]
_lowercase : Optional[Any] = [tokenizer.encode(snake_case ) for line in summary_lines]
_lowercase : Optional[Any] = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def _A ( snake_case , snake_case ) -> Any:
_lowercase : Dict = []
for sequence in batch:
_lowercase : Tuple = -1
_lowercase : str = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(snake_case )
return torch.tensor(snake_case )
| 245 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , lowerCAmelCase : int , lowerCAmelCase : Tuple=7 , lowerCAmelCase : int=3 , lowerCAmelCase : Optional[int]=18 , lowerCAmelCase : str=30 , lowerCAmelCase : Tuple=400 , lowerCAmelCase : Union[str, Any]=True , lowerCAmelCase : Tuple=None , lowerCAmelCase : List[Any]=True , lowerCAmelCase : Union[str, Any]=None , lowerCAmelCase : Optional[Any]=True , ):
lowerCAmelCase = size if size is not None else {"""shortest_edge""": 20}
lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = num_channels
lowerCAmelCase = image_size
lowerCAmelCase = min_resolution
lowerCAmelCase = max_resolution
lowerCAmelCase = do_resize
lowerCAmelCase = size
lowerCAmelCase = do_center_crop
lowerCAmelCase = crop_size
lowerCAmelCase = do_flip_channel_order
def __lowercase ( self : Union[str, Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
_a = MobileViTImageProcessor if is_vision_available() else None
def __lowercase ( self : List[Any] ):
lowerCAmelCase = MobileViTImageProcessingTester(self )
@property
def __lowercase ( self : str ):
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self : str ):
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase , """do_resize""" ) )
self.assertTrue(hasattr(lowerCAmelCase , """size""" ) )
self.assertTrue(hasattr(lowerCAmelCase , """do_center_crop""" ) )
self.assertTrue(hasattr(lowerCAmelCase , """center_crop""" ) )
self.assertTrue(hasattr(lowerCAmelCase , """do_flip_channel_order""" ) )
def __lowercase ( self : Optional[Any] ):
lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
lowerCAmelCase = 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 __lowercase ( self : Dict ):
pass
def __lowercase ( self : Any ):
# Initialize image_processing
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , Image.Image )
# Test not batched input
lowerCAmelCase = 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
lowerCAmelCase = image_processing(lowerCAmelCase , 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 __lowercase ( self : Union[str, Any] ):
# Initialize image_processing
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , numpify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , np.ndarray )
# Test not batched input
lowerCAmelCase = 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
lowerCAmelCase = image_processing(lowerCAmelCase , 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 __lowercase ( self : Union[str, Any] ):
# Initialize image_processing
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , torchify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , torch.Tensor )
# Test not batched input
lowerCAmelCase = 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
lowerCAmelCase = image_processing(lowerCAmelCase , 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"""],
) , )
| 529 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Union[str, Any] ):
lowerCAmelCase = {}
def __lowercase ( self : Optional[int] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Union[str, Any]=1 ):
if self.graph.get(lowerCAmelCase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
lowerCAmelCase = [[w, v]]
if not self.graph.get(lowerCAmelCase ):
lowerCAmelCase = []
def __lowercase ( self : Optional[int] ):
return list(self.graph )
def __lowercase ( self : Optional[Any] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Dict ):
if self.graph.get(lowerCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCAmelCase )
def __lowercase ( self : List[str] , lowerCAmelCase : Tuple=-2 , lowerCAmelCase : List[Any]=-1 ):
if s == d:
return []
lowerCAmelCase = []
lowerCAmelCase = []
if s == -2:
lowerCAmelCase = list(self.graph )[0]
stack.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
lowerCAmelCase = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCAmelCase ) != 0:
lowerCAmelCase = stack[len(lowerCAmelCase ) - 1]
else:
lowerCAmelCase = ss
# check if se have reached the starting point
if len(lowerCAmelCase ) == 0:
return visited
def __lowercase ( self : Tuple , lowerCAmelCase : Any=-1 ):
if c == -1:
lowerCAmelCase = floor(random() * 1_0000 ) + 10
for i in range(lowerCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowerCAmelCase = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCAmelCase , lowerCAmelCase , 1 )
def __lowercase ( self : Optional[int] , lowerCAmelCase : List[str]=-2 ):
lowerCAmelCase = deque()
lowerCAmelCase = []
if s == -2:
lowerCAmelCase = list(self.graph )[0]
d.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
while d:
lowerCAmelCase = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def __lowercase ( self : List[Any] , lowerCAmelCase : Union[str, Any] ):
lowerCAmelCase = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def __lowercase ( self : int , lowerCAmelCase : Tuple ):
return len(self.graph[u] )
def __lowercase ( self : Optional[int] , lowerCAmelCase : Optional[Any]=-2 ):
lowerCAmelCase = []
lowerCAmelCase = []
if s == -2:
lowerCAmelCase = list(self.graph )[0]
stack.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
lowerCAmelCase = s
lowerCAmelCase = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCAmelCase ) != 0:
lowerCAmelCase = stack[len(lowerCAmelCase ) - 1]
else:
lowerCAmelCase = ss
# check if se have reached the starting point
if len(lowerCAmelCase ) == 0:
return sorted_nodes
def __lowercase ( self : Any ):
lowerCAmelCase = []
lowerCAmelCase = []
lowerCAmelCase = list(self.graph )[0]
stack.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
lowerCAmelCase = -2
lowerCAmelCase = []
lowerCAmelCase = s
lowerCAmelCase = False
lowerCAmelCase = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCAmelCase = len(lowerCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCAmelCase = True
if len(lowerCAmelCase ) != 0:
lowerCAmelCase = stack[len(lowerCAmelCase ) - 1]
else:
lowerCAmelCase = False
indirect_parents.append(lowerCAmelCase )
lowerCAmelCase = s
lowerCAmelCase = ss
# check if se have reached the starting point
if len(lowerCAmelCase ) == 0:
return list(lowerCAmelCase )
def __lowercase ( self : Tuple ):
lowerCAmelCase = []
lowerCAmelCase = []
lowerCAmelCase = list(self.graph )[0]
stack.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
lowerCAmelCase = -2
lowerCAmelCase = []
lowerCAmelCase = s
lowerCAmelCase = False
lowerCAmelCase = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCAmelCase = len(lowerCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCAmelCase = True
if len(lowerCAmelCase ) != 0:
lowerCAmelCase = stack[len(lowerCAmelCase ) - 1]
else:
lowerCAmelCase = False
indirect_parents.append(lowerCAmelCase )
lowerCAmelCase = s
lowerCAmelCase = ss
# check if se have reached the starting point
if len(lowerCAmelCase ) == 0:
return False
def __lowercase ( self : List[Any] , lowerCAmelCase : Any=-2 , lowerCAmelCase : Tuple=-1 ):
lowerCAmelCase = time()
self.dfs(lowerCAmelCase , lowerCAmelCase )
lowerCAmelCase = time()
return end - begin
def __lowercase ( self : int , lowerCAmelCase : str=-2 ):
lowerCAmelCase = time()
self.bfs(lowerCAmelCase )
lowerCAmelCase = time()
return end - begin
class SCREAMING_SNAKE_CASE__ :
def __init__( self : int ):
lowerCAmelCase = {}
def __lowercase ( self : Optional[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : str , lowerCAmelCase : str=1 ):
# check if the u exists
if self.graph.get(lowerCAmelCase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
lowerCAmelCase = [[w, v]]
# add the other way
if self.graph.get(lowerCAmelCase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
lowerCAmelCase = [[w, u]]
def __lowercase ( self : Dict , lowerCAmelCase : Any , lowerCAmelCase : int ):
if self.graph.get(lowerCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCAmelCase )
# the other way round
if self.graph.get(lowerCAmelCase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCAmelCase )
def __lowercase ( self : Dict , lowerCAmelCase : Optional[int]=-2 , lowerCAmelCase : Optional[Any]=-1 ):
if s == d:
return []
lowerCAmelCase = []
lowerCAmelCase = []
if s == -2:
lowerCAmelCase = list(self.graph )[0]
stack.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
lowerCAmelCase = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCAmelCase ) != 0:
lowerCAmelCase = stack[len(lowerCAmelCase ) - 1]
else:
lowerCAmelCase = ss
# check if se have reached the starting point
if len(lowerCAmelCase ) == 0:
return visited
def __lowercase ( self : Any , lowerCAmelCase : Dict=-1 ):
if c == -1:
lowerCAmelCase = floor(random() * 1_0000 ) + 10
for i in range(lowerCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowerCAmelCase = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCAmelCase , lowerCAmelCase , 1 )
def __lowercase ( self : int , lowerCAmelCase : Tuple=-2 ):
lowerCAmelCase = deque()
lowerCAmelCase = []
if s == -2:
lowerCAmelCase = list(self.graph )[0]
d.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
while d:
lowerCAmelCase = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def __lowercase ( self : List[str] , lowerCAmelCase : str ):
return len(self.graph[u] )
def __lowercase ( self : Optional[Any] ):
lowerCAmelCase = []
lowerCAmelCase = []
lowerCAmelCase = list(self.graph )[0]
stack.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
lowerCAmelCase = -2
lowerCAmelCase = []
lowerCAmelCase = s
lowerCAmelCase = False
lowerCAmelCase = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCAmelCase = len(lowerCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCAmelCase = True
if len(lowerCAmelCase ) != 0:
lowerCAmelCase = stack[len(lowerCAmelCase ) - 1]
else:
lowerCAmelCase = False
indirect_parents.append(lowerCAmelCase )
lowerCAmelCase = s
lowerCAmelCase = ss
# check if se have reached the starting point
if len(lowerCAmelCase ) == 0:
return list(lowerCAmelCase )
def __lowercase ( self : int ):
lowerCAmelCase = []
lowerCAmelCase = []
lowerCAmelCase = list(self.graph )[0]
stack.append(lowerCAmelCase )
visited.append(lowerCAmelCase )
lowerCAmelCase = -2
lowerCAmelCase = []
lowerCAmelCase = s
lowerCAmelCase = False
lowerCAmelCase = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCAmelCase = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCAmelCase = len(lowerCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCAmelCase = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCAmelCase = True
if len(lowerCAmelCase ) != 0:
lowerCAmelCase = stack[len(lowerCAmelCase ) - 1]
else:
lowerCAmelCase = False
indirect_parents.append(lowerCAmelCase )
lowerCAmelCase = s
lowerCAmelCase = ss
# check if se have reached the starting point
if len(lowerCAmelCase ) == 0:
return False
def __lowercase ( self : str ):
return list(self.graph )
def __lowercase ( self : Dict , lowerCAmelCase : Dict=-2 , lowerCAmelCase : List[str]=-1 ):
lowerCAmelCase = time()
self.dfs(lowerCAmelCase , lowerCAmelCase )
lowerCAmelCase = time()
return end - begin
def __lowercase ( self : str , lowerCAmelCase : Union[str, Any]=-2 ):
lowerCAmelCase = time()
self.bfs(lowerCAmelCase )
lowerCAmelCase = time()
return end - begin
| 529 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
__lowerCamelCase = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def a ( __UpperCAmelCase : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Union[str, Any] ) -> Tuple:
for attribute in key.split(""".""" ):
__magic_name__: Tuple = getattr(__UpperCAmelCase , __UpperCAmelCase )
if weight_type is not None:
__magic_name__: Dict = getattr(__UpperCAmelCase , __UpperCAmelCase ).shape
else:
__magic_name__: Tuple = 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":
__magic_name__: Union[str, Any] = value
elif weight_type == "weight_g":
__magic_name__: str = value
elif weight_type == "weight_v":
__magic_name__: Optional[Any] = value
elif weight_type == "bias":
__magic_name__: Tuple = value
else:
__magic_name__: Union[str, Any] = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def a ( __UpperCAmelCase : int , __UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
__magic_name__: Union[str, Any] = []
__magic_name__: Tuple = fairseq_model.state_dict()
__magic_name__: List[str] = hf_model.feature_extractor
__magic_name__: str = hf_model.adapter
for name, value in fairseq_dict.items():
__magic_name__: str = False
if "conv_layers" in name:
load_conv_layer(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
__magic_name__: List[Any] = True
elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""] ):
load_adapter(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__magic_name__: Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__magic_name__: str = True
if "*" in mapped_key:
__magic_name__: Dict = name.split(__UpperCAmelCase )[0].split(""".""" )[-2]
__magic_name__: Optional[int] = mapped_key.replace("""*""" , __UpperCAmelCase )
if "weight_g" in name:
__magic_name__: Optional[Any] = """weight_g"""
elif "weight_v" in name:
__magic_name__: Dict = """weight_v"""
elif "bias" in name:
__magic_name__: List[Any] = """bias"""
elif "weight" in name:
__magic_name__: int = """weight"""
else:
__magic_name__: Optional[Any] = None
set_recursively(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
continue
if not is_used:
unused_weights.append(__UpperCAmelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def a ( __UpperCAmelCase : List[str] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : List[str] ) -> Union[str, Any]:
__magic_name__: List[Any] = full_name.split("""conv_layers.""" )[-1]
__magic_name__: str = name.split(""".""" )
__magic_name__: List[Any] = int(items[0] )
__magic_name__: 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.'
)
__magic_name__: Dict = 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.'
)
__magic_name__: 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."
)
__magic_name__: 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.'
)
__magic_name__: Tuple = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__UpperCAmelCase )
def a ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] ) -> str:
__magic_name__: Dict = full_name.split("""adaptor.""" )[-1]
__magic_name__: Dict = name.split(""".""" )
if items[1].isdigit():
__magic_name__: Optional[int] = int(items[1] )
else:
__magic_name__: Any = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
__magic_name__: str = value
logger.info(f'Adapter proj layer norm bias was initialized from {full_name}.' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
__magic_name__: Tuple = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), f'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
__magic_name__: Dict = value
logger.info(f'Adapter proj layer bias was initialized from {full_name}.' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), f'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
__magic_name__: Optional[int] = value
logger.info(f'Adapter proj layer weight was initialized from {full_name}.' )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
__magic_name__: int = value
logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
__magic_name__: Optional[Any] = value
logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.' )
else:
unused_weights.append(__UpperCAmelCase )
def a ( __UpperCAmelCase : List[str] ) -> Any:
__magic_name__, __magic_name__: Tuple = emb.weight.shape
__magic_name__: Union[str, Any] = nn.Linear(__UpperCAmelCase , __UpperCAmelCase , bias=__UpperCAmelCase )
__magic_name__: Optional[Any] = emb.weight.data
return lin_layer
@torch.no_grad()
def a ( __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict , ) -> Optional[int]:
__magic_name__: Optional[int] = WavaVecaConfig.from_pretrained(
__UpperCAmelCase , add_adapter=__UpperCAmelCase , adapter_stride=__UpperCAmelCase , adapter_kernel_size=__UpperCAmelCase , use_auth_token=__UpperCAmelCase , output_hidden_size=__UpperCAmelCase , )
__magic_name__: Optional[int] = MBartConfig.from_pretrained(__UpperCAmelCase )
# load model
__magic_name__, __magic_name__, __magic_name__: str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
"""config_yaml""": config_yaml_path,
"""data""": """/""".join(dict_path.split("""/""" )[:-1] ),
"""w2v_path""": checkpoint_path,
"""load_pretrained_decoder_from""": None,
} , )
__magic_name__: Tuple = model[0].eval()
# load feature extractor
__magic_name__: Tuple = WavaVecaFeatureExtractor.from_pretrained(__UpperCAmelCase , use_auth_token=__UpperCAmelCase )
# set weights for wav2vec2 encoder
__magic_name__: int = WavaVecaModel(__UpperCAmelCase )
recursively_load_weights_wavaveca(model.encoder , __UpperCAmelCase )
# load decoder weights
__magic_name__: Optional[Any] = MBartForCausalLM(__UpperCAmelCase )
__magic_name__, __magic_name__: Dict = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__UpperCAmelCase )
logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}' )
logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}' )
__magic_name__: List[str] = SpeechEncoderDecoderModel(encoder=__UpperCAmelCase , decoder=__UpperCAmelCase )
__magic_name__: Optional[int] = False
__magic_name__: Union[str, Any] = MBartaaTokenizer(__UpperCAmelCase )
tokenizer.save_pretrained(__UpperCAmelCase )
__magic_name__: List[Any] = hf_wavavec.config.to_dict()
__magic_name__: Any = tokenizer.pad_token_id
__magic_name__: Union[str, Any] = tokenizer.bos_token_id
__magic_name__: Optional[int] = tokenizer.eos_token_id
__magic_name__: int = """mbart50"""
__magic_name__: int = """wav2vec2"""
__magic_name__: str = tokenizer.eos_token_id
__magic_name__: Tuple = 2_5_0_0_0_4
__magic_name__: Optional[int] = tokenizer.eos_token_id
__magic_name__: List[Any] = SpeechEncoderDecoderConfig.from_dict(__UpperCAmelCase )
hf_wavavec.save_pretrained(__UpperCAmelCase )
feature_extractor.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
parser.add_argument(
'--encoder_config_path',
default='facebook/wav2vec2-xls-r-1b',
type=str,
help='Path to hf encoder wav2vec2 checkpoint config',
)
parser.add_argument(
'--decoder_config_path',
default='facebook/mbart-large-50-one-to-many-mmt',
type=str,
help='Path to hf decoder checkpoint config',
)
parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
parser.add_argument('--encoder_output_dim', default=10_24, type=int, help='encoder output dim')
parser.add_argument('--start_token_id', default=25_00_04, type=int, help='`decoder_start_token_id` of model config')
__lowerCamelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 96 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class __A :
def __init__( self : str , __snake_case : int , __snake_case : Dict=1_3 , __snake_case : Dict=7 , __snake_case : str=False , __snake_case : Dict=True , __snake_case : Tuple=False , __snake_case : List[Any]=False , __snake_case : Dict=1_9 , __snake_case : Dict=3_2 , __snake_case : Union[str, Any]=5 , __snake_case : List[Any]=4 , __snake_case : List[Any]=3_7 , __snake_case : Union[str, Any]="gelu" , __snake_case : List[str]=0.1 , __snake_case : Union[str, Any]=0.1 , __snake_case : List[str]=5_1_2 , __snake_case : List[str]=1_6 , __snake_case : Tuple=2 , __snake_case : Optional[Any]=0.02 , __snake_case : str=3 , __snake_case : List[str]=4 , __snake_case : Optional[int]=None , ) -> Optional[int]:
__magic_name__: Any = parent
__magic_name__: Optional[Any] = batch_size
__magic_name__: Optional[Any] = seq_length
__magic_name__: Optional[Any] = is_training
__magic_name__: str = use_input_mask
__magic_name__: Optional[Any] = use_token_type_ids
__magic_name__: List[str] = use_labels
__magic_name__: List[str] = vocab_size
__magic_name__: Union[str, Any] = hidden_size
__magic_name__: List[Any] = num_hidden_layers
__magic_name__: Dict = num_attention_heads
__magic_name__: int = intermediate_size
__magic_name__: List[Any] = hidden_act
__magic_name__: int = hidden_dropout_prob
__magic_name__: List[str] = attention_probs_dropout_prob
__magic_name__: Tuple = max_position_embeddings
__magic_name__: Any = type_vocab_size
__magic_name__: Union[str, Any] = type_sequence_label_size
__magic_name__: List[str] = initializer_range
__magic_name__: Optional[Any] = num_labels
__magic_name__: List[str] = num_choices
__magic_name__: Tuple = scope
def lowerCamelCase__ ( self : Optional[int] ) -> Any:
__magic_name__: Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__: Dict = None
if self.use_input_mask:
__magic_name__: Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__: str = None
__magic_name__: Optional[Any] = None
__magic_name__: int = None
if self.use_labels:
__magic_name__: List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__: str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__magic_name__: Dict = ids_tensor([self.batch_size] , self.num_choices )
__magic_name__: Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ ( self : int ) -> Any:
__magic_name__: List[Any] = EsmConfig(
vocab_size=3_3 , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , is_folding_model=__snake_case , esmfold_config={"""trunk""": {"""num_blocks""": 2}, """fp16_esm""": False} , )
return config
def lowerCamelCase__ ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : List[Any] , __snake_case : Any , __snake_case : Union[str, Any] ) -> Optional[Any]:
__magic_name__: str = EsmForProteinFolding(config=__snake_case ).float()
model.to(__snake_case )
model.eval()
__magic_name__: str = model(__snake_case , attention_mask=__snake_case )
__magic_name__: Any = model(__snake_case )
__magic_name__: Dict = model(__snake_case )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 1_4, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def lowerCamelCase__ ( self : Any ) -> int:
__magic_name__: str = self.prepare_config_and_inputs()
(
(
__magic_name__
), (
__magic_name__
), (
__magic_name__
), (
__magic_name__
), (
__magic_name__
), (
__magic_name__
),
): Union[str, Any] = config_and_inputs
__magic_name__: Any = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __A ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,unittest.TestCase ):
UpperCAmelCase__ = False
UpperCAmelCase__ = (EsmForProteinFolding,) if is_torch_available() else ()
UpperCAmelCase__ = ()
UpperCAmelCase__ = {} if is_torch_available() else {}
UpperCAmelCase__ = False
def lowerCamelCase__ ( self : List[Any] ) -> Optional[Any]:
__magic_name__: Optional[int] = EsmFoldModelTester(self )
__magic_name__: Optional[Any] = ConfigTester(self , config_class=__snake_case , hidden_size=3_7 )
def lowerCamelCase__ ( self : Optional[int] ) -> Any:
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self : List[str] ) -> str:
__magic_name__: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
@unittest.skip("""Does not support attention outputs""" )
def lowerCamelCase__ ( self : List[str] ) -> str:
pass
@unittest.skip
def lowerCamelCase__ ( self : List[Any] ) -> Tuple:
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def lowerCamelCase__ ( self : List[Any] ) -> Optional[int]:
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def lowerCamelCase__ ( self : str ) -> str:
pass
@unittest.skip("""ESMFold does not support passing input embeds!""" )
def lowerCamelCase__ ( self : Optional[int] ) -> List[Any]:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def lowerCamelCase__ ( self : Any ) -> List[str]:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[Any]:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def lowerCamelCase__ ( self : int ) -> Tuple:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def lowerCamelCase__ ( self : Optional[int] ) -> Optional[Any]:
pass
@unittest.skip("""ESMFold does not support head pruning.""" )
def lowerCamelCase__ ( self : Optional[Any] ) -> Optional[Any]:
pass
@unittest.skip("""ESMFold does not output hidden states in the normal way.""" )
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[Any]:
pass
@unittest.skip("""ESMfold does not output hidden states in the normal way.""" )
def lowerCamelCase__ ( self : Any ) -> int:
pass
@unittest.skip("""ESMFold only has one output format.""" )
def lowerCamelCase__ ( self : Union[str, Any] ) -> str:
pass
@unittest.skip("""This test doesn't work for ESMFold and doesn't test core functionality""" )
def lowerCamelCase__ ( self : List[Any] ) -> Dict:
pass
@unittest.skip("""ESMFold does not support input chunking.""" )
def lowerCamelCase__ ( self : int ) -> Any:
pass
@unittest.skip("""ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments.""" )
def lowerCamelCase__ ( self : int ) -> Union[str, Any]:
pass
@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
def lowerCamelCase__ ( self : Any ) -> Optional[Any]:
pass
@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
def lowerCamelCase__ ( self : Optional[int] ) -> int:
pass
@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[str]:
pass
@unittest.skip("""ESMFold doesn't support data parallel.""" )
def lowerCamelCase__ ( self : List[str] ) -> str:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def lowerCamelCase__ ( self : Union[str, Any] ) -> Dict:
pass
@require_torch
class __A ( SCREAMING_SNAKE_CASE_ ):
@slow
def lowerCamelCase__ ( self : Optional[Any] ) -> List[Any]:
__magic_name__: Any = EsmForProteinFolding.from_pretrained("""facebook/esmfold_v1""" ).float()
model.eval()
__magic_name__: Optional[int] = torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
__magic_name__: Dict = model(__snake_case )["""positions"""]
__magic_name__: Tuple = torch.tensor([2.5828, 0.7993, -10.9334] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , __snake_case , atol=1E-4 ) )
| 96 | 1 |
from manim import *
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def a_ ( self : int ) -> Optional[Any]:
"""simple docstring"""
A__ = Rectangle(height=0.5 , width=0.5 )
A__ = Rectangle(height=0.2_5 , width=0.2_5 )
A__ = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
A__ = [mem.copy() for i in range(6 )]
A__ = [mem.copy() for i in range(6 )]
A__ = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = VGroup(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = Text("""CPU""" , font_size=24 )
A__ = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__lowerCAmelCase )
A__ = [mem.copy() for i in range(4 )]
A__ = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = Text("""GPU""" , font_size=24 )
A__ = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(__lowerCAmelCase )
A__ = [mem.copy() for i in range(6 )]
A__ = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = Text("""Model""" , font_size=24 )
A__ = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(__lowerCAmelCase )
A__ = []
A__ = []
A__ = []
for i, rect in enumerate(__lowerCAmelCase ):
rect.set_stroke(__lowerCAmelCase )
A__ = Rectangle(height=0.4_6 / 4 , width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(__lowerCAmelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=__lowerCAmelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=__lowerCAmelCase , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=__lowerCAmelCase , buff=0.0 )
self.add(__lowerCAmelCase )
model_cpu_arr.append(__lowerCAmelCase )
self.add(*__lowerCAmelCase , *__lowerCAmelCase , *__lowerCAmelCase )
A__ = [mem.copy() for i in range(6 )]
A__ = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = Text("""Loaded Checkpoint""" , font_size=24 )
A__ = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
checkpoint.move_to([3, 0.5, 0] )
self.add(__lowerCAmelCase )
A__ = []
A__ = []
for i, rect in enumerate(__lowerCAmelCase ):
A__ = fill.copy().set_fill(__lowerCAmelCase , opacity=0.7 )
target.move_to(__lowerCAmelCase )
ckpt_arr.append(__lowerCAmelCase )
A__ = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(__lowerCAmelCase )
self.add(*__lowerCAmelCase , *__lowerCAmelCase )
A__ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
A__ = MarkupText(
f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__lowerCAmelCase , __lowerCAmelCase )
A__ = MarkupText(
f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(__lowerCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(__lowerCAmelCase )
A__ = MarkupText(
f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.' , font_size=24 , )
step_a.move_to([2, 2, 0] )
A__ = [meta_mem.copy() for i in range(6 )]
A__ = [meta_mem.copy() for i in range(6 )]
A__ = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = VGroup(*__lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = VGroup(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0 )
A__ = Text("""Disk""" , font_size=24 )
A__ = Group(__lowerCAmelCase , __lowerCAmelCase ).arrange(__lowerCAmelCase , buff=0.5 , aligned_edge=__lowerCAmelCase )
disk.move_to([-4.0, -1.2_5, 0] )
self.play(Write(__lowerCAmelCase , run_time=3 ) , Write(__lowerCAmelCase , run_time=1 ) , Create(__lowerCAmelCase , run_time=1 ) )
A__ = []
for i, rect in enumerate(__lowerCAmelCase ):
A__ = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(__lowerCAmelCase , run_time=1.5 ) )
self.play(*__lowerCAmelCase )
self.play(FadeOut(__lowerCAmelCase ) )
A__ = MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCAmelCase , run_time=3 ) )
self.play(
FadeOut(__lowerCAmelCase , __lowerCAmelCase , *__lowerCAmelCase , *__lowerCAmelCase ) , )
self.wait()
| 247 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class A :
'''simple docstring'''
def __init__( self : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : Optional[int]=30 , __lowerCAmelCase : List[str]=2 , __lowerCAmelCase : Optional[int]=3 , __lowerCAmelCase : Optional[Any]=True , __lowerCAmelCase : str=True , __lowerCAmelCase : List[Any]=32 , __lowerCAmelCase : str=5 , __lowerCAmelCase : Union[str, Any]=4 , __lowerCAmelCase : Tuple=37 , __lowerCAmelCase : List[str]="gelu" , __lowerCAmelCase : List[Any]=0.1 , __lowerCAmelCase : Dict=0.1 , __lowerCAmelCase : str=10 , __lowerCAmelCase : Tuple=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Optional[Any]=2 , ) -> str:
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = is_training
A__ = use_labels
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = type_sequence_label_size
A__ = initializer_range
A__ = scope
A__ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
A__ = (image_size // patch_size) ** 2
A__ = num_patches + 2
def a_ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = self.get_config()
return config, pixel_values, labels
def a_ ( self : str ) -> Union[str, Any]:
"""simple docstring"""
return DeiTConfig(
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=__lowerCAmelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def a_ ( self : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int ) -> str:
"""simple docstring"""
A__ = DeiTModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
A__ = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ) -> Any:
"""simple docstring"""
A__ = DeiTForMaskedImageModeling(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
A__ = model(__lowerCAmelCase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A__ = 1
A__ = DeiTForMaskedImageModeling(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(__lowerCAmelCase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def a_ ( self : Tuple , __lowerCAmelCase : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any] ) -> List[str]:
"""simple docstring"""
A__ = self.type_sequence_label_size
A__ = DeiTForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
A__ = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A__ = 1
A__ = DeiTForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) ,
) = config_and_inputs
A__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class A (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : int = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__lowerCamelCase : Any = (
{
'''feature-extraction''': DeiTModel,
'''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : Union[str, Any] = False
def a_ ( self : Any ) -> Any:
"""simple docstring"""
A__ = DeiTModelTester(self )
A__ = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def a_ ( self : str ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def a_ ( self : int ) -> Any:
"""simple docstring"""
pass
def a_ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(__lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowerCAmelCase , nn.Linear ) )
def a_ ( self : Any ) -> List[str]:
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(__lowerCAmelCase )
A__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def a_ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def a_ ( self : Optional[Any] ) -> str:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase )
def a_ ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
def a_ ( self : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any=False ) -> Dict:
"""simple docstring"""
A__ = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def a_ ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(__lowerCAmelCase )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
A__ = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
A__ = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
A__ = model(**__lowerCAmelCase ).loss
loss.backward()
def a_ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
A__ = False
A__ = True
for model_class in self.all_model_classes:
if model_class in get_values(__lowerCAmelCase ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
A__ = model_class(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.to(__lowerCAmelCase )
model.train()
A__ = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
A__ = model(**__lowerCAmelCase ).loss
loss.backward()
def a_ ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = [
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}' ):
A__ = problem_type["""title"""]
A__ = problem_type["""num_labels"""]
A__ = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
A__ = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if problem_type["num_labels"] > 1:
A__ = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
A__ = inputs["""labels"""].to(problem_type["""dtype"""] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=__lowerCAmelCase ) as warning_list:
A__ = model(**__lowerCAmelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f'Something is going wrong in the regression problem: intercepted {w.message}' )
loss.backward()
@slow
def a_ ( self : Any ) -> Dict:
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = DeiTModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def __lowerCamelCase ( ) -> Dict:
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class A (unittest.TestCase ):
'''simple docstring'''
@cached_property
def a_ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def a_ ( self : Dict ) -> str:
"""simple docstring"""
A__ = DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" ).to(
__lowerCAmelCase )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=__lowerCAmelCase , return_tensors="""pt""" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
A__ = model(**__lowerCAmelCase )
# verify the logits
A__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
A__ = torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def a_ ( self : Dict ) -> Dict:
"""simple docstring"""
A__ = DeiTModel.from_pretrained(
"""facebook/deit-base-distilled-patch16-224""" , torch_dtype=torch.floataa , device_map="""auto""" )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=__lowerCAmelCase , return_tensors="""pt""" )
A__ = inputs.pixel_values.to(__lowerCAmelCase )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A__ = model(__lowerCAmelCase )
| 247 | 1 |
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
__snake_case : Union[str, Any] = get_logger(__name__)
__snake_case : Dict = R'''\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n search or log softmax for each vocabulary token when using beam search\n kwargs (`Dict[str, Any]`, *optional*):\n Additional logits processor specific kwargs.\n\n Return:\n `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'''
class lowercase_ :
@add_start_docstrings(_UpperCamelCase )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class lowercase_ :
@add_start_docstrings(_UpperCamelCase )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class lowercase_ ( __lowerCamelCase ):
@add_start_docstrings(_UpperCamelCase )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
for processor in self:
UpperCAmelCase_ = inspect.signature(processor.__call__ ).parameters
if len(_UpperCamelCase ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
F"""Make sure that all the required parameters: {list(function_args.keys() )} for """
F"""{processor.__class__} are passed to the logits processor.""" )
UpperCAmelCase_ = processor(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , **_UpperCamelCase )
else:
UpperCAmelCase_ = processor(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
return scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not (temperature > 0):
raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}""" )
UpperCAmelCase_ = temperature
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
UpperCAmelCase_ = scores / self.temperature
return scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ = -float("Inf" ) , UpperCamelCase__ = 1 ) -> Optional[int]:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or (top_p < 0 or top_p > 1.0):
raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}""" )
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or (min_tokens_to_keep < 1):
raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}""" )
UpperCAmelCase_ = top_p
UpperCAmelCase_ = filter_value
UpperCAmelCase_ = min_tokens_to_keep
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = lax.top_k(_UpperCamelCase , scores.shape[-1] )
UpperCAmelCase_ = jnp.full_like(_UpperCamelCase , self.filter_value )
UpperCAmelCase_ = jax.nn.softmax(_UpperCamelCase , axis=-1 ).cumsum(axis=-1 )
UpperCAmelCase_ = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
UpperCAmelCase_ = jnp.roll(_UpperCamelCase , 1 )
score_mask |= score_mask.at[:, 0].set(_UpperCamelCase )
# min tokens to keep
UpperCAmelCase_ = score_mask.at[:, : self.min_tokens_to_keep].set(_UpperCamelCase )
UpperCAmelCase_ = jnp.where(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ = jax.lax.sort_key_val(_UpperCamelCase , _UpperCamelCase )[-1]
return next_scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ = -float("Inf" ) , UpperCamelCase__ = 1 ) -> List[Any]:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or top_k <= 0:
raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}""" )
UpperCAmelCase_ = max(_UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ = filter_value
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = scores.shape
UpperCAmelCase_ = jnp.full(batch_size * vocab_size , self.filter_value )
UpperCAmelCase_ = min(self.top_k , scores.shape[-1] ) # Safety check
UpperCAmelCase_ , UpperCAmelCase_ = lax.top_k(_UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ = jnp.broadcast_to((jnp.arange(_UpperCamelCase ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
UpperCAmelCase_ = topk_scores.flatten()
UpperCAmelCase_ = topk_indices.flatten() + shift
UpperCAmelCase_ = next_scores_flat.at[topk_indices_flat].set(_UpperCamelCase )
UpperCAmelCase_ = next_scores_flat.reshape(_UpperCamelCase , _UpperCamelCase )
return next_scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = bos_token_id
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
UpperCAmelCase_ = jnp.full(scores.shape , -float("inf" ) )
UpperCAmelCase_ = 1 - jnp.bool_(cur_len - 1 )
UpperCAmelCase_ = jnp.where(_UpperCamelCase , new_scores.at[:, self.bos_token_id].set(0 ) , _UpperCamelCase )
return scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = max_length
UpperCAmelCase_ = eos_token_id
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
UpperCAmelCase_ = jnp.full(scores.shape , -float("inf" ) )
UpperCAmelCase_ = 1 - jnp.bool_(cur_len - self.max_length + 1 )
UpperCAmelCase_ = jnp.where(_UpperCamelCase , new_scores.at[:, self.eos_token_id].set(0 ) , _UpperCamelCase )
return scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or min_length < 0:
raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}""" )
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or eos_token_id < 0:
raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}""" )
UpperCAmelCase_ = min_length
UpperCAmelCase_ = eos_token_id
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
UpperCAmelCase_ = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
UpperCAmelCase_ = jnp.where(_UpperCamelCase , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , _UpperCamelCase )
return scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
UpperCAmelCase_ = list(_UpperCamelCase )
UpperCAmelCase_ = begin_index
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = 1 - jnp.bool_(cur_len - self.begin_index )
UpperCAmelCase_ = jnp.where(_UpperCamelCase , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , _UpperCamelCase )
return scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = list(_UpperCamelCase )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
UpperCAmelCase_ = scores.at[..., self.suppress_tokens].set(-float("inf" ) )
return scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = dict(_UpperCamelCase )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
UpperCAmelCase_ = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
UpperCAmelCase_ = force_token_array.at[index].set(_UpperCamelCase )
UpperCAmelCase_ = jnp.intaa(_UpperCamelCase )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> jnp.ndarray:
"""simple docstring"""
def _force_token(UpperCamelCase__ ):
UpperCAmelCase_ = scores.shape[0]
UpperCAmelCase_ = self.force_token_array[generation_idx]
UpperCAmelCase_ = jnp.ones_like(_UpperCamelCase , dtype=scores.dtype ) * -float("inf" )
UpperCAmelCase_ = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
UpperCAmelCase_ = lax.dynamic_update_slice(_UpperCamelCase , _UpperCamelCase , (0, current_token) )
return new_scores
UpperCAmelCase_ = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(_UpperCamelCase ) , lambda: scores , ) , )
return scores
class lowercase_ ( __lowerCamelCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = generate_config.eos_token_id
UpperCAmelCase_ = generate_config.no_timestamps_token_id
UpperCAmelCase_ = generate_config.no_timestamps_token_id + 1
UpperCAmelCase_ = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(_UpperCamelCase , "max_initial_timestamp_index" ):
UpperCAmelCase_ = generate_config.max_initial_timestamp_index
else:
UpperCAmelCase_ = model_config.vocab_size
if self.max_initial_timestamp_index is None:
UpperCAmelCase_ = model_config.vocab_size
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) )
def handle_pairs(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = jnp.where((cur_len - self.begin_index) >= 1 , _UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , _UpperCamelCase , )
UpperCAmelCase_ = jnp.where((cur_len - self.begin_index) < 2 , _UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , _UpperCamelCase , _UpperCamelCase , )
return jnp.where(
_UpperCamelCase , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , _UpperCamelCase , )
UpperCAmelCase_ = jax.vmap(_UpperCamelCase )(_UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ = jnp.where(cur_len == self.begin_index , _UpperCamelCase , _UpperCamelCase )
UpperCAmelCase_ = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , _UpperCamelCase , )
UpperCAmelCase_ = self.timestamp_begin + self.max_initial_timestamp_index
UpperCAmelCase_ = jnp.where(
_UpperCamelCase , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , _UpperCamelCase , )
# if sum of probability over timestamps is above any other token, sample timestamp
UpperCAmelCase_ = jax.nn.log_softmax(_UpperCamelCase , axis=-1 )
def handle_cumulative_probs(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
UpperCAmelCase_ = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , _UpperCamelCase , )
UpperCAmelCase_ = jax.vmap(_UpperCamelCase )(_UpperCamelCase , _UpperCamelCase )
return scores
| 660 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCAmelCase_ ( ):
SCREAMING_SNAKE_CASE__ =ArgumentParser("""Diffusers CLI tool""", usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE__ =parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(__UpperCamelCase )
# Let's go
SCREAMING_SNAKE_CASE__ =parser.parse_args()
if not hasattr(__UpperCamelCase, """func""" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE__ =args.func(__UpperCamelCase )
service.run()
if __name__ == "__main__":
main()
| 151 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCamelCase_ ( ) -> int:
UpperCAmelCase_ : Dict = '''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
UpperCAmelCase_ : Optional[Any] = Image.open(requests.get(SCREAMING_SNAKE_CASE__, stream=SCREAMING_SNAKE_CASE__ ).raw ).convert('''RGB''' )
return image
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : List[str] ) -> Optional[Any]:
UpperCAmelCase_ : Any = []
# fmt: off
# vision encoder
rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''') )
rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''') )
# fmt: on
return rename_keys
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Union[str, Any], SCREAMING_SNAKE_CASE__ : Union[str, Any], SCREAMING_SNAKE_CASE__ : List[Any] ) -> Any:
UpperCAmelCase_ : int = dct.pop(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Union[str, Any] = val
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : List[Any], SCREAMING_SNAKE_CASE__ : Tuple ) -> List[str]:
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
UpperCAmelCase_ : Optional[int] = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias""" )
UpperCAmelCase_ : Any = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
UpperCAmelCase_ : Dict = torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE__, requires_grad=SCREAMING_SNAKE_CASE__ ), v_bias) )
UpperCAmelCase_ : Union[str, Any] = qkv_bias
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Dict, SCREAMING_SNAKE_CASE__ : Optional[int] ) -> str:
UpperCAmelCase_ : Optional[int] = 364 if '''coco''' in model_name else 224
UpperCAmelCase_ : List[str] = BlipaVisionConfig(image_size=SCREAMING_SNAKE_CASE__ ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
UpperCAmelCase_ : Dict = OPTConfig.from_pretrained('''facebook/opt-2.7b''', eos_token_id=SCREAMING_SNAKE_CASE__ ).to_dict()
elif "opt-6.7b" in model_name:
UpperCAmelCase_ : int = OPTConfig.from_pretrained('''facebook/opt-6.7b''', eos_token_id=SCREAMING_SNAKE_CASE__ ).to_dict()
elif "t5-xl" in model_name:
UpperCAmelCase_ : Any = TaConfig.from_pretrained('''google/flan-t5-xl''', dense_act_fn='''gelu''', bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
UpperCAmelCase_ : str = TaConfig.from_pretrained('''google/flan-t5-xxl''', dense_act_fn='''gelu''', bos_token_id=1 ).to_dict()
UpperCAmelCase_ : str = BlipaConfig(vision_config=SCREAMING_SNAKE_CASE__, text_config=SCREAMING_SNAKE_CASE__ )
return config, image_size
@torch.no_grad()
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : str, SCREAMING_SNAKE_CASE__ : List[str]=None, SCREAMING_SNAKE_CASE__ : Tuple=False ) -> Optional[Any]:
UpperCAmelCase_ : Union[str, Any] = (
AutoTokenizer.from_pretrained('''facebook/opt-2.7b''' )
if '''opt''' in model_name
else AutoTokenizer.from_pretrained('''google/flan-t5-xl''' )
)
UpperCAmelCase_ : Union[str, Any] = tokenizer('''\n''', add_special_tokens=SCREAMING_SNAKE_CASE__ ).input_ids[0]
UpperCAmelCase_ : List[str] = get_blipa_config(SCREAMING_SNAKE_CASE__, eos_token_id=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Optional[int] = BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE__ ).eval()
UpperCAmelCase_ : Optional[int] = {
'''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
'''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
'''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
'''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
'''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
'''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
'''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
}
UpperCAmelCase_ : List[str] = model_name_to_original[model_name]
# load original model
print('''Loading original model...''' )
UpperCAmelCase_ : List[Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
UpperCAmelCase_ : List[Any] = load_model_and_preprocess(
name=SCREAMING_SNAKE_CASE__, model_type=SCREAMING_SNAKE_CASE__, is_eval=SCREAMING_SNAKE_CASE__, device=SCREAMING_SNAKE_CASE__ )
original_model.eval()
print('''Done!''' )
# update state dict keys
UpperCAmelCase_ : Union[str, Any] = original_model.state_dict()
UpperCAmelCase_ : Dict = create_rename_keys(SCREAMING_SNAKE_CASE__ )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
UpperCAmelCase_ : int = state_dict.pop(SCREAMING_SNAKE_CASE__ )
if key.startswith('''Qformer.bert''' ):
UpperCAmelCase_ : Optional[int] = key.replace('''Qformer.bert''', '''qformer''' )
if "attention.self" in key:
UpperCAmelCase_ : Any = key.replace('''self''', '''attention''' )
if "opt_proj" in key:
UpperCAmelCase_ : Tuple = key.replace('''opt_proj''', '''language_projection''' )
if "t5_proj" in key:
UpperCAmelCase_ : int = key.replace('''t5_proj''', '''language_projection''' )
if key.startswith('''opt''' ):
UpperCAmelCase_ : int = key.replace('''opt''', '''language''' )
if key.startswith('''t5''' ):
UpperCAmelCase_ : Union[str, Any] = key.replace('''t5''', '''language''' )
UpperCAmelCase_ : str = val
# read in qv biases
read_in_q_v_bias(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Optional[Any] = hf_model.load_state_dict(SCREAMING_SNAKE_CASE__, strict=SCREAMING_SNAKE_CASE__ )
assert len(SCREAMING_SNAKE_CASE__ ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
UpperCAmelCase_ : Optional[Any] = load_demo_image()
UpperCAmelCase_ : Optional[int] = vis_processors['''eval'''](SCREAMING_SNAKE_CASE__ ).unsqueeze(0 ).to(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : List[Any] = tokenizer(['''\n'''], return_tensors='''pt''' ).input_ids.to(SCREAMING_SNAKE_CASE__ )
# create processor
UpperCAmelCase_ : Any = BlipImageProcessor(
size={'''height''': image_size, '''width''': image_size}, image_mean=SCREAMING_SNAKE_CASE__, image_std=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Tuple = BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE__, tokenizer=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Dict = processor(images=SCREAMING_SNAKE_CASE__, return_tensors='''pt''' ).pixel_values.to(SCREAMING_SNAKE_CASE__ )
# make sure processor creates exact same pixel values
assert torch.allclose(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
original_model.to(SCREAMING_SNAKE_CASE__ )
hf_model.to(SCREAMING_SNAKE_CASE__ )
with torch.no_grad():
if "opt" in model_name:
UpperCAmelCase_ : List[Any] = original_model({'''image''': original_pixel_values, '''text_input''': ['''''']} ).logits
UpperCAmelCase_ : Any = hf_model(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ).logits
else:
UpperCAmelCase_ : List[Any] = original_model(
{'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']} ).logits
UpperCAmelCase_ : Dict = input_ids.masked_fill(input_ids == tokenizer.pad_token_id, -100 )
UpperCAmelCase_ : List[str] = hf_model(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, labels=SCREAMING_SNAKE_CASE__ ).logits
assert original_logits.shape == logits.shape
print('''First values of original logits:''', original_logits[0, :3, :3] )
print('''First values of HF logits:''', logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
UpperCAmelCase_ : Any = torch.tensor(
[[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]], device=SCREAMING_SNAKE_CASE__ )
assert torch.allclose(logits[0, :3, :3], SCREAMING_SNAKE_CASE__, atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
UpperCAmelCase_ : Optional[Any] = torch.tensor(
[[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]], device=SCREAMING_SNAKE_CASE__ )
else:
# cast to same type
UpperCAmelCase_ : List[str] = logits.dtype
assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE__ ), SCREAMING_SNAKE_CASE__, atol=1E-2 )
print('''Looks ok!''' )
print('''Generating a caption...''' )
UpperCAmelCase_ : Tuple = ''''''
UpperCAmelCase_ : Any = tokenizer(SCREAMING_SNAKE_CASE__, return_tensors='''pt''' ).input_ids.to(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : int = original_model.generate({'''image''': original_pixel_values} )
UpperCAmelCase_ : str = hf_model.generate(
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, do_sample=SCREAMING_SNAKE_CASE__, num_beams=5, max_length=30, min_length=1, top_p=0.9, repetition_penalty=1.0, length_penalty=1.0, temperature=1, )
print('''Original generation:''', SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : str = input_ids.shape[1]
UpperCAmelCase_ : Tuple = processor.batch_decode(outputs[:, prompt_length:], skip_special_tokens=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Union[str, Any] = [text.strip() for text in output_text]
print('''HF generation:''', SCREAMING_SNAKE_CASE__ )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(SCREAMING_SNAKE_CASE__ )
hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if push_to_hub:
processor.push_to_hub(F"""nielsr/{model_name}""" )
hf_model.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
lowerCamelCase : int = argparse.ArgumentParser()
lowerCamelCase : int = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
lowerCamelCase : List[str] = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 707 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __a (lowerCamelCase , unittest.TestCase ):
__a : List[str] = BlenderbotSmallTokenizer
__a : List[Any] = False
def UpperCAmelCase__ ( self : str ) -> str:
"""simple docstring"""
super().setUp()
UpperCAmelCase_ : Tuple = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
UpperCAmelCase_ : Optional[Any] = dict(zip(__magic_name__ , range(len(__magic_name__ ) ) ) )
UpperCAmelCase_ : int = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
UpperCAmelCase_ : Optional[Any] = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
UpperCAmelCase_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase_ : Dict = 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(__magic_name__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__magic_name__ ) )
def UpperCAmelCase__ ( self : List[Any] , **__magic_name__ : Dict ) -> Tuple:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__magic_name__ )
def UpperCAmelCase__ ( self : Optional[int] , __magic_name__ : List[str] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ : str = '''adapt act apte'''
UpperCAmelCase_ : Tuple = '''adapt act apte'''
return input_text, output_text
def UpperCAmelCase__ ( self : str ) -> Any:
"""simple docstring"""
UpperCAmelCase_ : str = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCAmelCase_ : List[Any] = '''adapt act apte'''
UpperCAmelCase_ : Dict = ['''adapt''', '''act''', '''ap@@''', '''te''']
UpperCAmelCase_ : Dict = tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
UpperCAmelCase_ : Tuple = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
UpperCAmelCase_ : Dict = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , __magic_name__ )
def UpperCAmelCase__ ( self : int ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [13_84]
UpperCAmelCase_ : Optional[int] = '''I am a small frog.'''
UpperCAmelCase_ : List[str] = tok([src_text] , padding=__magic_name__ , truncation=__magic_name__ )['''input_ids''']
UpperCAmelCase_ : Dict = tok.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ , clean_up_tokenization_spaces=__magic_name__ )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def UpperCAmelCase__ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ : int = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
UpperCAmelCase_ : List[Any] = '''I am a small frog .'''
UpperCAmelCase_ : Any = '''.'''
UpperCAmelCase_ : List[Any] = tok(__magic_name__ )['''input_ids''']
UpperCAmelCase_ : Optional[int] = tok(__magic_name__ )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 644 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : Dict = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
__lowerCAmelCase : int = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
__lowerCAmelCase : List[str] = {"facebook/blenderbot-3B": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase ( ):
"""simple docstring"""
__UpperCAmelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
__UpperCAmelCase = bs[:]
__UpperCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(UpperCamelCase__ )
cs.append(2**8 + n )
n += 1
__UpperCAmelCase = [chr(UpperCamelCase__ ) for n in cs]
return dict(zip(UpperCamelCase__ , UpperCamelCase__ ) )
def lowerCAmelCase ( UpperCamelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase = set()
__UpperCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__UpperCAmelCase = char
return pairs
class A ( UpperCAmelCase ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ['''input_ids''', '''attention_mask''']
def __init__( self : str , __a : Union[str, Any] , __a : Optional[Any] , __a : List[Any]="replace" , __a : Union[str, Any]="<s>" , __a : Any="</s>" , __a : Dict="</s>" , __a : Dict="<s>" , __a : Tuple="<unk>" , __a : List[str]="<pad>" , __a : Any="<mask>" , __a : Dict=False , **__a : Union[str, Any] , ) -> Optional[int]:
__UpperCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else bos_token
__UpperCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else eos_token
__UpperCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else sep_token
__UpperCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else cls_token
__UpperCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else unk_token
__UpperCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__UpperCAmelCase = AddedToken(__a , lstrip=__a , rstrip=__a ) if isinstance(__a , __a ) else mask_token
super().__init__(
errors=__a , bos_token=__a , eos_token=__a , unk_token=__a , sep_token=__a , cls_token=__a , pad_token=__a , mask_token=__a , add_prefix_space=__a , **__a , )
with open(__a , encoding='''utf-8''' ) as vocab_handle:
__UpperCAmelCase = json.load(__a )
__UpperCAmelCase = {v: k for k, v in self.encoder.items()}
__UpperCAmelCase = errors # how to handle errors in decoding
__UpperCAmelCase = bytes_to_unicode()
__UpperCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(__a , encoding='''utf-8''' ) as merges_handle:
__UpperCAmelCase = merges_handle.read().split('''\n''' )[1:-1]
__UpperCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
__UpperCAmelCase = dict(zip(__a , range(len(__a ) ) ) )
__UpperCAmelCase = {}
__UpperCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__UpperCAmelCase = re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def snake_case__ ( self : List[Any] ) -> Union[str, Any]:
return len(self.encoder )
def snake_case__ ( self : str ) -> int:
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case__ ( self : List[Any] , __a : Tuple ) -> List[Any]:
if token in self.cache:
return self.cache[token]
__UpperCAmelCase = tuple(__a )
__UpperCAmelCase = get_pairs(__a )
if not pairs:
return token
while True:
__UpperCAmelCase = min(__a , key=lambda __a : self.bpe_ranks.get(__a , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__UpperCAmelCase , __UpperCAmelCase = bigram
__UpperCAmelCase = []
__UpperCAmelCase = 0
while i < len(__a ):
try:
__UpperCAmelCase = word.index(__a , __a )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__UpperCAmelCase = j
if word[i] == first and i < len(__a ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__UpperCAmelCase = tuple(__a )
__UpperCAmelCase = new_word
if len(__a ) == 1:
break
else:
__UpperCAmelCase = get_pairs(__a )
__UpperCAmelCase = ''' '''.join(__a )
__UpperCAmelCase = word
return word
def snake_case__ ( self : int , __a : int ) -> List[Any]:
__UpperCAmelCase = []
for token in re.findall(self.pat , __a ):
__UpperCAmelCase = ''''''.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(__a ).split(''' ''' ) )
return bpe_tokens
def snake_case__ ( self : Optional[Any] , __a : Tuple ) -> str:
return self.encoder.get(__a , self.encoder.get(self.unk_token ) )
def snake_case__ ( self : Optional[int] , __a : Any ) -> List[str]:
return self.decoder.get(__a )
def snake_case__ ( self : Union[str, Any] , __a : List[str] ) -> List[Any]:
__UpperCAmelCase = ''''''.join(__a )
__UpperCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def snake_case__ ( self : Union[str, Any] , __a : str , __a : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__a ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__UpperCAmelCase = os.path.join(
__a , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__UpperCAmelCase = os.path.join(
__a , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__a , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__a , ensure_ascii=__a ) + '''\n''' )
__UpperCAmelCase = 0
with open(__a , '''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 __a : 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!''' )
__UpperCAmelCase = token_index
writer.write(''' '''.join(__a ) + '''\n''' )
index += 1
return vocab_file, merge_file
def snake_case__ ( self : Union[str, Any] , __a : List[int] , __a : Optional[List[int]] = None , __a : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__a , token_ids_a=__a , already_has_special_tokens=__a )
if token_ids_a is None:
return [1] + ([0] * len(__a )) + [1]
return [1] + ([0] * len(__a )) + [1, 1] + ([0] * len(__a )) + [1]
def snake_case__ ( self : Dict , __a : List[int] , __a : Optional[List[int]] = None ) -> List[int]:
__UpperCAmelCase = [self.sep_token_id]
__UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case__ ( self : int , __a : Optional[int] , __a : int=False , **__a : Union[str, Any] ) -> Union[str, Any]:
__UpperCAmelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__a ) > 0 and not text[0].isspace()):
__UpperCAmelCase = ''' ''' + text
return (text, kwargs)
def snake_case__ ( self : List[str] , __a : List[int] , __a : Optional[List[int]] = None ) -> Dict:
return token_ids_a + [self.eos_token_id]
def snake_case__ ( self : Optional[Any] , __a : "Conversation" ) -> List[int]:
__UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(__a )
__UpperCAmelCase = ''' '''.join(__a )
__UpperCAmelCase = self.encode(__a )
if len(__a ) > self.model_max_length:
__UpperCAmelCase = input_ids[-self.model_max_length :]
logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 262 | '''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class A ( UpperCAmelCase ):
a_ = '''time_series_transformer'''
a_ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : str , __a : Optional[int] = None , __a : Optional[int] = None , __a : str = "student_t" , __a : str = "nll" , __a : int = 1 , __a : List[int] = [1, 2, 3, 4, 5, 6, 7] , __a : Optional[Union[str, bool]] = "mean" , __a : int = 0 , __a : int = 0 , __a : int = 0 , __a : int = 0 , __a : Optional[List[int]] = None , __a : Optional[List[int]] = None , __a : int = 3_2 , __a : int = 3_2 , __a : int = 2 , __a : int = 2 , __a : int = 2 , __a : int = 2 , __a : bool = True , __a : str = "gelu" , __a : int = 6_4 , __a : float = 0.1 , __a : float = 0.1 , __a : float = 0.1 , __a : float = 0.1 , __a : float = 0.1 , __a : int = 1_0_0 , __a : float = 0.0_2 , __a : Optional[int]=True , **__a : str , ) -> int:
# time series specific configuration
__UpperCAmelCase = prediction_length
__UpperCAmelCase = context_length or prediction_length
__UpperCAmelCase = distribution_output
__UpperCAmelCase = loss
__UpperCAmelCase = input_size
__UpperCAmelCase = num_time_features
__UpperCAmelCase = lags_sequence
__UpperCAmelCase = scaling
__UpperCAmelCase = num_dynamic_real_features
__UpperCAmelCase = num_static_real_features
__UpperCAmelCase = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(__a ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
__UpperCAmelCase = cardinality
else:
__UpperCAmelCase = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(__a ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
__UpperCAmelCase = embedding_dimension
else:
__UpperCAmelCase = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
__UpperCAmelCase = num_parallel_samples
# Transformer architecture configuration
__UpperCAmelCase = input_size * len(__a ) + self._number_of_features
__UpperCAmelCase = d_model
__UpperCAmelCase = encoder_attention_heads
__UpperCAmelCase = decoder_attention_heads
__UpperCAmelCase = encoder_ffn_dim
__UpperCAmelCase = decoder_ffn_dim
__UpperCAmelCase = encoder_layers
__UpperCAmelCase = decoder_layers
__UpperCAmelCase = dropout
__UpperCAmelCase = attention_dropout
__UpperCAmelCase = activation_dropout
__UpperCAmelCase = encoder_layerdrop
__UpperCAmelCase = decoder_layerdrop
__UpperCAmelCase = activation_function
__UpperCAmelCase = init_std
__UpperCAmelCase = use_cache
super().__init__(is_encoder_decoder=__a , **__a )
@property
def snake_case__ ( self : List[Any] ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 262 | 1 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : set , SCREAMING_SNAKE_CASE : set , SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : PriorityQueue , SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : float | int , ):
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
__lowerCamelCase : Tuple =cst_fwd.get(SCREAMING_SNAKE_CASE , np.inf )
__lowerCamelCase : str =cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
__lowerCamelCase : Union[str, Any] =new_cost_f
__lowerCamelCase : List[Any] =v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
__lowerCamelCase : Tuple =cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : dict ):
'''simple docstring'''
__lowerCamelCase : List[Any] =-1
__lowerCamelCase : Any =set()
__lowerCamelCase : List[Any] =set()
__lowerCamelCase : str ={source: 0}
__lowerCamelCase : List[str] ={destination: 0}
__lowerCamelCase : Optional[int] ={source: None}
__lowerCamelCase : Optional[Any] ={destination: None}
__lowerCamelCase : PriorityQueue[Any] =PriorityQueue()
__lowerCamelCase : PriorityQueue[Any] =PriorityQueue()
__lowerCamelCase : int =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():
__lowerCamelCase , __lowerCamelCase : Union[str, Any] =queue_forward.get()
visited_forward.add(SCREAMING_SNAKE_CASE )
__lowerCamelCase , __lowerCamelCase : List[str] =queue_backward.get()
visited_backward.add(SCREAMING_SNAKE_CASE )
__lowerCamelCase : Optional[int] =pass_and_relaxation(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , )
__lowerCamelCase : int =pass_and_relaxation(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
__lowerCamelCase : Union[str, Any] =shortest_distance
return shortest_path_distance
_UpperCamelCase = {
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
_UpperCamelCase = {
'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()
| 363 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE_ ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
__snake_case : Union[str, Any] = LDMTextToImagePipeline
__snake_case : Optional[Any] = TEXT_TO_IMAGE_PARAMS - {
"""negative_prompt""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
"""prompt_embeds""",
}
__snake_case : str = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
__snake_case : Union[str, Any] = TEXT_TO_IMAGE_BATCH_PARAMS
__snake_case : Optional[Any] = False
def __lowercase ( self :List[str] ):
torch.manual_seed(0 )
__lowerCamelCase : str =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
__lowerCamelCase : str =DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__lowercase , set_alpha_to_one=__lowercase , )
torch.manual_seed(0 )
__lowerCamelCase : Optional[int] =AutoencoderKL(
block_out_channels=(32, 64) , in_channels=3 , out_channels=3 , down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''') , up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''') , latent_channels=4 , )
torch.manual_seed(0 )
__lowerCamelCase : Any =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowerCamelCase : Optional[int] =CLIPTextModel(__lowercase )
__lowerCamelCase : Dict =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__lowerCamelCase : Optional[int] ={
'''unet''': unet,
'''scheduler''': scheduler,
'''vqvae''': vae,
'''bert''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def __lowercase ( self :int , __lowercase :Optional[int] , __lowercase :Optional[Any]=0 ):
if str(__lowercase ).startswith('''mps''' ):
__lowerCamelCase : Any =torch.manual_seed(__lowercase )
else:
__lowerCamelCase : str =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
__lowerCamelCase : Any ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def __lowercase ( self :List[str] ):
__lowerCamelCase : List[str] ='''cpu''' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase : str =self.get_dummy_components()
__lowerCamelCase : Optional[int] =LDMTextToImagePipeline(**__lowercase )
pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__lowerCamelCase : str =self.get_dummy_inputs(__lowercase )
__lowerCamelCase : List[Any] =pipe(**__lowercase ).images
__lowerCamelCase : Optional[int] =image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
__lowerCamelCase : Optional[Any] =np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self :Any ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self :int , __lowercase :Any , __lowercase :Optional[int]=torch.floataa , __lowercase :Dict=0 ):
__lowerCamelCase : List[str] =torch.manual_seed(__lowercase )
__lowerCamelCase : List[str] =np.random.RandomState(__lowercase ).standard_normal((1, 4, 32, 32) )
__lowerCamelCase : List[str] =torch.from_numpy(__lowercase ).to(device=__lowercase , dtype=__lowercase )
__lowerCamelCase : Any ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def __lowercase ( self :Tuple ):
__lowerCamelCase : int =LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256''' ).to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__lowerCamelCase : Tuple =self.get_inputs(__lowercase )
__lowerCamelCase : Optional[Any] =pipe(**__lowercase ).images
__lowerCamelCase : Union[str, Any] =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
__lowerCamelCase : Union[str, Any] =np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878] )
__lowerCamelCase : Dict =np.abs(expected_slice - image_slice ).max()
assert max_diff < 1e-3
@nightly
@require_torch_gpu
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self :Any ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self :Dict , __lowercase :Optional[Any] , __lowercase :int=torch.floataa , __lowercase :Dict=0 ):
__lowerCamelCase : Any =torch.manual_seed(__lowercase )
__lowerCamelCase : Dict =np.random.RandomState(__lowercase ).standard_normal((1, 4, 32, 32) )
__lowerCamelCase : str =torch.from_numpy(__lowercase ).to(device=__lowercase , dtype=__lowercase )
__lowerCamelCase : Dict ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 50,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def __lowercase ( self :Tuple ):
__lowerCamelCase : Optional[int] =LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256''' ).to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__lowerCamelCase : List[Any] =self.get_inputs(__lowercase )
__lowerCamelCase : Optional[int] =pipe(**__lowercase ).images[0]
__lowerCamelCase : Optional[int] =load_numpy(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy''' )
__lowerCamelCase : Dict =np.abs(expected_image - image ).max()
assert max_diff < 1e-3
| 363 | 1 |
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
A__ : Tuple =1
for i in range(1, num + 1 ):
fact *= i
return fact
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
A__ : Optional[Any] =0
while number > 0:
A__ : List[Any] =number % 1_0
sum_of_digits += last_digit
A__ : str =number // 1_0 # Removing the last_digit from the given number
return sum_of_digits
def SCREAMING_SNAKE_CASE__ ( snake_case_ = 1_0_0 ) -> int:
A__ : List[str] =factorial(snake_case_ )
A__ : str =split_and_add(snake_case_ )
return result
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 416 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : Optional[int] = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}
class a ( UpperCamelCase_ ):
__lowercase = """luke"""
def __init__( self , __UpperCamelCase=5_02_67 , __UpperCamelCase=50_00_00 , __UpperCamelCase=7_68 , __UpperCamelCase=2_56 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=30_72 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=5_12 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-12 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=2 , **__UpperCamelCase , )-> Dict:
'''simple docstring'''
super().__init__(pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
A__ : List[str] =vocab_size
A__ : Any =entity_vocab_size
A__ : List[Any] =hidden_size
A__ : Union[str, Any] =entity_emb_size
A__ : Dict =num_hidden_layers
A__ : List[Any] =num_attention_heads
A__ : List[str] =hidden_act
A__ : Dict =intermediate_size
A__ : Any =hidden_dropout_prob
A__ : Dict =attention_probs_dropout_prob
A__ : Optional[int] =max_position_embeddings
A__ : int =type_vocab_size
A__ : Optional[Any] =initializer_range
A__ : Optional[int] =layer_norm_eps
A__ : str =use_entity_aware_attention
A__ : str =classifier_dropout
| 416 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase_ : str = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : Any = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
lowerCAmelCase_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 464 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
def __init__( self : str , lowerCAmelCase__ : int , lowerCAmelCase__ : str=13 , lowerCAmelCase__ : List[Any]=7 , lowerCAmelCase__ : Optional[int]=True , lowerCAmelCase__ : Dict=True , lowerCAmelCase__ : Union[str, Any]=False , lowerCAmelCase__ : Tuple=True , lowerCAmelCase__ : Dict=99 , lowerCAmelCase__ : List[str]=32 , lowerCAmelCase__ : str=5 , lowerCAmelCase__ : str=4 , lowerCAmelCase__ : Any=37 , lowerCAmelCase__ : Tuple="gelu" , lowerCAmelCase__ : int=0.1 , lowerCAmelCase__ : Dict=0.1 , lowerCAmelCase__ : str=5_12 , lowerCAmelCase__ : List[str]=16 , lowerCAmelCase__ : List[str]=2 , lowerCAmelCase__ : Union[str, Any]=0.02 , lowerCAmelCase__ : int=3 , lowerCAmelCase__ : Tuple=4 , lowerCAmelCase__ : List[Any]=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = parent
SCREAMING_SNAKE_CASE : Optional[Any] = batch_size
SCREAMING_SNAKE_CASE : Any = seq_length
SCREAMING_SNAKE_CASE : Optional[int] = is_training
SCREAMING_SNAKE_CASE : Tuple = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : Tuple = use_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : str = type_vocab_size
SCREAMING_SNAKE_CASE : int = type_sequence_label_size
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : List[Any] = num_choices
SCREAMING_SNAKE_CASE : Any = scope
def __lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Dict = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : Union[str, Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase ( self : List[Any] ):
"""simple docstring"""
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase__ , initializer_range=self.initializer_range , )
def __lowercase ( self : List[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = BioGptModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Any = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase ( self : Union[str, Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Dict , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = BioGptForCausalLM(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase ( self : str , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : str , lowerCAmelCase__ : str , *lowerCAmelCase__ : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = BioGptModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# create attention mask
SCREAMING_SNAKE_CASE : List[str] = torch.ones(input_ids.shape , dtype=torch.long , device=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = self.seq_length // 2
SCREAMING_SNAKE_CASE : Any = 0
# first forward pass
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
SCREAMING_SNAKE_CASE : List[Any] = ids_tensor((1,) , lowerCAmelCase__ ).item() + 1
SCREAMING_SNAKE_CASE : Tuple = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
SCREAMING_SNAKE_CASE : str = random_other_next_tokens
# append to next input_ids and attn_mask
SCREAMING_SNAKE_CASE : Tuple = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE : str = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=lowerCAmelCase__ )] , dim=1 , )
# get two different outputs
SCREAMING_SNAKE_CASE : Any = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )['''last_hidden_state''']
SCREAMING_SNAKE_CASE : Optional[Any] = model(lowerCAmelCase__ , past_key_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )['''last_hidden_state''']
# select random slice
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE : Union[str, Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1e-3 ) )
def __lowercase ( self : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[Any] , *lowerCAmelCase__ : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = BioGptModel(config=lowerCAmelCase__ ).to(lowerCAmelCase__ ).eval()
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.ones(input_ids.shape , dtype=torch.long , device=lowerCAmelCase__ )
# first forward pass
SCREAMING_SNAKE_CASE : Tuple = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , use_cache=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE : Union[str, Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE : List[Any] = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )['''last_hidden_state''']
SCREAMING_SNAKE_CASE : str = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , past_key_values=lowerCAmelCase__ )[
'''last_hidden_state'''
]
# select random slice
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE : Union[str, Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1e-3 ) )
def __lowercase ( self : int , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Any , lowerCAmelCase__ : str , *lowerCAmelCase__ : Any , lowerCAmelCase__ : int=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = BioGptForCausalLM(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE : Tuple = model(lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def __lowercase ( self : Any , lowerCAmelCase__ : str , *lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = BioGptModel(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def __lowercase ( self : str , lowerCAmelCase__ : Any , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Tuple , *lowerCAmelCase__ : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.num_labels
SCREAMING_SNAKE_CASE : Tuple = BioGptForTokenClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,(
SCREAMING_SNAKE_CASE
) ,
) : Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE : Any = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
_lowerCAmelCase : Dict = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
_lowerCAmelCase : Optional[Any] = (BioGptForCausalLM,) if is_torch_available() else ()
_lowerCAmelCase : List[str] = (
{
'feature-extraction': BioGptModel,
'text-classification': BioGptForSequenceClassification,
'text-generation': BioGptForCausalLM,
'token-classification': BioGptForTokenClassification,
'zero-shot': BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowerCAmelCase : Dict = False
def __lowercase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = BioGptModelTester(self )
SCREAMING_SNAKE_CASE : Any = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def __lowercase ( self : Dict ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def __lowercase ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE : Optional[Any] = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase__ )
def __lowercase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase__ , gradient_checkpointing=lowerCAmelCase__ )
def __lowercase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase__ )
def __lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase__ )
def __lowercase ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase__ )
@slow
def __lowercase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
model.to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = '''left'''
# Define PAD Token = EOS Token = 50256
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.eos_token
SCREAMING_SNAKE_CASE : Dict = model.config.eos_token_id
# use different length sentences to test batching
SCREAMING_SNAKE_CASE : Any = [
'''Hello, my dog is a little''',
'''Today, I''',
]
SCREAMING_SNAKE_CASE : List[str] = tokenizer(lowerCAmelCase__ , return_tensors='''pt''' , padding=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Dict = inputs['''input_ids'''].to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = model.generate(
input_ids=lowerCAmelCase__ , attention_mask=inputs['''attention_mask'''].to(lowerCAmelCase__ ) , )
SCREAMING_SNAKE_CASE : int = tokenizer(sentences[0] , return_tensors='''pt''' ).input_ids.to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = model.generate(input_ids=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
SCREAMING_SNAKE_CASE : List[Any] = tokenizer(sentences[1] , return_tensors='''pt''' ).input_ids.to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = model.generate(input_ids=lowerCAmelCase__ , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.batch_decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = tokenizer.decode(output_padded[0] , skip_special_tokens=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Dict = [
'''Hello, my dog is a little bit bigger than a little bit.''',
'''Today, I have a good idea of how to use the information''',
]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , [non_padded_sentence, padded_sentence] )
@slow
def __lowercase ( self : Tuple ):
"""simple docstring"""
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : List[Any] = BioGptModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __lowercase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Union[str, Any] = 3
SCREAMING_SNAKE_CASE : Dict = input_dict['''input_ids''']
SCREAMING_SNAKE_CASE : str = input_ids.ne(1 ).to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE : Dict = BioGptForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowercase ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE : Tuple = 3
SCREAMING_SNAKE_CASE : Optional[Any] = '''multi_label_classification'''
SCREAMING_SNAKE_CASE : Any = input_dict['''input_ids''']
SCREAMING_SNAKE_CASE : Any = input_ids.ne(1 ).to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE : List[Any] = BioGptForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class lowerCamelCase_ ( unittest.TestCase ):
@slow
def __lowercase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
SCREAMING_SNAKE_CASE : str = torch.tensor([[2, 48_05, 9, 6_56, 21]] )
SCREAMING_SNAKE_CASE : Dict = model(lowerCAmelCase__ )[0]
SCREAMING_SNAKE_CASE : Tuple = 4_23_84
SCREAMING_SNAKE_CASE : List[Any] = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase__ , atol=1e-4 ) )
@slow
def __lowercase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
SCREAMING_SNAKE_CASE : Optional[Any] = BioGptForCausalLM.from_pretrained('''microsoft/biogpt''' )
model.to(lowerCAmelCase__ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = tokenizer('''COVID-19 is''' , return_tensors='''pt''' ).to(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = model.generate(
**lowerCAmelCase__ , min_length=1_00 , max_length=10_24 , num_beams=5 , early_stopping=lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = (
'''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
''' more than 800,000 deaths.'''
)
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
| 464 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : Optional[Any] = {
"""configuration_efficientnet""": [
"""EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EfficientNetConfig""",
"""EfficientNetOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : str = ["""EfficientNetImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[Any] = [
"""EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EfficientNetForImageClassification""",
"""EfficientNetModel""",
"""EfficientNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
a__ : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 589 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
UpperCAmelCase__ = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 351 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class UpperCamelCase__ :
'''simple docstring'''
@staticmethod
def A__ ( *A , **A ) ->List[Any]:
pass
@is_pipeline_test
@require_torch
@require_vision
class UpperCamelCase__ ( unittest.TestCase):
'''simple docstring'''
__a : int = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def A__ ( self , A , A , A ) ->List[Any]:
UpperCAmelCase__ :Tuple = pipeline('visual-question-answering' , model='hf-internal-testing/tiny-vilt-random-vqa' )
UpperCAmelCase__ :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 A__ ( self , A , A ) ->List[Any]:
UpperCAmelCase__ :Optional[Any] = vqa_pipeline(A , top_k=1 )
self.assertEqual(
A , [
[{'score': ANY(A ), 'answer': ANY(A )}],
[{'score': ANY(A ), 'answer': ANY(A )}],
] , )
@require_torch
def A__ ( self ) ->Tuple:
UpperCAmelCase__ :Tuple = pipeline('visual-question-answering' , model='hf-internal-testing/tiny-vilt-random-vqa' )
UpperCAmelCase__ :Optional[int] = './tests/fixtures/tests_samples/COCO/000000039769.png'
UpperCAmelCase__ :List[Any] = 'How many cats are there?'
UpperCAmelCase__ :Tuple = vqa_pipeline(image=A , question='How many cats are there?' , top_k=2 )
self.assertEqual(
A , [{'score': ANY(A ), 'answer': ANY(A )}, {'score': ANY(A ), 'answer': ANY(A )}] )
UpperCAmelCase__ :Any = vqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
A , [{'score': ANY(A ), 'answer': ANY(A )}, {'score': ANY(A ), 'answer': ANY(A )}] )
@slow
@require_torch
def A__ ( self ) ->List[str]:
UpperCAmelCase__ :Optional[int] = pipeline('visual-question-answering' , model='dandelin/vilt-b32-finetuned-vqa' )
UpperCAmelCase__ :Optional[Any] = './tests/fixtures/tests_samples/COCO/000000039769.png'
UpperCAmelCase__ :List[str] = 'How many cats are there?'
UpperCAmelCase__ :str = vqa_pipeline(image=A , question=A , top_k=2 )
self.assertEqual(
nested_simplify(A , decimals=4 ) , [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}] )
UpperCAmelCase__ :List[str] = vqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(A , decimals=4 ) , [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}] )
UpperCAmelCase__ :Any = vqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(A , 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 A__ ( self ) ->Optional[int]:
pass
| 433 |
import argparse
import struct
import unittest
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , A ) ->None:
UpperCAmelCase__ :Dict = data
# Initialize hash values
UpperCAmelCase__ :str = [
0x6a_09e_667,
0xbb_67a_e85,
0x3c_6ef_372,
0xa5_4ff_53a,
0x51_0e5_27f,
0x9b_056_88c,
0x1f_83d_9ab,
0x5b_e0c_d19,
]
# Initialize round constants
UpperCAmelCase__ :str = [
0x42_8a2_f98,
0x71_374_491,
0xb5_c0f_bcf,
0xe9_b5d_ba5,
0x39_56c_25b,
0x59_f11_1f1,
0x92_3f8_2a4,
0xab_1c5_ed5,
0xd8_07a_a98,
0x12_835_b01,
0x24_318_5be,
0x55_0c7_dc3,
0x72_be5_d74,
0x80_deb_1fe,
0x9b_dc0_6a7,
0xc1_9bf_174,
0xe4_9b6_9c1,
0xef_be4_786,
0x0f_c19_dc6,
0x24_0ca_1cc,
0x2d_e92_c6f,
0x4a_748_4aa,
0x5c_b0a_9dc,
0x76_f98_8da,
0x98_3e5_152,
0xa8_31c_66d,
0xb0_032_7c8,
0xbf_597_fc7,
0xc6_e00_bf3,
0xd5_a79_147,
0x06_ca6_351,
0x14_292_967,
0x27_b70_a85,
0x2e_1b2_138,
0x4d_2c6_dfc,
0x53_380_d13,
0x65_0a7_354,
0x76_6a0_abb,
0x81_c2c_92e,
0x92_722_c85,
0xa2_bfe_8a1,
0xa8_1a6_64b,
0xc2_4b8_b70,
0xc7_6c5_1a3,
0xd1_92e_819,
0xd6_990_624,
0xf4_0e3_585,
0x10_6aa_070,
0x19_a4c_116,
0x1e_376_c08,
0x27_487_74c,
0x34_b0b_cb5,
0x39_1c0_cb3,
0x4e_d8a_a4a,
0x5b_9cc_a4f,
0x68_2e6_ff3,
0x74_8f8_2ee,
0x78_a56_36f,
0x84_c87_814,
0x8c_c70_208,
0x90_bef_ffa,
0xa4_506_ceb,
0xbe_f9a_3f7,
0xc6_717_8f2,
]
UpperCAmelCase__ :Any = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def A__ ( A ) ->bytes:
UpperCAmelCase__ :List[Any] = b'\x80' + (b'\x00' * (63 - (len(A ) + 8) % 64))
UpperCAmelCase__ :Optional[int] = struct.pack('>Q' , (len(A ) * 8) )
return data + padding + big_endian_integer
def A__ ( self ) ->None:
# Convert into blocks of 64 bytes
UpperCAmelCase__ :List[Any] = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCAmelCase__ :Any = list(struct.unpack('>16L' , A ) )
# add 48 0-ed integers
words += [0] * 48
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :List[Any] = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCAmelCase__ :Optional[Any] = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCAmelCase__ :Any = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCAmelCase__ :str = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x100_000_000
# Compression
UpperCAmelCase__ :Tuple = self.ror(A , 6 ) ^ self.ror(A , 11 ) ^ self.ror(A , 25 )
UpperCAmelCase__ :int = (e & f) ^ ((~e & 0xff_fff_fff) & g)
UpperCAmelCase__ :str = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x100_000_000
UpperCAmelCase__ :Optional[int] = self.ror(A , 2 ) ^ self.ror(A , 13 ) ^ self.ror(A , 22 )
UpperCAmelCase__ :Optional[int] = (a & b) ^ (a & c) ^ (b & c)
UpperCAmelCase__ :Optional[int] = (sa + maj) % 0x100_000_000
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :Tuple = (
g,
f,
e,
((d + tempa) % 0x100_000_000),
c,
b,
a,
((tempa + tempa) % 0x100_000_000),
)
UpperCAmelCase__ :str = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCAmelCase__ :Tuple = [
((element + mutated_hash_values[index]) % 0x100_000_000)
for index, element in enumerate(self.hashes )
]
UpperCAmelCase__ :Dict = ''.join([hex(A )[2:].zfill(8 ) for value in self.hashes] )
def A__ ( self , A , A ) ->int:
return 0xff_fff_fff & (value << (32 - rotations)) | (value >> rotations)
class UpperCamelCase__ ( unittest.TestCase):
'''simple docstring'''
def A__ ( self ) ->None:
import hashlib
UpperCAmelCase__ :Optional[Any] = bytes('Test String' , 'utf-8' )
self.assertEqual(SHAaaa(A ).hash , hashlib.shaaaa(A ).hexdigest() )
def A ( ):
"""simple docstring"""
import doctest
doctest.testmod()
UpperCAmelCase__ :Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'-s' , '--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument(
'-f' , '--file' , dest='input_file' , help='Hash contents of a file' )
UpperCAmelCase__ :List[Any] = parser.parse_args()
UpperCAmelCase__ :List[Any] = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
UpperCAmelCase__ :Optional[Any] = f.read()
else:
UpperCAmelCase__ :Any = bytes(SCREAMING_SNAKE_CASE , 'utf-8' )
print(SHAaaa(SCREAMING_SNAKE_CASE ).hash )
if __name__ == "__main__":
main()
| 433 | 1 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
lowerCAmelCase__ = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
lowerCAmelCase__ = F'{src_lang}-{tgt_lang}'
lowerCAmelCase__ = F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
lowerCAmelCase__ = os.path.join(lowerCAmelCase_ , "README.md" )
print(F'Generating {path}' )
with open(lowerCAmelCase_ , "w" , encoding="utf-8" ) as f:
f.write(lowerCAmelCase_ )
# make sure we are under the root of the project
UpperCamelCase = Path(__file__).resolve().parent.parent.parent
UpperCamelCase = repo_dir / 'model_cards'
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
UpperCamelCase , UpperCamelCase , UpperCamelCase = model_name.split('-')
UpperCamelCase = model_cards_dir / 'facebook' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 61 | import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class _lowercase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: Union[str, Any] = BertJapaneseTokenizer
SCREAMING_SNAKE_CASE: Dict = False
SCREAMING_SNAKE_CASE: List[Any] = True
def _a ( self ):
super().setUp()
lowerCAmelCase_: Any = [
"[UNK]",
"[CLS]",
"[SEP]",
"こんにちは",
"こん",
"にちは",
"ばんは",
"##こん",
"##にちは",
"##ばんは",
"世界",
"##世界",
"、",
"##、",
"。",
"##。",
]
lowerCAmelCase_: Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def _a ( self , lowerCamelCase__ ):
lowerCAmelCase_: Optional[int] = "こんにちは、世界。 \nこんばんは、世界。"
lowerCAmelCase_: Optional[int] = "こんにちは 、 世界 。 こんばんは 、 世界 。"
return input_text, output_text
def _a ( self , lowerCamelCase__ ):
lowerCAmelCase_ , lowerCAmelCase_: str = self.get_input_output_texts(lowerCamelCase__ )
lowerCAmelCase_: Dict = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
lowerCAmelCase_: List[str] = tokenizer.decode(lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ )
return text, ids
def _a ( self ):
pass # TODO add if relevant
def _a ( self ):
pass # TODO add if relevant
def _a ( self ):
pass # TODO add if relevant
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = self.tokenizer_class(self.vocab_file )
lowerCAmelCase_: Any = tokenizer.tokenize("こんにちは、世界。\nこんばんは、世界。" )
self.assertListEqual(lowerCamelCase__ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def _a ( self ):
lowerCAmelCase_: List[Any] = self.tokenizer_class(self.vocab_file , word_tokenizer_type="mecab" )
self.assertIsNotNone(lowerCamelCase__ )
lowerCAmelCase_: str = "こんにちは、世界。\nこんばんは、世界。"
lowerCAmelCase_: List[Any] = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase_: Dict = os.path.join(self.tmpdirname , "tokenizer.bin" )
with open(lowerCamelCase__ , "wb" ) as handle:
pickle.dump(lowerCamelCase__ , lowerCamelCase__ )
with open(lowerCamelCase__ , "rb" ) as handle:
lowerCAmelCase_: Union[str, Any] = pickle.load(lowerCamelCase__ )
lowerCAmelCase_: Union[str, Any] = tokenizer_new.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
def _a ( self ):
lowerCAmelCase_: List[str] = MecabTokenizer(mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
def _a ( self ):
try:
lowerCAmelCase_: List[Any] = MecabTokenizer(mecab_dic="unidic_lite" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
def _a ( self ):
try:
lowerCAmelCase_: str = MecabTokenizer(mecab_dic="unidic" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
def _a ( self ):
lowerCAmelCase_: List[Any] = MecabTokenizer(do_lower_case=lowerCamelCase__ , mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップルストア", "で", "iphone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
def _a ( self ):
try:
lowerCAmelCase_: Any = MecabTokenizer(
do_lower_case=lowerCamelCase__ , normalize_text=lowerCamelCase__ , mecab_option="-d /usr/local/lib/mecab/dic/jumandic" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れた", "\u3000", "。"] , )
def _a ( self ):
lowerCAmelCase_: str = MecabTokenizer(normalize_text=lowerCamelCase__ , mecab_dic="ipadic" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れ", "た", " ", "。"] , )
@require_sudachi
def _a ( self ):
lowerCAmelCase_: Any = self.tokenizer_class(self.vocab_file , word_tokenizer_type="sudachi" )
self.assertIsNotNone(lowerCamelCase__ )
lowerCAmelCase_: str = "こんにちは、世界。\nこんばんは、世界。"
lowerCAmelCase_: List[str] = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase_: Optional[int] = os.path.join(self.tmpdirname , "tokenizer.bin" )
with open(lowerCamelCase__ , "wb" ) as handle:
pickle.dump(lowerCamelCase__ , lowerCamelCase__ )
with open(lowerCamelCase__ , "rb" ) as handle:
lowerCAmelCase_: List[str] = pickle.load(lowerCamelCase__ )
lowerCAmelCase_: Optional[int] = tokenizer_new.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@require_sudachi
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = SudachiTokenizer(sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , [" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "] , )
@require_sudachi
def _a ( self ):
lowerCAmelCase_: Optional[int] = SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="A" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国", "人", "参政", "権"] )
@require_sudachi
def _a ( self ):
lowerCAmelCase_: Any = SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="B" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国人", "参政権"] )
@require_sudachi
def _a ( self ):
lowerCAmelCase_: Dict = SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="C" )
self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国人参政権"] )
@require_sudachi
def _a ( self ):
lowerCAmelCase_: List[str] = SudachiTokenizer(do_lower_case=lowerCamelCase__ , sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , [" ", "\t", "アップル", "ストア", "で", "iphone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "] , )
@require_sudachi
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = SudachiTokenizer(normalize_text=lowerCamelCase__ , sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , [" ", "\t", "ｱｯﾌﾟﾙ", "ストア", "で", "iPhone", "８", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", "\u3000", "。", " ", " "] , )
@require_sudachi
def _a ( self ):
lowerCAmelCase_: Tuple = SudachiTokenizer(trim_whitespace=lowerCamelCase__ , sudachi_dict_type="core" )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , )
@require_jumanpp
def _a ( self ):
lowerCAmelCase_: int = self.tokenizer_class(self.vocab_file , word_tokenizer_type="jumanpp" )
self.assertIsNotNone(lowerCamelCase__ )
lowerCAmelCase_: Any = "こんにちは、世界。\nこんばんは、世界。"
lowerCAmelCase_: Optional[int] = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase_: Optional[Any] = os.path.join(self.tmpdirname , "tokenizer.bin" )
with open(lowerCamelCase__ , "wb" ) as handle:
pickle.dump(lowerCamelCase__ , lowerCamelCase__ )
with open(lowerCamelCase__ , "rb" ) as handle:
lowerCAmelCase_: Any = pickle.load(lowerCamelCase__ )
lowerCAmelCase_: Union[str, Any] = tokenizer_new.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@require_jumanpp
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , )
@require_jumanpp
def _a ( self ):
lowerCAmelCase_: List[str] = JumanppTokenizer(do_lower_case=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iphone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , )
@require_jumanpp
def _a ( self ):
lowerCAmelCase_: Optional[Any] = JumanppTokenizer(normalize_text=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["ｱ", "ｯ", "ﾌ", "ﾟ", "ﾙ", "ストア", "で", "iPhone", "８", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , )
@require_jumanpp
def _a ( self ):
lowerCAmelCase_: List[str] = JumanppTokenizer(trim_whitespace=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れた", "。"] , )
@require_jumanpp
def _a ( self ):
lowerCAmelCase_: Any = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("ありがとうございますm(_ _)ｍ見つけるのが大変です。" ) , ["ありがとう", "ございます", "m(_ _)m", "見つける", "の", "が", "大変です", "。"] , )
def _a ( self ):
lowerCAmelCase_: Optional[int] = ["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"]
lowerCAmelCase_: Tuple = {}
for i, token in enumerate(lowerCamelCase__ ):
lowerCAmelCase_: List[Any] = i
lowerCAmelCase_: List[str] = WordpieceTokenizer(vocab=lowerCamelCase__ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("こんにちは" ) , ["こんにちは"] )
self.assertListEqual(tokenizer.tokenize("こんばんは" ) , ["こん", "##ばんは"] )
self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは" ) , ["こん", "##ばんは", "[UNK]", "こんにちは"] )
def _a ( self ):
lowerCAmelCase_: List[str] = BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp" )
lowerCAmelCase_: Optional[Any] = tokenizer.subword_tokenizer
lowerCAmelCase_: List[str] = subword_tokenizer.tokenize("国境 の 長い トンネル を 抜ける と 雪国 であった 。" )
self.assertListEqual(lowerCamelCase__ , ["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"] )
lowerCAmelCase_: Optional[int] = subword_tokenizer.tokenize("こんばんは こんばん にち は こんにちは" )
self.assertListEqual(lowerCamelCase__ , ["▁こん", "ばん", "は", "▁こん", "ばん", "▁に", "ち", "▁は", "▁こんにちは"] )
def _a ( self ):
lowerCAmelCase_: str = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese" )
lowerCAmelCase_: int = tokenizer.encode("ありがとう。" , add_special_tokens=lowerCamelCase__ )
lowerCAmelCase_: Optional[int] = tokenizer.encode("どういたしまして。" , add_special_tokens=lowerCamelCase__ )
lowerCAmelCase_: List[Any] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
lowerCAmelCase_: List[Any] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ , lowerCamelCase__ )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class _lowercase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE: Tuple = BertJapaneseTokenizer
SCREAMING_SNAKE_CASE: Optional[Any] = False
def _a ( self ):
super().setUp()
lowerCAmelCase_: Any = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
lowerCAmelCase_: int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def _a ( self , **lowerCamelCase__ ):
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="character" , **lowerCamelCase__ )
def _a ( self , lowerCamelCase__ ):
lowerCAmelCase_: str = "こんにちは、世界。 \nこんばんは、世界。"
lowerCAmelCase_: Tuple = "こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
return input_text, output_text
def _a ( self ):
pass # TODO add if relevant
def _a ( self ):
pass # TODO add if relevant
def _a ( self ):
pass # TODO add if relevant
def _a ( self ):
lowerCAmelCase_: str = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="character" )
lowerCAmelCase_: Any = tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。" )
self.assertListEqual(
lowerCamelCase__ , ["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def _a ( self ):
lowerCAmelCase_: Optional[Any] = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
lowerCAmelCase_: List[str] = {}
for i, token in enumerate(lowerCamelCase__ ):
lowerCAmelCase_: Optional[Any] = i
lowerCAmelCase_: List[Any] = CharacterTokenizer(vocab=lowerCamelCase__ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("こんにちは" ) , ["こ", "ん", "に", "ち", "は"] )
self.assertListEqual(tokenizer.tokenize("こんにちほ" ) , ["こ", "ん", "に", "ち", "[UNK]"] )
def _a ( self ):
lowerCAmelCase_: Optional[Any] = self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char" )
lowerCAmelCase_: Any = tokenizer.encode("ありがとう。" , add_special_tokens=lowerCamelCase__ )
lowerCAmelCase_: Dict = tokenizer.encode("どういたしまして。" , add_special_tokens=lowerCamelCase__ )
lowerCAmelCase_: Dict = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
lowerCAmelCase_: Any = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ , lowerCamelCase__ )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def _a ( self ):
lowerCAmelCase_: Union[str, Any] = "cl-tohoku/bert-base-japanese"
lowerCAmelCase_: Optional[int] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ , lowerCamelCase__ )
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def _a ( self ):
lowerCAmelCase_: Dict = "cl-tohoku/bert-base-japanese"
with self.assertLogs("transformers" , level="WARNING" ) as cm:
BertTokenizer.from_pretrained(lowerCamelCase__ )
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class this function"
" is called from." ) )
lowerCAmelCase_: List[str] = "bert-base-cased"
with self.assertLogs("transformers" , level="WARNING" ) as cm:
BertJapaneseTokenizer.from_pretrained(lowerCamelCase__ )
self.assertTrue(
cm.records[0].message.startswith(
"The tokenizer class you load from this checkpoint is not the same type as the class this function"
" is called from." ) ) | 613 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
A_ : Tuple = logging.getLogger(__name__)
@dataclass
class _a :
'''simple docstring'''
UpperCAmelCase__: str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCAmelCase__: Optional[str] = field(
default=__magic_name__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCAmelCase__: Optional[str] = field(
default=__magic_name__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCAmelCase__: Optional[str] = field(
default=__magic_name__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCAmelCase__: bool = field(
default=__magic_name__ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCAmelCase__: str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase__: bool = field(
default=__magic_name__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class _a :
'''simple docstring'''
UpperCAmelCase__: Optional[str] = field(default=__magic_name__ , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCAmelCase__: Optional[str] = field(
default=__magic_name__ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCAmelCase__: bool = field(
default=__magic_name__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCAmelCase__: Optional[int] = field(
default=__magic_name__ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCAmelCase__: Optional[int] = field(
default=__magic_name__ , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCAmelCase__: bool = field(
default=__magic_name__ , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
UpperCAmelCase__: Optional[int] = field(
default=__magic_name__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase__: Optional[int] = field(
default=__magic_name__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def __A ( self ):
if self.train_file is not None:
A__ : int = self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
A__ : Any = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class _a :
'''simple docstring'''
UpperCAmelCase__: PreTrainedTokenizerBase
UpperCAmelCase__: Union[bool, str, PaddingStrategy] = True
UpperCAmelCase__: Optional[int] = None
UpperCAmelCase__: Optional[int] = None
def __call__( self , A__ ):
A__ : Dict = """label""" if """label""" in features[0].keys() else """labels"""
A__ : int = [feature.pop(A__ ) for feature in features]
A__ : Tuple = len(A__ )
A__ : Any = len(features[0]["""input_ids"""] )
A__ : str = [
[{k: v[i] for k, v in feature.items()} for i in range(A__ )] for feature in features
]
A__ : Dict = list(chain(*A__ ) )
A__ : Dict = self.tokenizer.pad(
A__ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" , )
# Un-flatten
A__ : List[Any] = {k: v.view(A__ , A__ , -1 ) for k, v in batch.items()}
# Add back labels
A__ : Any = torch.tensor(A__ , dtype=torch.intaa )
return batch
def UpperCamelCase () -> Tuple:
# 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.
A__ : 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.
A__ : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
A__ : Any = 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_swag""" , lowercase_ , lowercase_ )
# 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()
A__ : Any = training_args.get_process_log_level()
logger.setLevel(lowercase_ )
datasets.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
A__ : int = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A__ : str = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
A__ : Union[str, Any] = {}
if data_args.train_file is not None:
A__ : List[str] = data_args.train_file
if data_args.validation_file is not None:
A__ : Optional[Any] = data_args.validation_file
A__ : List[Any] = data_args.train_file.split(""".""" )[-1]
A__ : Any = load_dataset(
lowercase_ , data_files=lowercase_ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
A__ : int = load_dataset(
"""swag""" , """regular""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A__ : Dict = 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 , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
A__ : 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 , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
A__ : List[Any] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
A__ : Optional[int] = [f"""ending{i}""" for i in range(4 )]
A__ : List[str] = """sent1"""
A__ : List[str] = """sent2"""
if data_args.max_seq_length is None:
A__ : Tuple = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
"""The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"""
""" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"""
""" override this default with `--block_size xxx`.""" )
A__ : Optional[Any] = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
A__ : List[Any] = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowercase_: Dict ):
A__ : Union[str, Any] = [[context] * 4 for context in examples[context_name]]
A__ : str = examples[question_header_name]
A__ : List[str] = [
[f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(lowercase_ )
]
# Flatten out
A__ : int = list(chain(*lowercase_ ) )
A__ : Any = list(chain(*lowercase_ ) )
# Tokenize
A__ : Optional[Any] = tokenizer(
lowercase_ , lowercase_ , truncation=lowercase_ , max_length=lowercase_ , padding="""max_length""" if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowercase_ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
A__ : Dict = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
A__ : int = min(len(lowercase_ ) , data_args.max_train_samples )
A__ : str = train_dataset.select(range(lowercase_ ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
A__ : str = train_dataset.map(
lowercase_ , batched=lowercase_ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
A__ : Dict = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
A__ : List[Any] = min(len(lowercase_ ) , data_args.max_eval_samples )
A__ : int = eval_dataset.select(range(lowercase_ ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
A__ : Dict = eval_dataset.map(
lowercase_ , batched=lowercase_ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
A__ : int = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowercase_ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowercase_: str ):
A__ : Union[str, Any] = eval_predictions
A__ : Dict = np.argmax(lowercase_ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
A__ : Union[str, Any] = Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowercase_ , data_collator=lowercase_ , compute_metrics=lowercase_ , )
# Training
if training_args.do_train:
A__ : str = None
if training_args.resume_from_checkpoint is not None:
A__ : Optional[int] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
A__ : List[Any] = last_checkpoint
A__ : Optional[int] = trainer.train(resume_from_checkpoint=lowercase_ )
trainer.save_model() # Saves the tokenizer too for easy upload
A__ : Tuple = train_result.metrics
A__ : str = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_ )
)
A__ : int = min(lowercase_ , len(lowercase_ ) )
trainer.log_metrics("""train""" , lowercase_ )
trainer.save_metrics("""train""" , lowercase_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
A__ : Any = trainer.evaluate()
A__ : Dict = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase_ )
A__ : Union[str, Any] = min(lowercase_ , len(lowercase_ ) )
trainer.log_metrics("""eval""" , lowercase_ )
trainer.save_metrics("""eval""" , lowercase_ )
A__ : Union[str, Any] = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """multiple-choice""",
"""dataset_tags""": """swag""",
"""dataset_args""": """regular""",
"""dataset""": """SWAG""",
"""language""": """en""",
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase_ )
else:
trainer.create_model_card(**lowercase_ )
def UpperCamelCase (lowercase_: Optional[Any] ) -> Tuple:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 717 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
A_ : Any = logging.getLogger(__name__)
def UpperCamelCase (lowercase_: Optional[Any]=2 , lowercase_: Union[str, Any]=3 , lowercase_: int=16 , lowercase_: int = 10 , lowercase_: int = 2 ) -> int:
def get_dataset(lowercase_: Optional[int] ):
A__ : Optional[Any] = torch.randn(batch_size * n_batches , 1 )
return TensorDataset(lowercase_ , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
A__ : Dict = get_dataset(lowercase_ )
A__ : Any = get_dataset(lowercase_ )
A__ : Dict = DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4 )
A__ : Optional[Any] = DataLoader(lowercase_ , shuffle=lowercase_ , batch_size=lowercase_ , num_workers=4 )
return (train_dataloader, valid_dataloader)
def UpperCamelCase (lowercase_: Optional[Any] , lowercase_: List[str] , lowercase_: int , lowercase_: int , lowercase_: List[str] , lowercase_: Dict=None ) -> List[Any]:
A__ : List[Any] = []
for epoch in range(lowercase_ ):
# Train quickly
model.train()
for batch in dataloader:
A__ , A__ : Any = batch
A__ : Any = model(lowercase_ )
A__ : Any = torch.nn.functional.mse_loss(lowercase_ , lowercase_ )
accelerator.backward(lowercase_ )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class _a (nn.Module ):
'''simple docstring'''
def __init__( self ):
super().__init__()
A__ : str = nn.Parameter(torch.randn(1 ) )
A__ : Any = nn.Parameter(torch.randn(1 ) )
def __A ( self , A__ ):
return x * self.a + self.b
class _a (unittest.TestCase ):
'''simple docstring'''
def __A ( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
A__ : Optional[Any] = DummyModel()
A__ : Optional[Any] = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
A__ , A__ : str = dummy_dataloaders()
A__ : Dict = ProjectConfiguration(total_limit=1 , project_dir=A__ , automatic_checkpoint_naming=A__ )
# Train baseline
A__ : List[str] = Accelerator(project_config=A__ )
A__ , A__ , A__ , A__ : Any = accelerator.prepare(
A__ , A__ , A__ , A__ )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) , 1 )
def __A ( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
A__ : str = DummyModel()
A__ : Optional[int] = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
A__ , A__ : int = dummy_dataloaders()
# Train baseline
A__ : str = Accelerator()
A__ , A__ , A__ , A__ : List[str] = accelerator.prepare(
A__ , A__ , A__ , A__ )
# Save initial
A__ : List[Any] = os.path.join(A__ , """initial""" )
accelerator.save_state(A__ )
((A__) , (A__)) : str = model.a.item(), model.b.item()
A__ : Dict = optimizer.state_dict()
A__ : List[str] = train(3 , A__ , A__ , A__ , A__ )
((A__) , (A__)) : str = model.a.item(), model.b.item()
A__ : Any = optimizer.state_dict()
# Train partially
set_seed(42 )
A__ : Optional[int] = DummyModel()
A__ : Dict = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
A__ , A__ : Dict = dummy_dataloaders()
A__ : List[str] = Accelerator()
A__ , A__ , A__ , A__ : Optional[Any] = accelerator.prepare(
A__ , A__ , A__ , A__ )
accelerator.load_state(A__ )
((A__) , (A__)) : Tuple = model.a.item(), model.b.item()
A__ : Union[str, Any] = optimizer.state_dict()
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
A__ : List[str] = train(2 , A__ , A__ , A__ , A__ )
# Save everything
A__ : Optional[int] = os.path.join(A__ , """checkpoint""" )
accelerator.save_state(A__ )
# Load everything back in and make sure all states work
accelerator.load_state(A__ )
test_rands += train(1 , A__ , A__ , A__ , A__ )
((A__) , (A__)) : Union[str, Any] = model.a.item(), model.b.item()
A__ : Optional[int] = optimizer.state_dict()
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
def __A ( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
A__ : int = DummyModel()
A__ : Optional[int] = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
A__ , A__ : List[str] = dummy_dataloaders()
A__ : str = ProjectConfiguration(automatic_checkpoint_naming=A__ )
# Train baseline
A__ : Any = Accelerator(project_dir=A__ , project_config=A__ )
A__ , A__ , A__ , A__ : str = accelerator.prepare(
A__ , A__ , A__ , A__ )
# Save initial
accelerator.save_state()
((A__) , (A__)) : Tuple = model.a.item(), model.b.item()
A__ : int = optimizer.state_dict()
A__ : int = train(3 , A__ , A__ , A__ , A__ )
((A__) , (A__)) : Optional[Any] = model.a.item(), model.b.item()
A__ : Any = optimizer.state_dict()
# Train partially
set_seed(42 )
A__ : Dict = DummyModel()
A__ : List[Any] = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
A__ , A__ : Union[str, Any] = dummy_dataloaders()
A__ : List[Any] = ProjectConfiguration(iteration=1 , automatic_checkpoint_naming=A__ )
A__ : Dict = Accelerator(project_dir=A__ , project_config=A__ )
A__ , A__ , A__ , A__ : Union[str, Any] = accelerator.prepare(
A__ , A__ , A__ , A__ )
accelerator.load_state(os.path.join(A__ , """checkpoints""" , """checkpoint_0""" ) )
((A__) , (A__)) : Optional[int] = model.a.item(), model.b.item()
A__ : Tuple = optimizer.state_dict()
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
A__ : str = train(2 , A__ , A__ , A__ , A__ )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(A__ , """checkpoints""" , """checkpoint_1""" ) )
test_rands += train(1 , A__ , A__ , A__ , A__ )
((A__) , (A__)) : Optional[int] = model.a.item(), model.b.item()
A__ : List[Any] = optimizer.state_dict()
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
self.assertEqual(A__ , A__ )
def __A ( self ):
A__ : Union[str, Any] = torch.tensor([1, 2, 3] )
A__ : int = torch.tensor([2, 3, 4] )
A__ : List[Any] = DummyModel()
A__ : List[Any] = torch.optim.Adam(net.parameters() )
A__ : Tuple = Accelerator()
with self.assertRaises(A__ ) as ve:
accelerator.register_for_checkpointing(A__ , A__ , A__ , A__ )
A__ : Any = str(ve.exception )
self.assertTrue("""Item at index 0""" in message )
self.assertTrue("""Item at index 1""" in message )
self.assertFalse("""Item at index 2""" in message )
self.assertFalse("""Item at index 3""" in message )
def __A ( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
A__ : Any = DummyModel()
A__ : Union[str, Any] = torch.optim.Adam(params=model.parameters() , lr=1e-3 )
A__ : Dict = torch.optim.lr_scheduler.StepLR(A__ , step_size=1 , gamma=0.9_9 )
A__ , A__ : List[Any] = dummy_dataloaders()
A__ : Tuple = ProjectConfiguration(automatic_checkpoint_naming=A__ )
# Train baseline
A__ : Optional[Any] = Accelerator(project_dir=A__ , project_config=A__ )
A__ , A__ , A__ , A__ , A__ : Union[str, Any] = accelerator.prepare(
A__ , A__ , A__ , A__ , A__ )
# Save initial
accelerator.save_state()
A__ : Tuple = scheduler.state_dict()
train(3 , A__ , A__ , A__ , A__ , A__ )
self.assertNotEqual(A__ , scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(A__ , """checkpoints""" , """checkpoint_0""" ) )
self.assertEqual(A__ , scheduler.state_dict() )
def __A ( self ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
A__ : Optional[Any] = DummyModel()
A__ : int = ProjectConfiguration(automatic_checkpoint_naming=A__ , total_limit=2 )
# Train baseline
A__ : List[str] = Accelerator(project_dir=A__ , project_config=A__ )
A__ : Union[str, Any] = accelerator.prepare(A__ )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(A__ , """checkpoints""" , """checkpoint_0""" ) ) )
self.assertTrue(os.path.exists(os.path.join(A__ , """checkpoints""" , """checkpoint_9""" ) ) )
self.assertTrue(os.path.exists(os.path.join(A__ , """checkpoints""" , """checkpoint_10""" ) ) )
@require_cuda
def __A ( self ):
A__ : Dict = ["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
execute_subprocess_async(A__ , env=os.environ.copy() )
if __name__ == "__main__":
A_ : List[str] = '/tmp/accelerate/state_checkpointing'
A_ : Optional[Any] = DummyModel()
A_ : Union[str, Any] = torch.optim.Adam(params=model.parameters(), lr=1E-3)
A_ : str = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
A_ , A_ : List[Any] = dummy_dataloaders()
A_ : int = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
A_ : List[str] = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
A_ , A_ , A_ , A_ , A_ : List[Any] = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
A_ , A_ : Dict = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
A_ : str = group['params'][0].device
break
assert param_device.type == accelerator.device.type
A_ : Optional[Any] = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
for group in optimizer.param_groups:
A_ : str = group['params'][0].device
break
assert (
param_device.type == torch.device('cpu').type
), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
for group in optimizer.param_groups:
A_ : Tuple = group['params'][0].device
break
assert (
param_device.type == accelerator.device.type
), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone()
| 64 | 0 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def lowercase_ ( __snake_case : Dict , __snake_case : Optional[int] ) -> str:
'''simple docstring'''
snake_case__ :Any = Mock()
snake_case__ :Optional[int] = conn, Mock()
snake_case__ :Dict = iter([1, None] )
snake_case__ :str = lambda __snake_case : next(__snake_case )
# ===== invoke =====
send_file(filename="mytext.txt" , testing=__snake_case )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once() | 241 |
from __future__ import annotations
def lowercase_ ( __snake_case : list[int] ) -> int:
'''simple docstring'''
if not nums:
return 0
snake_case__ :Union[str, Any] = nums[0]
snake_case__ :List[Any] = 0
for num in nums[1:]:
snake_case__ , snake_case__ :Optional[Any] = (
max_excluding + num,
max(__snake_case , __snake_case ),
)
return max(__snake_case , __snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod() | 241 | 1 |
"""simple docstring"""
class a :
def __init__( self : int , __lowerCAmelCase : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] ):
_UpperCAmelCase = name
_UpperCAmelCase = value
_UpperCAmelCase = weight
def __repr__( self : List[str] ):
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def lowerCAmelCase_ ( self : Tuple ):
return self.value
def lowerCAmelCase_ ( self : Optional[Any] ):
return self.name
def lowerCAmelCase_ ( self : Optional[Any] ):
return self.weight
def lowerCAmelCase_ ( self : List[str] ):
return self.value / self.weight
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = []
for i in range(len(lowercase ) ):
menu.append(Things(name[i] ,value[i] ,weight[i] ) )
return menu
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = sorted(lowercase ,key=lowercase ,reverse=lowercase )
_UpperCAmelCase = []
_UpperCAmelCase , _UpperCAmelCase = 0.0, 0.0
for i in range(len(lowercase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def __UpperCAmelCase ( ):
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 275 | """simple docstring"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = abs(lowercase )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = abs(lowercase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
return sum(int(lowercase ) for c in str(abs(lowercase ) ) )
def __UpperCAmelCase ( ):
"""simple docstring"""
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(lowercase ,lowercase ) -> None:
_UpperCAmelCase = f'''{func.__name__}({value})'''
_UpperCAmelCase = timeit(f'''__main__.{call}''' ,setup="""import __main__""" )
print(f'''{call:56} = {func(lowercase )} -- {timing:.4f} seconds''' )
for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(lowercase ,lowercase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 275 | 1 |
from __future__ import annotations
from random import random
class UpperCAmelCase__ :
def __init__( self ,A__ = None ):
_A : Optional[Any] = value
_A : Any = random()
_A : str = None
_A : Union[str, Any] = None
def __repr__( self ):
from pprint import pformat
if self.left is None and self.right is None:
return f"""\'{self.value}: {self.prior:.5}\'"""
else:
return pformat(
{f"""{self.value}: {self.prior:.5}""": (self.left, self.right)} ,indent=1 )
def __str__( self ):
_A : str = str(self.value ) + ''' '''
_A : Union[str, Any] = str(self.left or '''''' )
_A : Tuple = str(self.right or '''''' )
return value + left + right
def a__ (__lowercase :List[Any] , __lowercase :Any ) -> tuple[Node | None, Node | None]:
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
_A , _A : List[Any] = split(root.left , __lowerCamelCase )
return left, root
else:
_A , _A : Optional[Any] = split(root.right , __lowerCamelCase )
return root, right
def a__ (__lowercase :int , __lowercase :int ) -> Node | None:
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
_A : Tuple = merge(left.right , __lowerCamelCase )
return left
else:
_A : Tuple = merge(__lowerCamelCase , right.left )
return right
def a__ (__lowercase :Dict , __lowercase :Optional[Any] ) -> Node | None:
_A : Tuple = Node(__lowerCamelCase )
_A , _A : Optional[int] = split(__lowerCamelCase , __lowerCamelCase )
return merge(merge(__lowerCamelCase , __lowerCamelCase ) , __lowerCamelCase )
def a__ (__lowercase :List[Any] , __lowercase :Dict ) -> Node | None:
_A , _A : List[Any] = split(__lowerCamelCase , value - 1 )
_A , _A : int = split(__lowerCamelCase , __lowerCamelCase )
return merge(__lowerCamelCase , __lowerCamelCase )
def a__ (__lowercase :List[str] ) -> None:
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=''',''' )
inorder(root.right )
def a__ (__lowercase :List[str] , __lowercase :List[Any] ) -> Node | None:
for arg in args.split():
if arg[0] == "+":
_A : Dict = insert(__lowerCamelCase , int(arg[1:] ) )
elif arg[0] == "-":
_A : Optional[int] = erase(__lowerCamelCase , int(arg[1:] ) )
else:
print('''Unknown command''' )
return root
def a__ () -> None:
_A : Optional[int] = None
print(
'''enter numbers to create a tree, + value to add value into treap, '''
'''- value to erase all nodes with value. \'q\' to quit. ''' )
_A : Optional[Any] = input()
while args != "q":
_A : Any = interact_treap(__lowerCamelCase , __lowerCamelCase )
print(__lowerCamelCase )
_A : Dict = input()
print('''good by!''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 206 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class __lowerCAmelCase :
UpperCamelCase__ = PegasusConfig
UpperCamelCase__ = {}
UpperCamelCase__ = '''gelu'''
def __init__( self :int , __magic_name__ :Optional[int] , __magic_name__ :str=13 , __magic_name__ :List[Any]=7 , __magic_name__ :Optional[int]=True , __magic_name__ :Optional[int]=False , __magic_name__ :List[Any]=99 , __magic_name__ :int=32 , __magic_name__ :Tuple=2 , __magic_name__ :List[str]=4 , __magic_name__ :Dict=37 , __magic_name__ :Tuple=0.1 , __magic_name__ :Optional[Any]=0.1 , __magic_name__ :Dict=40 , __magic_name__ :Tuple=2 , __magic_name__ :Optional[Any]=1 , __magic_name__ :Dict=0 , ):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = eos_token_id
a = pad_token_id
a = bos_token_id
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
a = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
a = tf.concat([input_ids, eos_tensor] , axis=1 )
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
a = prepare_pegasus_inputs_dict(__magic_name__ , __magic_name__ , __magic_name__ )
return config, inputs_dict
def lowerCamelCase__ ( self :List[Any] , __magic_name__ :Any , __magic_name__ :str ):
'''simple docstring'''
a = TFPegasusModel(config=__magic_name__ ).get_decoder()
a = inputs_dict["""input_ids"""]
a = input_ids[:1, :]
a = inputs_dict["""attention_mask"""][:1, :]
a = inputs_dict["""head_mask"""]
a = 1
# first forward pass
a = model(__magic_name__ , attention_mask=__magic_name__ , head_mask=__magic_name__ , use_cache=__magic_name__ )
a , a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
a = ids_tensor((self.batch_size, 3) , config.vocab_size )
a = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
a = tf.concat([input_ids, next_tokens] , axis=-1 )
a = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
a = model(__magic_name__ , attention_mask=__magic_name__ )[0]
a = model(__magic_name__ , attention_mask=__magic_name__ , past_key_values=__magic_name__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
a = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
a = output_from_no_past[:, -3:, random_slice_idx]
a = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__magic_name__ , __magic_name__ , rtol=1E-3 )
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , ) -> Tuple:
if attention_mask is None:
a = tf.cast(tf.math.not_equal(__lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
a = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
a = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
a = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
a = 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 __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
UpperCamelCase__ = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ = (
{
'''conversational''': TFPegasusForConditionalGeneration,
'''feature-extraction''': TFPegasusModel,
'''summarization''': TFPegasusForConditionalGeneration,
'''text2text-generation''': TFPegasusForConditionalGeneration,
'''translation''': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
a = TFPegasusModelTester(self )
a = ConfigTester(self , config_class=__magic_name__ )
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__ )
@require_sentencepiece
@require_tokenizers
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase__ = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
UpperCamelCase__ = [
'''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
''' reduce the risk of wildfires.''',
'''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
UpperCamelCase__ = '''google/pegasus-xsum'''
@cached_property
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def lowerCamelCase__ ( self :int , **__magic_name__ :int ):
'''simple docstring'''
a = self.translate_src_text(**__magic_name__ )
assert self.expected_text == generated_words
def lowerCamelCase__ ( self :Union[str, Any] , **__magic_name__ :int ):
'''simple docstring'''
a = self.tokenizer(self.src_text , **__magic_name__ , padding=__magic_name__ , return_tensors="""tf""" )
a = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=__magic_name__ , )
a = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=__magic_name__ )
return generated_words
@slow
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
self._assert_generated_batch_equal_expected()
| 468 | 0 |
"""simple docstring"""
UpperCamelCase = 256
# Modulus to hash a string
UpperCamelCase = 1_000_003
def lowerCAmelCase ( UpperCamelCase_: str , UpperCamelCase_: str ) -> bool:
'''simple docstring'''
_a = len(UpperCamelCase_ )
_a = len(UpperCamelCase_ )
if p_len > t_len:
return False
_a = 0
_a = 0
_a = 1
# Calculating the hash of pattern and substring of text
for i in range(UpperCamelCase_ ):
_a = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_a = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_a = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_a = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def lowerCAmelCase ( ) -> None:
'''simple docstring'''
_a = "abc1abc12"
_a = "alskfjaldsabc1abc1abc12k23adsfabcabc"
_a = "alskfjaldsk23adsfabcabc"
assert rabin_karp(UpperCamelCase_ , UpperCamelCase_ ) and not rabin_karp(UpperCamelCase_ , UpperCamelCase_ )
# Test 2)
_a = "ABABX"
_a = "ABABZABABYABABX"
assert rabin_karp(UpperCamelCase_ , UpperCamelCase_ )
# Test 3)
_a = "AAAB"
_a = "ABAAAAAB"
assert rabin_karp(UpperCamelCase_ , UpperCamelCase_ )
# Test 4)
_a = "abcdabcy"
_a = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(UpperCamelCase_ , UpperCamelCase_ )
# Test 5)
_a = "Lü"
_a = "Lüsai"
assert rabin_karp(UpperCamelCase_ , UpperCamelCase_ )
_a = "Lue"
assert not rabin_karp(UpperCamelCase_ , UpperCamelCase_ )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 706 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
UpperCamelCase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def lowerCAmelCase ( UpperCamelCase_: List[str] ) -> List[Any]:
'''simple docstring'''
_a = {}
with open(UpperCamelCase_ , "r" ) as file:
for line_number, line in enumerate(UpperCamelCase_ ):
_a = line.strip()
if line:
_a = line.split()
_a = line_number
_a = words[0]
_a = value
return result
def lowerCAmelCase ( UpperCamelCase_: List[str] , UpperCamelCase_: Dict , UpperCamelCase_: List[str] , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
for attribute in key.split("." ):
_a = getattr(UpperCamelCase_ , UpperCamelCase_ )
_a = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(UpperCamelCase_ ):
_a = PARAM_MAPPING[full_name.split("." )[-1]]
_a = "param"
if weight_type is not None and weight_type != "param":
_a = getattr(UpperCamelCase_ , UpperCamelCase_ ).shape
elif weight_type is not None and weight_type == "param":
_a = hf_pointer
for attribute in hf_param_name.split("." ):
_a = getattr(UpperCamelCase_ , UpperCamelCase_ )
_a = shape_pointer.shape
# let's reduce dimension
_a = value[0]
else:
_a = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}''' )
if weight_type == "weight":
_a = value
elif weight_type == "weight_g":
_a = value
elif weight_type == "weight_v":
_a = value
elif weight_type == "bias":
_a = value
elif weight_type == "param":
for attribute in hf_param_name.split("." ):
_a = getattr(UpperCamelCase_ , UpperCamelCase_ )
_a = value
else:
_a = value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def lowerCAmelCase ( UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Dict , UpperCamelCase_: Tuple , UpperCamelCase_: Any , UpperCamelCase_: Union[str, Any] ) -> str:
'''simple docstring'''
_a = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(UpperCamelCase_ ):
_a = PARAM_MAPPING[full_name.split("." )[-1]]
_a = "param"
if weight_type is not None and weight_type != "param":
_a = ".".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_a = ".".join([key, hf_param_name] )
else:
_a = key
_a = value if "lm_head" in full_key else value[0]
UpperCamelCase = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def lowerCAmelCase ( UpperCamelCase_: str , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: int=None , UpperCamelCase_: Optional[int]=None ) -> Tuple:
'''simple docstring'''
_a = False
for key, mapped_key in MAPPING.items():
_a = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
_a = True
if "*" in mapped_key:
_a = name.split(UpperCamelCase_ )[0].split("." )[-2]
_a = mapped_key.replace("*" , UpperCamelCase_ )
if "weight_g" in name:
_a = "weight_g"
elif "weight_v" in name:
_a = "weight_v"
elif "bias" in name:
_a = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_a = "weight"
else:
_a = None
if hf_dict is not None:
rename_dict(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
else:
set_recursively(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
return is_used
return is_used
def lowerCAmelCase ( UpperCamelCase_: Dict , UpperCamelCase_: Dict , UpperCamelCase_: Dict ) -> Dict:
'''simple docstring'''
_a = []
_a = fairseq_model.state_dict()
_a = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_a = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , hf_model.config.feat_extract_norm == "group" , )
_a = True
else:
_a = load_wavaveca_layer(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
if not is_used:
unused_weights.append(UpperCamelCase_ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def lowerCAmelCase ( UpperCamelCase_: Union[str, Any] , UpperCamelCase_: List[Any] , UpperCamelCase_: int , UpperCamelCase_: Optional[int] , UpperCamelCase_: Optional[int] ) -> Dict:
'''simple docstring'''
_a = full_name.split("conv_layers." )[-1]
_a = name.split("." )
_a = int(items[0] )
_a = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
_a = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
_a = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
_a = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
_a = 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 lowerCAmelCase ( UpperCamelCase_: Tuple , UpperCamelCase_: List[str] , UpperCamelCase_: int=None , UpperCamelCase_: int=None , UpperCamelCase_: Optional[Any]=True , UpperCamelCase_: Any=False ) -> Optional[Any]:
'''simple docstring'''
if config_path is not None:
_a = WavaVecaConfig.from_pretrained(UpperCamelCase_ )
else:
_a = WavaVecaConfig()
if is_seq_class:
_a = read_txt_into_dict(UpperCamelCase_ )
_a = idalabel
_a = WavaVecaForSequenceClassification(UpperCamelCase_ )
_a = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , )
feature_extractor.save_pretrained(UpperCamelCase_ )
elif is_finetuned:
if dict_path:
_a = Dictionary.load(UpperCamelCase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_a = target_dict.pad_index
_a = target_dict.bos_index
_a = target_dict.eos_index
_a = len(target_dict.symbols )
_a = os.path.join(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_ )
_a = target_dict.indices
# fairseq has the <pad> and <s> switched
_a = 0
_a = 1
with open(UpperCamelCase_ , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(UpperCamelCase_ , UpperCamelCase_ )
_a = WavaVecaCTCTokenizer(
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_ , )
_a = True if config.feat_extract_norm == "layer" else False
_a = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCamelCase_ , return_attention_mask=UpperCamelCase_ , )
_a = WavaVecaProcessor(feature_extractor=UpperCamelCase_ , tokenizer=UpperCamelCase_ )
processor.save_pretrained(UpperCamelCase_ )
_a = WavaVecaForCTC(UpperCamelCase_ )
else:
_a = WavaVecaForPreTraining(UpperCamelCase_ )
if is_finetuned or is_seq_class:
_a , _a , _a = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
_a = argparse.Namespace(task="audio_pretraining" )
_a = fairseq.tasks.setup_task(UpperCamelCase_ )
_a , _a , _a = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=UpperCamelCase_ )
_a = model[0].eval()
recursively_load_weights(UpperCamelCase_ , UpperCamelCase_ , not is_finetuned )
hf_wavavec.save_pretrained(UpperCamelCase_ )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
UpperCamelCase = parser.parse_args()
UpperCamelCase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 612 | 0 |
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