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"""simple docstring"""
import numpy as np
import datasets
__lowerCAmelCase : Tuple = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''
__lowerCAmelCase : List[Any] = '''\
@article{de2000mahalanobis,
title={The mahalanobis distance},
author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
journal={Chemometrics and intelligent laboratory systems},
volume={50},
number={1},
pages={1--18},
year={2000},
publisher={Elsevier}
}
'''
__lowerCAmelCase : Optional[Any] = '''
Args:
X: List of datapoints to be compared with the `reference_distribution`.
reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:
>>> mahalanobis_metric = datasets.load_metric("mahalanobis")
>>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
>>> print(results)
{\'mahalanobis\': array([0.5])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""X""": datasets.Sequence(datasets.Value("""float""" , id="""sequence""" ) , id="""X""" ),
} ) , )
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : Dict , snake_case__ : Dict ):
lowerCAmelCase__ = np.array(__lowerCAmelCase )
lowerCAmelCase__ = np.array(__lowerCAmelCase )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError("""Expected `X` to be a 2D vector""" )
if len(reference_distribution.shape ) != 2:
raise ValueError("""Expected `reference_distribution` to be a 2D vector""" )
if reference_distribution.shape[0] < 2:
raise ValueError(
"""Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension""" )
# Get mahalanobis distance for each prediction
lowerCAmelCase__ = X - np.mean(__lowerCAmelCase )
lowerCAmelCase__ = np.cov(reference_distribution.T )
try:
lowerCAmelCase__ = np.linalg.inv(__lowerCAmelCase )
except np.linalg.LinAlgError:
lowerCAmelCase__ = np.linalg.pinv(__lowerCAmelCase )
lowerCAmelCase__ = np.dot(__lowerCAmelCase , __lowerCAmelCase )
lowerCAmelCase__ = np.dot(__lowerCAmelCase , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 710 | """simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = LayoutLMTokenizer
UpperCamelCase_ : List[Any] = LayoutLMTokenizerFast
UpperCamelCase_ : Dict = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
lowerCAmelCase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ = 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 _SCREAMING_SNAKE_CASE ( self : int , **snake_case__ : Union[str, Any] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Tuple ):
lowerCAmelCase__ = """UNwant\u00E9d,running"""
lowerCAmelCase__ = """unwanted, running"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase__ = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(snake_case__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [7, 4, 5, 10, 8, 9] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
pass
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif stress < 0:
raise ValueError("""Stress cannot be negative""" )
elif tangential_force < 0:
raise ValueError("""Tangential Force cannot be negative""" )
elif area < 0:
raise ValueError("""Area cannot be negative""" )
elif stress == 0:
return (
"stress",
tangential_force / area,
)
elif tangential_force == 0:
return (
"tangential_force",
stress * area,
)
else:
return (
"area",
tangential_force / stress,
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 711 | """simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase : Any = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
def __init__( self : List[str] , snake_case__ : int = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
lowerCAmelCase__ = primes[group]["""prime"""]
lowerCAmelCase__ = primes[group]["""generator"""]
lowerCAmelCase__ = int(hexlify(urandom(32 ) ) , base=16 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
return hex(self.__private_key )[2:]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = pow(self.generator , self.__private_key , self.prime )
return hex(snake_case__ )[2:]
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(snake_case__ , (self.prime - 1) // 2 , self.prime ) == 1
)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : str ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
if not self.is_valid_public_key(snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , self.__private_key , self.prime )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : int , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(snake_case__ , (prime - 1) // 2 , snake_case__ ) == 1
)
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : str , snake_case__ : str , snake_case__ : int = 14 ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(snake_case__ , snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , snake_case__ , snake_case__ )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[str] = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Tuple = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__lowerCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 712 | """simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
lowerCAmelCase__ = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = weights[0][0][0]
lowerCAmelCase__ = np.asarray(layer_norm_a[0] )
lowerCAmelCase__ = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# lsh weights + output
lowerCAmelCase__ = weights[0][1]
if len(lowerCamelCase__ ) < 4:
set_layer_weights_in_torch_lsh(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
else:
set_layer_weights_in_torch_local(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
# intermediate weighs
lowerCAmelCase__ = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowerCamelCase__ ) == 4:
lowerCAmelCase__ = intermediate_weights[2]
# layernorm 2
lowerCAmelCase__ = np.asarray(intermediate_weights[0][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# intermediate dense
lowerCAmelCase__ = np.asarray(intermediate_weights[1][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
# intermediate out
lowerCAmelCase__ = np.asarray(intermediate_weights[4][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = torch_model.reformer
# word embeds
lowerCAmelCase__ = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCamelCase__ ) , )
if isinstance(weights[3] , lowerCamelCase__ ):
lowerCAmelCase__ = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowerCAmelCase__ = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f"""{position_embeddings[emb_idx]} emb does not match"""
lowerCAmelCase__ = nn.Parameter(torch.tensor(lowerCamelCase__ ) )
lowerCAmelCase__ = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowerCamelCase__ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowerCAmelCase__ = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# output layer norm
lowerCAmelCase__ = np.asarray(weights[7][0] )
lowerCAmelCase__ = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# output embeddings
lowerCAmelCase__ = np.asarray(weights[9][0] )
lowerCAmelCase__ = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = ReformerConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = ReformerModelWithLMHead(lowerCamelCase__ )
with open(lowerCamelCase__ , """rb""" ) as f:
lowerCAmelCase__ = pickle.load(lowerCamelCase__ )["""weights"""]
set_model_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , config.hidden_size )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
__lowerCAmelCase : Tuple = 8.31_44_62 # Unit - J mol-1 K-1
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 713 | """simple docstring"""
import os
from math import logaa
def _UpperCAmelCase ( lowerCamelCase__ = "base_exp.txt" ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) ):
lowerCAmelCase__ , lowerCAmelCase__ = list(map(lowerCamelCase__ , line.split(""",""" ) ) )
if x * logaa(lowerCamelCase__ ) > largest:
lowerCAmelCase__ = x * logaa(lowerCamelCase__ )
lowerCAmelCase__ = i + 1
return result
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 2 , lowerCamelCase__ = 1 , lowerCamelCase__ = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
return (pow(lowerCamelCase__ , 2 ) + step) % modulus
for _ in range(lowerCamelCase__ ):
# These track the position within the cycle detection logic.
lowerCAmelCase__ = seed
lowerCAmelCase__ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowerCAmelCase__ = gcd(hare - tortoise , lowerCamelCase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowerCAmelCase__ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
__lowerCAmelCase : Optional[Any] = parser.parse_args()
__lowerCAmelCase : List[str] = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"{args.num} is probably prime")
else:
__lowerCAmelCase : Optional[int] = args.num // divisor
print(F"{args.num} = {divisor} * {quotient}")
| 714 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
while b:
lowerCAmelCase__ , lowerCAmelCase__ = b, a % b
return a
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(lowerCamelCase__ , a % b )
def _UpperCAmelCase ( ):
"""simple docstring"""
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 674 | 0 |
"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class a_ ( lowercase__ ):
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowercase , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(__lowercase , """num_attention_heads""" ) )
class a_ :
def __init__( self : Any , snake_case__ : List[str] , snake_case__ : Dict=13 , snake_case__ : str=64 , snake_case__ : str=3 , snake_case__ : Any=3 , snake_case__ : Tuple=2 , snake_case__ : List[str]=1 , snake_case__ : int=16 , snake_case__ : Optional[int]=[128, 256, 384] , snake_case__ : List[Any]=[4, 6, 8] , snake_case__ : str=[2, 3, 4] , snake_case__ : List[Any]=[16, 16, 16] , snake_case__ : int=0 , snake_case__ : str=[2, 2, 2] , snake_case__ : Tuple=[2, 2, 2] , snake_case__ : Dict=0.02 , snake_case__ : Optional[Any]=True , snake_case__ : Any=True , snake_case__ : List[str]=2 , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = kernel_size
lowerCAmelCase__ = stride
lowerCAmelCase__ = padding
lowerCAmelCase__ = hidden_sizes
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = depths
lowerCAmelCase__ = key_dim
lowerCAmelCase__ = drop_path_rate
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = attention_ratio
lowerCAmelCase__ = mlp_ratio
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = initializer_range
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Optional[int] , snake_case__ : Any , snake_case__ : str ):
lowerCAmelCase__ = LevitModel(config=__lowercase )
model.to(__lowercase )
model.eval()
lowerCAmelCase__ = model(__lowercase )
lowerCAmelCase__ = (self.image_size, self.image_size)
lowerCAmelCase__ , lowerCAmelCase__ = image_size[0], image_size[1]
for _ in range(4 ):
lowerCAmelCase__ = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
lowerCAmelCase__ = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : int , snake_case__ : List[str] , snake_case__ : List[str] ):
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = LevitForImageClassification(__lowercase )
model.to(__lowercase )
model.eval()
lowerCAmelCase__ = model(__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( lowercase__ , lowercase__ , unittest.TestCase ):
UpperCamelCase_ : str = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
UpperCamelCase_ : Dict = (
{
"""feature-extraction""": LevitModel,
"""image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : str = False
UpperCamelCase_ : Any = False
UpperCamelCase_ : str = False
UpperCamelCase_ : List[str] = False
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = LevitModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
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 _SCREAMING_SNAKE_CASE ( self : int ):
return
@unittest.skip(reason="""Levit does not use inputs_embeds""" )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
pass
@unittest.skip(reason="""Levit does not support input and output embeddings""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip(reason="""Levit does not output attentions""" )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
pass
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(__lowercase )
lowerCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __lowercase )
def _SCREAMING_SNAKE_CASE ( self : int ):
def check_hidden_states_output(snake_case__ : str , snake_case__ : Tuple , snake_case__ : List[Any] ):
lowerCAmelCase__ = model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__lowercase , __lowercase ) )
lowerCAmelCase__ = outputs.hidden_states
lowerCAmelCase__ = len(self.model_tester.depths ) + 1
self.assertEqual(len(__lowercase ) , __lowercase )
lowerCAmelCase__ = (self.model_tester.image_size, self.model_tester.image_size)
lowerCAmelCase__ , lowerCAmelCase__ = image_size[0], image_size[1]
for _ in range(4 ):
lowerCAmelCase__ = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
lowerCAmelCase__ = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ = True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
pass
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : List[str] , snake_case__ : List[str] , snake_case__ : Dict=False ):
lowerCAmelCase__ = super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowercase )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
if not self.model_tester.is_training:
return
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(__lowercase )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
lowerCAmelCase__ = model_class(__lowercase )
model.to(__lowercase )
model.train()
lowerCAmelCase__ = self._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
lowerCAmelCase__ = model(**__lowercase ).loss
loss.backward()
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowerCAmelCase__ = False
lowerCAmelCase__ = True
for model_class in self.all_model_classes:
if model_class in get_values(__lowercase ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
lowerCAmelCase__ = model_class(__lowercase )
model.gradient_checkpointing_enable()
model.to(__lowercase )
model.train()
lowerCAmelCase__ = self._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
lowerCAmelCase__ = model(**__lowercase ).loss
loss.backward()
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = [
{"""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(__lowercase ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}""" ):
lowerCAmelCase__ = problem_type["""title"""]
lowerCAmelCase__ = problem_type["""num_labels"""]
lowerCAmelCase__ = model_class(__lowercase )
model.to(__lowercase )
model.train()
lowerCAmelCase__ = self._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if problem_type["num_labels"] > 1:
lowerCAmelCase__ = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
lowerCAmelCase__ = 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=__lowercase ) as warning_list:
lowerCAmelCase__ = model(**__lowercase ).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 _SCREAMING_SNAKE_CASE ( self : Any ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = LevitModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def _SCREAMING_SNAKE_CASE ( self : int ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__lowercase )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=__lowercase , return_tensors="""pt""" ).to(__lowercase )
# forward pass
with torch.no_grad():
lowerCAmelCase__ = model(**__lowercase )
# verify the logits
lowerCAmelCase__ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowercase )
lowerCAmelCase__ = torch.tensor([1.0448, -0.3745, -1.8317] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1E-4 ) )
| 715 | """simple docstring"""
import os
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCamelCase__ ) )
lowerCAmelCase__ = os.path.join(lowerCamelCase__ , """triangle.txt""" )
with open(lowerCamelCase__ ) as f:
lowerCAmelCase__ = f.readlines()
lowerCAmelCase__ = []
for line in triangle:
lowerCAmelCase__ = []
for number in line.strip().split(""" """ ):
numbers_from_line.append(int(lowerCamelCase__ ) )
a.append(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
for j in range(len(a[i] ) ):
lowerCAmelCase__ = a[i - 1][j] if j != len(a[i - 1] ) else 0
lowerCAmelCase__ = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowerCamelCase__ , lowerCamelCase__ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
lowerCAmelCase__ = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716 | """simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase : Union[str, Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
__lowerCAmelCase : Optional[int] = json.load(f)
@require_torch
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Dict ):
return FSMTTokenizer.from_pretrained(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : Any ):
lowerCAmelCase__ = FSMTForConditionalGeneration.from_pretrained(snake_case__ ).to(snake_case__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Any , snake_case__ : int ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
lowerCAmelCase__ = F"""facebook/wmt19-{pair}"""
lowerCAmelCase__ = self.get_tokenizer(snake_case__ )
lowerCAmelCase__ = self.get_model(snake_case__ )
lowerCAmelCase__ = bleu_data[pair]["""src"""]
lowerCAmelCase__ = bleu_data[pair]["""tgt"""]
lowerCAmelCase__ = tokenizer(snake_case__ , return_tensors="""pt""" , truncation=snake_case__ , padding="""longest""" ).to(snake_case__ )
lowerCAmelCase__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
lowerCAmelCase__ = tokenizer.batch_decode(
snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
lowerCAmelCase__ = calculate_bleu(snake_case__ , snake_case__ )
print(snake_case__ )
self.assertGreaterEqual(scores["""bleu"""] , snake_case__ )
| 674 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=False , lowerCamelCase__=False ):
"""simple docstring"""
lowerCAmelCase__ = """backbone.""" if is_semantic else """"""
lowerCAmelCase__ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
(f"""{prefix}cls_token""", """beit.embeddings.cls_token"""),
(f"""{prefix}patch_embed.proj.weight""", """beit.embeddings.patch_embeddings.projection.weight"""),
(f"""{prefix}patch_embed.proj.bias""", """beit.embeddings.patch_embeddings.projection.bias"""),
(f"""{prefix}pos_embed""", """beit.embeddings.position_embeddings"""),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("""mask_token""", """beit.embeddings.mask_token"""),
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False , lowerCamelCase__=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
lowerCAmelCase__ = """backbone.""" if is_semantic else """"""
# queries, keys and values
lowerCAmelCase__ = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight""" )
lowerCAmelCase__ = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias""" )
lowerCAmelCase__ = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias""" )
lowerCAmelCase__ = in_proj_weight[
: config.hidden_size, :
]
lowerCAmelCase__ = q_bias
lowerCAmelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase__ = in_proj_weight[
-config.hidden_size :, :
]
lowerCAmelCase__ = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
lowerCAmelCase__ = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1""" )
lowerCAmelCase__ = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2""" )
lowerCAmelCase__ = gamma_a
lowerCAmelCase__ = gamma_a
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = dct.pop(lowerCamelCase__ )
lowerCAmelCase__ = val
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase__ = Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__ ).raw )
return im
@torch.no_grad()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ):
"""simple docstring"""
lowerCAmelCase__ = False if """rvlcdip""" in checkpoint_url else True
lowerCAmelCase__ = BeitConfig(use_absolute_position_embeddings=lowerCamelCase__ , use_mask_token=lowerCamelCase__ )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
lowerCAmelCase__ = 1024
lowerCAmelCase__ = 4096
lowerCAmelCase__ = 24
lowerCAmelCase__ = 16
# labels
if "rvlcdip" in checkpoint_url:
lowerCAmelCase__ = 16
lowerCAmelCase__ = """huggingface/label-files"""
lowerCAmelCase__ = """rvlcdip-id2label.json"""
lowerCAmelCase__ = json.load(open(hf_hub_download(lowerCamelCase__ , lowerCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
lowerCAmelCase__ = {int(lowerCamelCase__ ): v for k, v in idalabel.items()}
lowerCAmelCase__ = idalabel
lowerCAmelCase__ = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
lowerCAmelCase__ = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location="""cpu""" )["""model"""]
lowerCAmelCase__ = create_rename_keys(lowerCamelCase__ , has_lm_head=lowerCamelCase__ )
for src, dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
read_in_q_k_v(lowerCamelCase__ , lowerCamelCase__ , has_lm_head=lowerCamelCase__ )
# load HuggingFace model
lowerCAmelCase__ = BeitForMaskedImageModeling(lowerCamelCase__ ) if has_lm_head else BeitForImageClassification(lowerCamelCase__ )
model.eval()
model.load_state_dict(lowerCamelCase__ )
# Check outputs on an image
lowerCAmelCase__ = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=lowerCamelCase__ )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowerCamelCase__ , return_tensors="""pt""" )
lowerCAmelCase__ = encoding["""pixel_values"""]
lowerCAmelCase__ = model(lowerCamelCase__ )
lowerCAmelCase__ = outputs.logits
# verify logits
lowerCAmelCase__ = [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
assert logits.shape == torch.Size(lowerCamelCase__ ), "Shape of logits not as expected"
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCamelCase__ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowerCamelCase__ )
if push_to_hub:
if has_lm_head:
lowerCAmelCase__ = """dit-base""" if """base""" in checkpoint_url else """dit-large"""
else:
lowerCAmelCase__ = """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
image_processor.push_to_hub(
repo_path_or_name=Path(lowerCamelCase__ , lowerCamelCase__ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowerCamelCase__ , )
model.push_to_hub(
repo_path_or_name=Path(lowerCamelCase__ , lowerCamelCase__ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowerCamelCase__ , )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
__lowerCAmelCase : int = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 717 | """simple docstring"""
import pprint
import requests
__lowerCAmelCase : Union[str, Any] = "https://zenquotes.io/api"
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = random_quotes()
pprint.pprint(response)
| 674 | 0 |
"""simple docstring"""
from math import isqrt
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = False
return [i for i in range(2 , __lowerCAmelCase ) if is_prime[i]]
def _UpperCAmelCase ( lowerCamelCase__ = 10**8 ):
"""simple docstring"""
lowerCAmelCase__ = calculate_prime_numbers(max_number // 2 )
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = len(__lowerCAmelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F"{solution() = }")
| 718 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ ).to_dict()
config_dict.pop("""image_processor_type""" )
lowerCAmelCase__ = CLIPImageProcessor(**snake_case__ )
# save in new folder
model_config.save_pretrained(snake_case__ )
config.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
# make sure private variable is not incorrectly saved
lowerCAmelCase__ = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with self.assertRaisesRegex(
snake_case__ , """clip-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""clip-base""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
with self.assertRaisesRegex(
snake_case__ , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , revision="""aaaaaa""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
with self.assertRaisesRegex(
snake_case__ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , trust_remote_code=snake_case__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case__ ):
AutoImageProcessor.register(snake_case__ , snake_case__ )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = CustomImageProcessor.from_pretrained(snake_case__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Tuple = True
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# If remote code is not set, the default is to use local
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(snake_case__ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 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 GLPNImageProcessor
class a_ ( unittest.TestCase ):
def __init__( self : int , snake_case__ : Union[str, Any] , snake_case__ : Optional[int]=7 , snake_case__ : List[str]=3 , snake_case__ : Optional[int]=18 , snake_case__ : Dict=30 , snake_case__ : Any=400 , snake_case__ : List[Any]=True , snake_case__ : Dict=32 , snake_case__ : Any=True , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = image_size
lowerCAmelCase__ = min_resolution
lowerCAmelCase__ = max_resolution
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size_divisor
lowerCAmelCase__ = do_rescale
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class a_ ( __a , unittest.TestCase ):
UpperCamelCase_ : Dict = GLPNImageProcessor if is_vision_available() else None
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = GLPNImageProcessingTester(self )
@property
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
return self.image_processor_tester.prepare_image_processor_dict()
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase_ , """do_resize""" ) )
self.assertTrue(hasattr(lowerCAmelCase_ , """size_divisor""" ) )
self.assertTrue(hasattr(lowerCAmelCase_ , """resample""" ) )
self.assertTrue(hasattr(lowerCAmelCase_ , """do_rescale""" ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
pass
def _SCREAMING_SNAKE_CASE ( self : str ):
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 (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def _SCREAMING_SNAKE_CASE ( self : str ):
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 (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
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 (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 719 | """simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a_ :
def __init__( self : Optional[int] , snake_case__ : List[Any]=2 , snake_case__ : Any=3 , snake_case__ : Union[str, Any]=64 , snake_case__ : Any=None ):
lowerCAmelCase__ = np.random.default_rng(snake_case__ )
lowerCAmelCase__ = length
lowerCAmelCase__ = rng.normal(size=(length,) ).astype(np.floataa )
lowerCAmelCase__ = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Optional[Any] ):
return self.length
def __getitem__( self : List[str] , snake_case__ : Optional[int] ):
return {"x": self.x[i], "y": self.y[i]}
class a_ ( torch.nn.Module ):
def __init__( self : List[str] , snake_case__ : str=0 , snake_case__ : Dict=0 , snake_case__ : Any=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Any=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a[0] + self.b[0]
class a_ ( torch.nn.Module ):
def __init__( self : Any , snake_case__ : Union[str, Any]=0 , snake_case__ : Union[str, Any]=0 , snake_case__ : List[Any]=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any]=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a + self.b
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase__ = {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
lowerCAmelCase__ = load_dataset("""csv""" , data_files=lowerCamelCase__ )
lowerCAmelCase__ = datasets["""train"""].unique("""label""" )
lowerCAmelCase__ = {v: i for i, v in enumerate(lowerCamelCase__ )}
def tokenize_function(lowerCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase__ = tokenizer(
examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
if "label" in examples:
lowerCAmelCase__ = [label_to_id[l] for l in examples["""label"""]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase__ = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""sentence1""", """sentence2""", """label"""] , )
def collate_fn(lowerCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase__ = DataLoader(tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=2 )
lowerCAmelCase__ = DataLoader(tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 674 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Dict = logging.get_logger()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True ):
"""simple docstring"""
print(f"""Converting {name}...""" )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
A = timm.create_model("""levit_128s""" , pretrained=UpperCamelCase__ )
else:
A = timm.create_model("""levit_128""" , pretrained=UpperCamelCase__ )
if hidden_sizes == 192:
A = timm.create_model("""levit_192""" , pretrained=UpperCamelCase__ )
if hidden_sizes == 256:
A = timm.create_model("""levit_256""" , pretrained=UpperCamelCase__ )
if hidden_sizes == 384:
A = timm.create_model("""levit_384""" , pretrained=UpperCamelCase__ )
from_model.eval()
A = LevitForImageClassificationWithTeacher(UpperCamelCase__ ).eval()
A = OrderedDict()
A = from_model.state_dict()
A = list(from_model.state_dict().keys() )
A = list(our_model.state_dict().keys() )
print(len(UpperCamelCase__ ) , len(UpperCamelCase__ ) )
for i in range(len(UpperCamelCase__ ) ):
A = weights[og_keys[i]]
our_model.load_state_dict(UpperCamelCase__ )
A = torch.randn((2, 3, 224, 224) )
A = from_model(UpperCamelCase__ )
A = our_model(UpperCamelCase__ ).logits
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__ ), "The model logits don't match the original one."
A = name
print(UpperCamelCase__ )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
A = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f"""Pushed {checkpoint_name}""" )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = True ):
"""simple docstring"""
A = """imagenet-1k-id2label.json"""
A = 1000
A = (1, num_labels)
A = """huggingface/label-files"""
A = num_labels
A = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
A = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
A = idalabel
A = {v: k for k, v in idalabel.items()}
A = partial(UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ )
A = {
"""levit-128S""": 128,
"""levit-128""": 128,
"""levit-192""": 192,
"""levit-256""": 256,
"""levit-384""": 384,
}
A = {
"""levit-128S""": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
"""levit-128""": ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
"""levit-192""": ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
"""levit-256""": ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
"""levit-384""": ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , UpperCamelCase__ , names_to_config[model_name] , UpperCamelCase__ , UpperCamelCase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, expected_shape
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="levit-dump-folder/",
type=Path,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
__lowerCAmelCase : List[Any] = parser.parse_args()
__lowerCAmelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 720 | """simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = MobileBertConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = MobileBertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
lowerCAmelCase__ = load_tf_weights_in_mobilebert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = 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(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT 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."
)
__lowerCAmelCase : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a_ :
UpperCamelCase_ : Tuple = 42
UpperCamelCase_ : Union[str, Any] = None
UpperCamelCase_ : Tuple = None
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Node(1 )
lowerCAmelCase__ = Node(2 )
lowerCAmelCase__ = Node(3 )
lowerCAmelCase__ = Node(4 )
lowerCAmelCase__ = Node(5 )
return tree
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = []
if root is None:
return output
lowerCAmelCase__ = deque([root] )
while process_queue:
lowerCAmelCase__ = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = []
def populate_output(lowerCamelCase__ , lowerCamelCase__ ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(_UpperCamelCase , _UpperCamelCase )
return output
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = []
def populate_output(lowerCamelCase__ , lowerCamelCase__ ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(_UpperCamelCase , _UpperCamelCase )
return output
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if root is None:
return []
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
lowerCAmelCase__ = height(_UpperCamelCase )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(_UpperCamelCase , _UpperCamelCase ) )
lowerCAmelCase__ = 1
else:
output.append(get_nodes_from_right_to_left(_UpperCamelCase , _UpperCamelCase ) )
lowerCAmelCase__ = 0
return output
def _UpperCAmelCase ( ): # Main function for testing.
"""simple docstring"""
lowerCAmelCase__ = make_tree()
print(f"""In-order Traversal: {inorder(_UpperCamelCase )}""" )
print(f"""Pre-order Traversal: {preorder(_UpperCamelCase )}""" )
print(f"""Post-order Traversal: {postorder(_UpperCamelCase )}""" , """\n""" )
print(f"""Height of Tree: {height(_UpperCamelCase )}""" , """\n""" )
print("""Complete Level Order Traversal: """ )
print(level_order(_UpperCamelCase ) , """\n""" )
print("""Level-wise order Traversal: """ )
for level in range(1 , height(_UpperCamelCase ) + 1 ):
print(f"""Level {level}:""" , get_nodes_from_left_to_right(_UpperCamelCase , level=_UpperCamelCase ) )
print("""\nZigZag order Traversal: """ )
print(zigzag(_UpperCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 721 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
lowerCAmelCase__ = f"""The input value of [n={number}] has to be > 0"""
raise ValueError(lowerCamelCase__ )
else:
lowerCAmelCase__ = sylvester(number - 1 )
lowerCAmelCase__ = num - 1
lowerCAmelCase__ = num
return lower * upper + 1
if __name__ == "__main__":
print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")
| 674 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( __a , unittest.TestCase ):
UpperCamelCase_ : Dict = None
UpperCamelCase_ : List[Any] = BloomTokenizerFast
UpperCamelCase_ : str = BloomTokenizerFast
UpperCamelCase_ : Optional[Any] = True
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[Any] = "tokenizer_file"
UpperCamelCase_ : Optional[Any] = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
super().setUp()
lowerCAmelCase__ = BloomTokenizerFast.from_pretrained("""bigscience/tokenizer""" )
tokenizer.save_pretrained(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : Tuple , **snake_case__ : Dict ):
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **A__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = ["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
lowerCAmelCase__ = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]
lowerCAmelCase__ = tokenizer.batch_encode_plus(A__ )["""input_ids"""]
self.assertListEqual(A__ , A__ )
lowerCAmelCase__ = tokenizer.batch_decode(A__ )
self.assertListEqual(A__ , A__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : List[str]=6 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
lowerCAmelCase__ = """This is a simple input"""
lowerCAmelCase__ = ["""This is a simple input 1""", """This is a simple input 2"""]
lowerCAmelCase__ = ("""This is a simple input""", """This is a pair""")
lowerCAmelCase__ = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
try:
tokenizer_r.encode(A__ , max_length=A__ )
tokenizer_r.encode_plus(A__ , max_length=A__ )
tokenizer_r.batch_encode_plus(A__ , max_length=A__ )
tokenizer_r.encode(A__ , max_length=A__ )
tokenizer_r.batch_encode_plus(A__ , max_length=A__ )
except ValueError:
self.fail("""Bloom Tokenizer should be able to deal with padding""" )
lowerCAmelCase__ = None # Hotfixing padding = None
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding="""max_length""" )
# Simple input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding="""max_length""" )
# Simple input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding="""max_length""" , )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode , A__ , max_length=A__ , padding="""max_length""" )
# Pair input
self.assertRaises(A__ , tokenizer_r.encode_plus , A__ , max_length=A__ , padding="""max_length""" )
# Pair input
self.assertRaises(
A__ , tokenizer_r.batch_encode_plus , A__ , max_length=A__ , padding="""max_length""" , )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = load_dataset("""xnli""" , """all_languages""" , split="""test""" , streaming=A__ )
lowerCAmelCase__ = next(iter(A__ ) )["""premise"""] # pick up one data
lowerCAmelCase__ = list(sample_data.values() )
lowerCAmelCase__ = list(map(tokenizer.encode , A__ ) )
lowerCAmelCase__ = [tokenizer.decode(A__ , clean_up_tokenization_spaces=A__ ) for x in output_tokens]
self.assertListEqual(A__ , A__ )
def _SCREAMING_SNAKE_CASE ( self : Any ):
# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
# any sequence length constraints. This test of the parent class will fail since it relies on the
# maximum sequence length of the positoonal embeddings.
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 700 | """simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : Optional[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Tuple = PegasusTokenizer
UpperCamelCase_ : Any = PegasusTokenizerFast
UpperCamelCase_ : int = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = PegasusTokenizer(snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : Optional[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any] ):
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = """</s>"""
lowerCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """</s>""" )
self.assertEqual(vocab_keys[-1] , """v""" )
self.assertEqual(len(snake_case__ ) , 1103 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
""" </s> <pad> <pad> <pad>"""
)
lowerCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
lowerCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowerCAmelCase__ = """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
lowerCAmelCase__ = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
lowerCAmelCase__ = tokenizer([raw_input_str] , return_tensors=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
lowerCAmelCase__ = """To ensure a smooth flow of bank resolutions."""
lowerCAmelCase__ = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
lowerCAmelCase__ = tokenizer([raw_input_str] , return_tensors=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = ["""This is going to be way too long.""" * 150, """short example"""]
lowerCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
lowerCAmelCase__ = self._large_tokenizer(snake_case__ , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
lowerCAmelCase__ = self._large_tokenizer(
text_target=snake_case__ , max_length=5 , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case__ ) == 2 # input_ids, attention_mask.
@slow
def _SCREAMING_SNAKE_CASE ( self : str ):
# fmt: off
lowerCAmelCase__ = {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=snake_case__ , model_name="""google/bigbird-pegasus-large-arxiv""" , revision="""ba85d0851d708441f91440d509690f1ab6353415""" , )
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = PegasusTokenizer
UpperCamelCase_ : Optional[int] = PegasusTokenizerFast
UpperCamelCase_ : Union[str, Any] = True
UpperCamelCase_ : Optional[int] = True
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = PegasusTokenizer(snake_case__ , offset=0 , mask_token_sent=snake_case__ , mask_token="""[MASK]""" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Dict ):
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : List[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Dict ):
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
""" <pad> <pad> <pad>"""
)
lowerCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
lowerCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
@require_torch
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = ["""This is going to be way too long.""" * 1000, """short example"""]
lowerCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
lowerCAmelCase__ = self._large_tokenizer(snake_case__ , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
lowerCAmelCase__ = self._large_tokenizer(
text_target=snake_case__ , max_length=5 , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case__ ) == 2 # input_ids, attention_mask.
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = (
"""This is an example string that is used to test the original TF implementation against the HF"""
""" implementation"""
)
lowerCAmelCase__ = self._large_tokenizer(snake_case__ ).input_ids
self.assertListEqual(
snake_case__ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1] , )
| 674 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCAmelCase : List[Any] = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
"GroupViTTextConfig",
"GroupViTVisionConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : int = [
"GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GroupViTModel",
"GroupViTPreTrainedModel",
"GroupViTTextModel",
"GroupViTVisionModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : str = [
"TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGroupViTModel",
"TFGroupViTPreTrainedModel",
"TFGroupViTTextModel",
"TFGroupViTVisionModel",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
__lowerCAmelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 701 | """simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : int ):
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=snake_case__ , )
assert hasattr(self , """env""" )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Optional[Any] ):
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase__ = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase__ = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase__ = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase__ = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=snake_case__ , instance_type=self.instance_type , debugger_hook_config=snake_case__ , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=snake_case__ , py_version="""py36""" , )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : str ):
TrainingJobAnalytics(snake_case__ ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : List[str] ):
# create estimator
lowerCAmelCase__ = self.create_estimator(snake_case__ )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , snake_case__ )
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class a_ :
def __init__( self : Any , snake_case__ : List[str] = 6 ):
lowerCAmelCase__ = None
lowerCAmelCase__ = None
self.create_linked_list(__UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : int ):
lowerCAmelCase__ = Node()
lowerCAmelCase__ = current_node
lowerCAmelCase__ = current_node
lowerCAmelCase__ = current_node
for _ in range(1 , __UpperCamelCase ):
lowerCAmelCase__ = Node()
lowerCAmelCase__ = current_node
lowerCAmelCase__ = previous_node
lowerCAmelCase__ = current_node
lowerCAmelCase__ = self.front
lowerCAmelCase__ = previous_node
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
self.check_can_perform_operation()
return self.front.data if self.front else None
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : List[Any] ):
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowerCAmelCase__ = self.rear.next
if self.rear:
lowerCAmelCase__ = data
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowerCAmelCase__ = self.front.data
lowerCAmelCase__ = None
return data
lowerCAmelCase__ = self.front
lowerCAmelCase__ = old_front.next
lowerCAmelCase__ = old_front.data
lowerCAmelCase__ = None
return data
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
if self.is_empty():
raise Exception("""Empty Queue""" )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""" )
class a_ :
def __init__( self : Any ):
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 702 | """simple docstring"""
from math import pi, sqrt
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_71.5:
raise OverflowError("""math range error""" )
elif num - int(lowerCamelCase__ ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(lowerCamelCase__ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def _UpperCAmelCase ( ):
"""simple docstring"""
assert gamma(0.5 ) == sqrt(lowerCamelCase__ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__lowerCAmelCase : Dict = 1.0
while num:
__lowerCAmelCase : Any = float(input("Gamma of: "))
print(F"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| 674 | 0 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = AutoConfig.from_pretrained(lowerCAmelCase_ )
lowerCAmelCase__ = FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase_ )
lowerCAmelCase__ = checkpoints.load_tax_checkpoint(lowerCAmelCase_ )
lowerCAmelCase__ = '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
if config.model_type == "t5":
lowerCAmelCase__ = '''SelfAttention'''
if config.model_type == "longt5" and config.encoder_attention_type == "local":
lowerCAmelCase__ = '''LocalSelfAttention'''
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ = '''TransientGlobalSelfAttention'''
else:
raise ValueError(
"""Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`"""
""" attribute with a value from [\'local\', \'transient-global].""" )
# Encoder
for layer_index in range(config.num_layers ):
lowerCAmelCase__ = f"""layers_{str(lowerCAmelCase_ )}"""
# Self-Attention
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']
# Layer Normalization
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
if split_mlp_wi:
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
lowerCAmelCase__ = tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
lowerCAmelCase__ = flax_model.params['''encoder''']['''block'''][str(lowerCAmelCase_ )]['''layer''']
lowerCAmelCase__ = tax_attention_key
lowerCAmelCase__ = tax_attention_out
lowerCAmelCase__ = tax_attention_query
lowerCAmelCase__ = tax_attention_value
lowerCAmelCase__ = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ = tax_global_layer_norm
if split_mlp_wi:
lowerCAmelCase__ = tax_mlp_wi_a
lowerCAmelCase__ = tax_mlp_wi_a
else:
lowerCAmelCase__ = tax_mlp_wi
lowerCAmelCase__ = tax_mlp_wo
lowerCAmelCase__ = tax_mlp_layer_norm
lowerCAmelCase__ = flax_model_encoder_layer_block
# Only for layer 0:
lowerCAmelCase__ = tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
lowerCAmelCase__ = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ = tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
lowerCAmelCase__ = tax_encoder_global_rel_embedding
# Assigning
lowerCAmelCase__ = tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
lowerCAmelCase__ = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
lowerCAmelCase__ = f"""layers_{str(lowerCAmelCase_ )}"""
# Self-Attention
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
# Layer Normalization
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
'''scale'''
]
# Encoder-Decoder-Attention
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
lowerCAmelCase__ = tax_enc_dec_attention_module['''key''']['''kernel''']
lowerCAmelCase__ = tax_enc_dec_attention_module['''out''']['''kernel''']
lowerCAmelCase__ = tax_enc_dec_attention_module['''query''']['''kernel''']
lowerCAmelCase__ = tax_enc_dec_attention_module['''value''']['''kernel''']
# Layer Normalization
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
# MLP
if split_mlp_wi:
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
else:
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
# Layer Normalization
lowerCAmelCase__ = tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
# Assigning
lowerCAmelCase__ = flax_model.params['''decoder''']['''block'''][str(lowerCAmelCase_ )]['''layer''']
lowerCAmelCase__ = tax_attention_key
lowerCAmelCase__ = tax_attention_out
lowerCAmelCase__ = tax_attention_query
lowerCAmelCase__ = tax_attention_value
lowerCAmelCase__ = tax_pre_attention_layer_norm
lowerCAmelCase__ = tax_enc_dec_attention_key
lowerCAmelCase__ = tax_enc_dec_attention_out
lowerCAmelCase__ = tax_enc_dec_attention_query
lowerCAmelCase__ = tax_enc_dec_attention_value
lowerCAmelCase__ = tax_cross_layer_norm
if split_mlp_wi:
lowerCAmelCase__ = tax_mlp_wi_a
lowerCAmelCase__ = tax_mlp_wi_a
else:
lowerCAmelCase__ = tax_mlp_wi
lowerCAmelCase__ = tax_mlp_wo
lowerCAmelCase__ = txa_mlp_layer_norm
lowerCAmelCase__ = flax_model_decoder_layer_block
# Decoder Normalization
lowerCAmelCase__ = tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
lowerCAmelCase__ = txa_decoder_norm
# Only for layer 0:
lowerCAmelCase__ = tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
lowerCAmelCase__ = tax_decoder_rel_embedding
# Token Embeddings
lowerCAmelCase__ = tax_model['''target''']['''token_embedder''']['''embedding''']
lowerCAmelCase__ = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
lowerCAmelCase__ = tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
flax_model.save_pretrained(lowerCAmelCase_ )
print("""T5X Model was sucessfully converted!""" )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
)
parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
parser.add_argument(
"--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
)
__lowerCAmelCase : List[Any] = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 703 | """simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a_ :
def __init__( self : Optional[int] , snake_case__ : Optional[Any] , snake_case__ : Any=13 , snake_case__ : int=30 , snake_case__ : int=2 , snake_case__ : Union[str, Any]=3 , snake_case__ : Dict=True , snake_case__ : Optional[int]=True , snake_case__ : List[Any]=32 , snake_case__ : List[str]=2 , snake_case__ : Optional[Any]=4 , snake_case__ : Optional[int]=37 , snake_case__ : Tuple="gelu" , snake_case__ : str=0.1 , snake_case__ : Any=0.1 , snake_case__ : int=10 , snake_case__ : Dict=0.02 , snake_case__ : Union[str, Any]=3 , snake_case__ : str=None , snake_case__ : List[Any]=2 , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
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__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = scope
lowerCAmelCase__ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowerCAmelCase__ = (image_size // patch_size) ** 2
lowerCAmelCase__ = num_patches + 2
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
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=snake_case__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : int , snake_case__ : Optional[Any] , snake_case__ : List[str] ):
lowerCAmelCase__ = TFDeiTModel(config=snake_case__ )
lowerCAmelCase__ = model(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 , snake_case__ : Dict , snake_case__ : Dict , snake_case__ : Dict ):
lowerCAmelCase__ = TFDeiTForMaskedImageModeling(config=snake_case__ )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = TFDeiTForMaskedImageModeling(snake_case__ )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Union[str, Any] , snake_case__ : Any , snake_case__ : Tuple ):
lowerCAmelCase__ = self.type_sequence_label_size
lowerCAmelCase__ = TFDeiTForImageClassification(snake_case__ )
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = TFDeiTForImageClassification(snake_case__ )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class a_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Optional[Any] = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : Any = (
{
"feature-extraction": TFDeiTModel,
"image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[int] = False
UpperCamelCase_ : int = False
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = TFDeiTModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
lowerCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , tf.keras.layers.Dense ) )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : Union[str, Any] , snake_case__ : int , snake_case__ : Union[str, Any]=False ):
lowerCAmelCase__ = super()._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def _SCREAMING_SNAKE_CASE ( self : Any ):
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = TFDeiTModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Any ):
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=snake_case__ , return_tensors="""tf""" )
# forward pass
lowerCAmelCase__ = model(**snake_case__ )
# verify the logits
lowerCAmelCase__ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case__ )
lowerCAmelCase__ = tf.constant([-1.0266, 0.1912, -1.2861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , snake_case__ , atol=1E-4 ) )
| 674 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[str] = logging.get_logger(__name__)
__lowerCAmelCase : List[Any] = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
),
}
class a_ ( _UpperCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Optional[int] = """dpr"""
def __init__( self : Dict , snake_case__ : Union[str, Any]=30522 , snake_case__ : str=768 , snake_case__ : Optional[int]=12 , snake_case__ : Union[str, Any]=12 , snake_case__ : Any=3072 , snake_case__ : Optional[int]="gelu" , snake_case__ : str=0.1 , snake_case__ : Optional[Any]=0.1 , snake_case__ : Optional[Any]=512 , snake_case__ : Dict=2 , snake_case__ : Union[str, Any]=0.02 , snake_case__ : Optional[int]=1E-12 , snake_case__ : List[str]=0 , snake_case__ : Optional[Any]="absolute" , snake_case__ : List[Any] = 0 , **snake_case__ : List[Any] , ):
super().__init__(pad_token_id=lowercase__ , **lowercase__ )
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = max_position_embeddings
lowerCAmelCase__ = type_vocab_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = layer_norm_eps
lowerCAmelCase__ = projection_dim
lowerCAmelCase__ = position_embedding_type
| 704 | """simple docstring"""
from __future__ import annotations
from math import gcd
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 2 , lowerCamelCase__ = 1 , lowerCamelCase__ = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
return (pow(lowerCamelCase__ , 2 ) + step) % modulus
for _ in range(lowerCamelCase__ ):
# These track the position within the cycle detection logic.
lowerCAmelCase__ = seed
lowerCAmelCase__ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowerCAmelCase__ = gcd(hare - tortoise , lowerCamelCase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowerCAmelCase__ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
__lowerCAmelCase : List[str] = parser.parse_args()
__lowerCAmelCase : Dict = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"{args.num} is probably prime")
else:
__lowerCAmelCase : List[str] = args.num // divisor
print(F"{args.num} = {divisor} * {quotient}")
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_=0 ):
"""simple docstring"""
return sorted(a_ , key=lambda lowerCAmelCase_ : x[column] )
def _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=float("""inf""" ) ):
"""simple docstring"""
for i in range(points_counts - 1 ):
for j in range(i + 1 , a_ ):
lowerCAmelCase__ = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
lowerCAmelCase__ = current_dis
return min_dis
def _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=float("""inf""" ) ):
"""simple docstring"""
for i in range(min(6 , points_counts - 1 ) , a_ ):
for j in range(max(0 , i - 6 ) , a_ ):
lowerCAmelCase__ = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
lowerCAmelCase__ = current_dis
return min_dis
def _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if points_counts <= 3:
return dis_between_closest_pair(a_ , a_ )
# recursion
lowerCAmelCase__ = points_counts // 2
lowerCAmelCase__ = closest_pair_of_points_sqr(
a_ , points_sorted_on_y[:mid] , a_ )
lowerCAmelCase__ = closest_pair_of_points_sqr(
a_ , points_sorted_on_y[mid:] , points_counts - mid )
lowerCAmelCase__ = min(a_ , a_ )
lowerCAmelCase__ = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(a_ )
lowerCAmelCase__ = dis_between_closest_in_strip(
a_ , len(a_ ) , a_ )
return min(a_ , a_ )
def _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowerCAmelCase__ = column_based_sort(a_ , column=0 )
lowerCAmelCase__ = column_based_sort(a_ , column=1 )
return (
closest_pair_of_points_sqr(
a_ , a_ , a_ )
) ** 0.5
if __name__ == "__main__":
__lowerCAmelCase : Optional[int] = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("Distance:", closest_pair_of_points(points, len(points)))
| 705 | """simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = TapasConfig.from_json_file(lowerCamelCase__ )
# set absolute/relative position embeddings parameter
lowerCAmelCase__ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "WTQ":
# run_task_main.py hparams
lowerCAmelCase__ = 4
lowerCAmelCase__ = True
# hparam_utils.py hparams
lowerCAmelCase__ = 0.66_46_94
lowerCAmelCase__ = 0.20_79_51
lowerCAmelCase__ = 0.12_11_94
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = 0.0_35_25_13
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
lowerCAmelCase__ = 4
lowerCAmelCase__ = False
# hparam_utils.py hparams
lowerCAmelCase__ = 36.45_19
lowerCAmelCase__ = 0.90_34_21
lowerCAmelCase__ = 2_22.0_88
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = 0.76_31_41
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "TABFACT":
lowerCAmelCase__ = TapasForSequenceClassification(config=lowerCamelCase__ )
elif task == "MLM":
lowerCAmelCase__ = TapasForMaskedLM(config=lowerCamelCase__ )
elif task == "INTERMEDIATE_PRETRAINING":
lowerCAmelCase__ = TapasModel(config=lowerCamelCase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowerCamelCase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
lowerCAmelCase__ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" , model_max_length=512 )
tokenizer.save_pretrained(lowerCamelCase__ )
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
)
parser.add_argument(
"--reset_position_index_per_cell",
default=False,
action="store_true",
help="Whether to use relative position embeddings or not. Defaults to True.",
)
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--tapas_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained TAPAS 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."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 674 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = """hf-internal-testing/tiny-random-t5"""
lowerCAmelCase__ = AutoTokenizer.from_pretrained(snake_case__ )
lowerCAmelCase__ = AutoModelForSeqaSeqLM.from_pretrained(snake_case__ )
lowerCAmelCase__ = tokenizer("""This is me""" , return_tensors="""pt""" )
lowerCAmelCase__ = model.to_bettertransformer()
self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
lowerCAmelCase__ = model.generate(**snake_case__ )
lowerCAmelCase__ = model.reverse_bettertransformer()
self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoModelForSeqaSeqLM.from_pretrained(snake_case__ )
self.assertFalse(
any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
lowerCAmelCase__ = model_reloaded.generate(**snake_case__ )
self.assertTrue(torch.allclose(snake_case__ , snake_case__ ) )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = """hf-internal-testing/tiny-random-t5"""
lowerCAmelCase__ = AutoModelForSeqaSeqLM.from_pretrained(snake_case__ )
lowerCAmelCase__ = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(snake_case__ ):
model.save_pretrained(snake_case__ )
lowerCAmelCase__ = model.reverse_bettertransformer()
model.save_pretrained(snake_case__ )
| 706 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ = 50 ):
"""simple docstring"""
lowerCAmelCase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F"{solution() = }")
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
__lowerCAmelCase : int = "Muhammad Umer Farooq"
__lowerCAmelCase : str = "MIT"
__lowerCAmelCase : str = "1.0.0"
__lowerCAmelCase : int = "Muhammad Umer Farooq"
__lowerCAmelCase : Tuple = "[email protected]"
__lowerCAmelCase : Dict = "Alpha"
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class a_ ( _UpperCamelCase ):
def __init__( self : List[Any] , snake_case__ : str ):
super().__init__()
lowerCAmelCase__ = []
lowerCAmelCase__ = domain
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : str , snake_case__ : list[tuple[str, str | None]] ):
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
lowerCAmelCase__ = parse.urljoin(self.domain , __a )
self.urls.append(__a )
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return ".".join(get_sub_domain_name(lowercase_ ).split(""".""" )[-2:] )
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return parse.urlparse(lowercase_ ).netloc
def _UpperCAmelCase ( lowerCamelCase__ = "https://github.com" ):
"""simple docstring"""
lowerCAmelCase__ = get_domain_name(lowercase_ )
# Initialize the parser
lowerCAmelCase__ = Parser(lowercase_ )
try:
# Open URL
lowerCAmelCase__ = requests.get(lowercase_ )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
lowerCAmelCase__ = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
lowerCAmelCase__ = requests.get(lowercase_ )
# Get the valid email.
lowerCAmelCase__ = re.findall("""[a-zA-Z0-9]+@""" + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(lowercase_ )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(lowercase_ )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = emails_from_url("https://github.com")
print(F"{len(emails)} emails found:")
print("\n".join(sorted(emails)))
| 707 | """simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse("0.8.3"):
raise Exception("requires gluonnlp == 0.8.3")
if version.parse(mx.__version__) != version.parse("1.5.0"):
raise Exception("requires mxnet == 1.5.0")
logging.set_verbosity_info()
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Any = "The Nymphenburg Palace is a beautiful palace in Munich!"
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {
"""attention_cell""": """multi_head""",
"""num_layers""": 4,
"""units""": 1024,
"""hidden_size""": 768,
"""max_length""": 512,
"""num_heads""": 8,
"""scaled""": True,
"""dropout""": 0.1,
"""use_residual""": True,
"""embed_size""": 1024,
"""embed_dropout""": 0.1,
"""word_embed""": None,
"""layer_norm_eps""": 1e-5,
"""token_type_vocab_size""": 2,
}
lowerCAmelCase__ = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
lowerCAmelCase__ = BERTEncoder(
attention_cell=predefined_args["""attention_cell"""] , num_layers=predefined_args["""num_layers"""] , units=predefined_args["""units"""] , hidden_size=predefined_args["""hidden_size"""] , max_length=predefined_args["""max_length"""] , num_heads=predefined_args["""num_heads"""] , scaled=predefined_args["""scaled"""] , dropout=predefined_args["""dropout"""] , output_attention=lowerCamelCase__ , output_all_encodings=lowerCamelCase__ , use_residual=predefined_args["""use_residual"""] , activation=predefined_args.get("""activation""" , """gelu""" ) , layer_norm_eps=predefined_args.get("""layer_norm_eps""" , lowerCamelCase__ ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
lowerCAmelCase__ = """openwebtext_ccnews_stories_books_cased"""
# Specify download folder to Gluonnlp's vocab
lowerCAmelCase__ = os.path.join(get_home_dir() , """models""" )
lowerCAmelCase__ = _load_vocab(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , cls=lowerCamelCase__ )
lowerCAmelCase__ = nlp.model.BERTModel(
lowerCamelCase__ , len(lowerCamelCase__ ) , units=predefined_args["""units"""] , embed_size=predefined_args["""embed_size"""] , embed_dropout=predefined_args["""embed_dropout"""] , word_embed=predefined_args["""word_embed"""] , use_pooler=lowerCamelCase__ , use_token_type_embed=lowerCamelCase__ , token_type_vocab_size=predefined_args["""token_type_vocab_size"""] , use_classifier=lowerCamelCase__ , use_decoder=lowerCamelCase__ , )
original_bort.load_parameters(lowerCamelCase__ , cast_dtype=lowerCamelCase__ , ignore_extra=lowerCamelCase__ )
lowerCAmelCase__ = original_bort._collect_params_with_prefix()
# Build our config 🤗
lowerCAmelCase__ = {
"""architectures""": ["""BertForMaskedLM"""],
"""attention_probs_dropout_prob""": predefined_args["""dropout"""],
"""hidden_act""": """gelu""",
"""hidden_dropout_prob""": predefined_args["""dropout"""],
"""hidden_size""": predefined_args["""embed_size"""],
"""initializer_range""": 0.02,
"""intermediate_size""": predefined_args["""hidden_size"""],
"""layer_norm_eps""": predefined_args["""layer_norm_eps"""],
"""max_position_embeddings""": predefined_args["""max_length"""],
"""model_type""": """bort""",
"""num_attention_heads""": predefined_args["""num_heads"""],
"""num_hidden_layers""": predefined_args["""num_layers"""],
"""pad_token_id""": 1, # 2 = BERT, 1 = RoBERTa
"""type_vocab_size""": 1, # 2 = BERT, 1 = RoBERTa
"""vocab_size""": len(lowerCamelCase__ ),
}
lowerCAmelCase__ = BertConfig.from_dict(lowerCamelCase__ )
lowerCAmelCase__ = BertForMaskedLM(lowerCamelCase__ )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(lowerCamelCase__ ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = hf_param.shape
lowerCAmelCase__ = to_torch(params[gluon_param] )
lowerCAmelCase__ = gluon_param.shape
assert (
shape_hf == shape_gluon
), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""
return gluon_param
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , """word_embed.0.weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , """encoder.position_weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , """encoder.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , """encoder.layer_norm.gamma""" )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
lowerCAmelCase__ = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
lowerCAmelCase__ = hf_bort_model.bert.encoder.layer[i]
# self attention
lowerCAmelCase__ = layer.attention.self
lowerCAmelCase__ = check_and_map_params(
self_attn.key.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.key.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight""" )
# self attention output
lowerCAmelCase__ = layer.attention.output
lowerCAmelCase__ = check_and_map_params(
self_output.dense.bias , f"""encoder.transformer_cells.{i}.proj.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_output.dense.weight , f"""encoder.transformer_cells.{i}.proj.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.layer_norm.gamma""" )
# intermediate
lowerCAmelCase__ = layer.intermediate
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias""" )
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight""" )
# output
lowerCAmelCase__ = layer.output
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma""" )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
lowerCAmelCase__ = RobertaTokenizer.from_pretrained("""roberta-base""" )
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ )["""input_ids"""]
# Get gluon output
lowerCAmelCase__ = mx.nd.array([input_ids] )
lowerCAmelCase__ = original_bort(inputs=lowerCamelCase__ , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(lowerCamelCase__ )
lowerCAmelCase__ = BertModel.from_pretrained(lowerCamelCase__ )
hf_bort_model.eval()
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ , return_tensors="""pt""" )
lowerCAmelCase__ = hf_bort_model(**lowerCamelCase__ )[0]
lowerCAmelCase__ = output_gluon[0].asnumpy()
lowerCAmelCase__ = output_hf[0].detach().numpy()
lowerCAmelCase__ = np.max(np.abs(hf_layer - gluon_layer ) ).item()
lowerCAmelCase__ = np.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-3 )
if success:
print("""✔️ Both model do output the same tensors""" )
else:
print("""❌ Both model do **NOT** output the same tensors""" )
print("""Absolute difference is:""" , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : str = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 674 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : List[Any] = ["model.decoder.embed_positions.weights"]
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if "emb" in name:
lowerCAmelCase__ = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
lowerCAmelCase__ = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
lowerCAmelCase__ = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
lowerCAmelCase__ = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
lowerCAmelCase__ = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
lowerCAmelCase__ = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
lowerCAmelCase__ = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
lowerCAmelCase__ = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
lowerCAmelCase__ = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
lowerCAmelCase__ = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
lowerCAmelCase__ = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = list(state_dict.keys() )
lowerCAmelCase__ = {}
for key in keys:
lowerCAmelCase__ = state_dict.pop(__UpperCamelCase )
lowerCAmelCase__ = rename_keys(__UpperCamelCase )
if "in_proj_weight" in key:
# split fused qkv proj
lowerCAmelCase__ = val[:hidden_size, :]
lowerCAmelCase__ = val[hidden_size : 2 * hidden_size, :]
lowerCAmelCase__ = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
lowerCAmelCase__ = val
else:
lowerCAmelCase__ = val
return state_dict, enc_dec_proj_state_dict
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if checkpoint == "small":
# default config values
lowerCAmelCase__ = 1024
lowerCAmelCase__ = 24
lowerCAmelCase__ = 16
elif checkpoint == "medium":
lowerCAmelCase__ = 1536
lowerCAmelCase__ = 48
lowerCAmelCase__ = 24
elif checkpoint == "large":
lowerCAmelCase__ = 2048
lowerCAmelCase__ = 48
lowerCAmelCase__ = 32
else:
raise ValueError(f"""Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.""" )
lowerCAmelCase__ = MusicgenDecoderConfig(
hidden_size=__UpperCamelCase , ffn_dim=hidden_size * 4 , num_hidden_layers=__UpperCamelCase , num_attention_heads=__UpperCamelCase , )
return config
@torch.no_grad()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__="cpu" ):
"""simple docstring"""
lowerCAmelCase__ = MusicGen.get_pretrained(__UpperCamelCase , device=__UpperCamelCase )
lowerCAmelCase__ = decoder_config_from_checkpoint(__UpperCamelCase )
lowerCAmelCase__ = fairseq_model.lm.state_dict()
lowerCAmelCase__ , lowerCAmelCase__ = rename_state_dict(
__UpperCamelCase , hidden_size=decoder_config.hidden_size )
lowerCAmelCase__ = TaEncoderModel.from_pretrained("""t5-base""" )
lowerCAmelCase__ = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
lowerCAmelCase__ = MusicgenForCausalLM(__UpperCamelCase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
lowerCAmelCase__ , lowerCAmelCase__ = decoder.load_state_dict(__UpperCamelCase , strict=__UpperCamelCase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(__UpperCamelCase )
if len(__UpperCamelCase ) > 0:
raise ValueError(f"""Missing key(s) in state_dict: {missing_keys}""" )
if len(__UpperCamelCase ) > 0:
raise ValueError(f"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
lowerCAmelCase__ = MusicgenForConditionalGeneration(text_encoder=__UpperCamelCase , audio_encoder=__UpperCamelCase , decoder=__UpperCamelCase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(__UpperCamelCase )
# check we can do a forward pass
lowerCAmelCase__ = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
lowerCAmelCase__ = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
lowerCAmelCase__ = model(input_ids=__UpperCamelCase , decoder_input_ids=__UpperCamelCase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""t5-base""" )
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
lowerCAmelCase__ = MusicgenProcessor(feature_extractor=__UpperCamelCase , tokenizer=__UpperCamelCase )
# set the appropriate bos/pad token ids
lowerCAmelCase__ = 2048
lowerCAmelCase__ = 2048
# set other default generation config params
lowerCAmelCase__ = int(30 * audio_encoder.config.frame_rate )
lowerCAmelCase__ = True
lowerCAmelCase__ = 3.0
if pytorch_dump_folder is not None:
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
logger.info(f"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(__UpperCamelCase )
processor.save_pretrained(__UpperCamelCase )
if repo_id:
logger.info(f"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(__UpperCamelCase )
processor.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
__lowerCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 708 | """simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self : Optional[int] ):
lowerCAmelCase__ = """"""
lowerCAmelCase__ = """"""
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
lowerCAmelCase__ = 256
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Union[str, Any] ):
lowerCAmelCase__ = cva.imread(snake_case__ , 0 )
lowerCAmelCase__ = copy.deepcopy(self.img )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="""x""" )
lowerCAmelCase__ = np.sum(snake_case__ )
for i in range(len(snake_case__ ) ):
lowerCAmelCase__ = x[i] / self.k
self.sk += prk
lowerCAmelCase__ = (self.L - 1) * self.sk
if self.rem != 0:
lowerCAmelCase__ = int(last % last )
lowerCAmelCase__ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(snake_case__ )
lowerCAmelCase__ = int(np.ma.count(self.img ) / self.img[1].size )
lowerCAmelCase__ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCAmelCase__ = self.img[j][i]
if num != self.last_list[num]:
lowerCAmelCase__ = self.last_list[num]
cva.imwrite("""output_data/output.jpg""" , self.img )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
cva.imshow("""Output-Image""" , self.img )
cva.imshow("""Input-Image""" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
__lowerCAmelCase : Dict = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
__lowerCAmelCase : Optional[int] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
def get_matched_characters(lowerCamelCase__ , lowerCamelCase__ ) -> str:
lowerCAmelCase__ = []
lowerCAmelCase__ = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
lowerCAmelCase__ = int(max(0 , i - limit ) )
lowerCAmelCase__ = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCamelCase__ )
lowerCAmelCase__ = f"""{_stra[0:_stra.index(lowerCamelCase__ )]} {_stra[_stra.index(lowerCamelCase__ ) + 1:]}"""
return "".join(lowerCamelCase__ )
# matching characters
lowerCAmelCase__ = get_matched_characters(lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = get_matched_characters(lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = len(lowerCamelCase__ )
# transposition
lowerCAmelCase__ = (
len([(ca, ca) for ca, ca in zip(lowerCamelCase__ , lowerCamelCase__ ) if ca != ca] ) // 2
)
if not match_count:
lowerCAmelCase__ = 0.0
else:
lowerCAmelCase__ = (
1
/ 3
* (
match_count / len(lowerCamelCase__ )
+ match_count / len(lowerCamelCase__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
lowerCAmelCase__ = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("hello", "world"))
| 709 | """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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : List[str] = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
UpperCamelCase_ : str = "CIDAS/clipseg-rd64-refined"
UpperCamelCase_ : Any = "image_segmenter"
UpperCamelCase_ : Optional[Any] = CLIPSegForImageSegmentation
UpperCamelCase_ : List[str] = ["image", "text"]
UpperCamelCase_ : int = ["image"]
def __init__( self : Tuple , *snake_case__ : str , **snake_case__ : Optional[Any] ):
requires_backends(self , ["""vision"""] )
super().__init__(*snake_case__ , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : "Image" , snake_case__ : str ):
return self.pre_processor(text=[label] , images=[image] , padding=snake_case__ , return_tensors="""pt""" )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Tuple ):
with torch.no_grad():
lowerCAmelCase__ = self.model(**snake_case__ ).logits
return logits
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : List[Any] ):
lowerCAmelCase__ = outputs.cpu().detach().numpy()
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 674 | 0 |
"""simple docstring"""
from math import loga
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(snake_case_ , snake_case_ ):
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()
| 710 | """simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = LayoutLMTokenizer
UpperCamelCase_ : List[Any] = LayoutLMTokenizerFast
UpperCamelCase_ : Dict = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
lowerCAmelCase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ = 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 _SCREAMING_SNAKE_CASE ( self : int , **snake_case__ : Union[str, Any] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Tuple ):
lowerCAmelCase__ = """UNwant\u00E9d,running"""
lowerCAmelCase__ = """unwanted, running"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase__ = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(snake_case__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [7, 4, 5, 10, 8, 9] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
pass
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ = 1000 ):
"""simple docstring"""
lowerCAmelCase__ = 1, 1
lowerCAmelCase__ = []
for i in range(1 , n + 1 ):
lowerCAmelCase__ = prev_numerator + 2 * prev_denominator
lowerCAmelCase__ = prev_numerator + prev_denominator
if len(str(__UpperCamelCase ) ) > len(str(__UpperCamelCase ) ):
result.append(__UpperCamelCase )
lowerCAmelCase__ = numerator
lowerCAmelCase__ = denominator
return len(__UpperCamelCase )
if __name__ == "__main__":
print(F"{solution() = }")
| 711 | """simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase : Any = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
def __init__( self : List[str] , snake_case__ : int = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
lowerCAmelCase__ = primes[group]["""prime"""]
lowerCAmelCase__ = primes[group]["""generator"""]
lowerCAmelCase__ = int(hexlify(urandom(32 ) ) , base=16 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
return hex(self.__private_key )[2:]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = pow(self.generator , self.__private_key , self.prime )
return hex(snake_case__ )[2:]
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(snake_case__ , (self.prime - 1) // 2 , self.prime ) == 1
)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : str ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
if not self.is_valid_public_key(snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , self.__private_key , self.prime )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : int , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(snake_case__ , (prime - 1) // 2 , snake_case__ ) == 1
)
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : str , snake_case__ : str , snake_case__ : int = 14 ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(snake_case__ , snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , snake_case__ , snake_case__ )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__lowerCAmelCase : Any = "<<<<<<< This should probably be modified because it mentions: "
__lowerCAmelCase : Any = "=======\n>>>>>>>\n"
__lowerCAmelCase : str = [
"TextEncoderConfig",
"ByteTextEncoder",
"SubwordTextEncoder",
"encoder_config",
"maybe_build_from_corpus",
"manual_dir",
]
__lowerCAmelCase : Union[str, Any] = [
# (pattern, replacement)
# Order is important here for some replacements
(R"tfds\.core", R"datasets"),
(R"tf\.io\.gfile\.GFile", R"open"),
(R"tf\.([\w\d]+)", R"datasets.Value(\'\1\')"),
(R"tfds\.features\.Text\(\)", R"datasets.Value(\'string\')"),
(R"tfds\.features\.Text\(", R"datasets.Value(\'string\'),"),
(R"features\s*=\s*tfds.features.FeaturesDict\(", R"features=datasets.Features("),
(R"tfds\.features\.FeaturesDict\(", R"dict("),
(R"The TensorFlow Datasets Authors", R"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
(R"tfds\.", R"datasets."),
(R"dl_manager\.manual_dir", R"self.config.data_dir"),
(R"self\.builder_config", R"self.config"),
]
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return ConvertCommand(args.tfds_path , args.datasets_directory )
class a_ ( lowerCAmelCase__ ):
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : Tuple ):
lowerCAmelCase__ = parser.add_parser(
"""convert""" , help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.""" , )
train_parser.add_argument(
"""--tfds_path""" , type=_lowerCamelCase , required=_lowerCamelCase , help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.""" , )
train_parser.add_argument(
"""--datasets_directory""" , type=_lowerCamelCase , required=_lowerCamelCase , help="""Path to the HuggingFace Datasets folder.""" )
train_parser.set_defaults(func=_lowerCamelCase )
def __init__( self : Dict , snake_case__ : Optional[int] , snake_case__ : str , *snake_case__ : Any ):
lowerCAmelCase__ = get_logger("""datasets-cli/converting""" )
lowerCAmelCase__ = tfds_path
lowerCAmelCase__ = datasets_directory
def _SCREAMING_SNAKE_CASE ( self : Dict ):
if os.path.isdir(self._tfds_path ):
lowerCAmelCase__ = os.path.abspath(self._tfds_path )
elif os.path.isfile(self._tfds_path ):
lowerCAmelCase__ = os.path.dirname(self._tfds_path )
else:
raise ValueError("""--tfds_path is neither a directory nor a file. Please check path.""" )
lowerCAmelCase__ = os.path.abspath(self._datasets_directory )
self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}""" )
lowerCAmelCase__ = []
lowerCAmelCase__ = []
lowerCAmelCase__ = {}
if os.path.isdir(self._tfds_path ):
lowerCAmelCase__ = os.listdir(_lowerCamelCase )
else:
lowerCAmelCase__ = [os.path.basename(self._tfds_path )]
for f_name in file_names:
self._logger.info(F"""Looking at file {f_name}""" )
lowerCAmelCase__ = os.path.join(_lowerCamelCase , _lowerCamelCase )
lowerCAmelCase__ = os.path.join(_lowerCamelCase , _lowerCamelCase )
if not os.path.isfile(_lowerCamelCase ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("""Skipping file""" )
continue
with open(_lowerCamelCase , encoding="""utf-8""" ) as f:
lowerCAmelCase__ = f.readlines()
lowerCAmelCase__ = []
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = []
for line in lines:
lowerCAmelCase__ = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
lowerCAmelCase__ = """import datasets\n"""
elif "import tensorflow" in out_line:
# order is important here
lowerCAmelCase__ = """"""
continue
elif "from absl import logging" in out_line:
lowerCAmelCase__ = """from datasets import logging\n"""
elif "getLogger" in out_line:
lowerCAmelCase__ = out_line.replace("""getLogger""" , """get_logger""" )
elif any(expression in out_line for expression in TO_HIGHLIGHT ):
lowerCAmelCase__ = True
lowerCAmelCase__ = list(filter(lambda snake_case__ : e in out_line , _lowerCamelCase ) )
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_lowerCamelCase ) + """\n""" )
out_lines.append(_lowerCamelCase )
out_lines.append(_lowerCamelCase )
continue
else:
for pattern, replacement in TO_CONVERT:
lowerCAmelCase__ = re.sub(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
lowerCAmelCase__ = re.match(R"""from\stensorflow_datasets.*import\s([^\.\r\n]+)""" , _lowerCamelCase )
tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(""",""" ) )
lowerCAmelCase__ = """from . import """ + match.group(1 )
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(F"""Error converting {out_line.strip()}""" )
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
lowerCAmelCase__ = True
out_lines.append(_lowerCamelCase )
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
lowerCAmelCase__ = f_name.replace(""".py""" , """""" )
lowerCAmelCase__ = os.path.join(_lowerCamelCase , _lowerCamelCase )
lowerCAmelCase__ = os.path.join(_lowerCamelCase , _lowerCamelCase )
os.makedirs(_lowerCamelCase , exist_ok=_lowerCamelCase )
self._logger.info(F"""Adding directory {output_dir}""" )
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports} )
else:
# Utilities will be moved at the end
utils_files.append(_lowerCamelCase )
if needs_manual_update:
with_manual_update.append(_lowerCamelCase )
with open(_lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.writelines(_lowerCamelCase )
self._logger.info(F"""Converted in {output_file}""" )
for utils_file in utils_files:
try:
lowerCAmelCase__ = os.path.basename(_lowerCamelCase )
lowerCAmelCase__ = imports_to_builder_map[f_name.replace(""".py""" , """""" )]
self._logger.info(F"""Moving {dest_folder} to {utils_file}""" )
shutil.copy(_lowerCamelCase , _lowerCamelCase )
except KeyError:
self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually.""" )
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.""" )
| 712 | """simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
lowerCAmelCase__ = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = weights[0][0][0]
lowerCAmelCase__ = np.asarray(layer_norm_a[0] )
lowerCAmelCase__ = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# lsh weights + output
lowerCAmelCase__ = weights[0][1]
if len(lowerCamelCase__ ) < 4:
set_layer_weights_in_torch_lsh(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
else:
set_layer_weights_in_torch_local(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
# intermediate weighs
lowerCAmelCase__ = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowerCamelCase__ ) == 4:
lowerCAmelCase__ = intermediate_weights[2]
# layernorm 2
lowerCAmelCase__ = np.asarray(intermediate_weights[0][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# intermediate dense
lowerCAmelCase__ = np.asarray(intermediate_weights[1][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
# intermediate out
lowerCAmelCase__ = np.asarray(intermediate_weights[4][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = torch_model.reformer
# word embeds
lowerCAmelCase__ = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCamelCase__ ) , )
if isinstance(weights[3] , lowerCamelCase__ ):
lowerCAmelCase__ = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowerCAmelCase__ = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f"""{position_embeddings[emb_idx]} emb does not match"""
lowerCAmelCase__ = nn.Parameter(torch.tensor(lowerCamelCase__ ) )
lowerCAmelCase__ = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowerCamelCase__ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowerCAmelCase__ = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# output layer norm
lowerCAmelCase__ = np.asarray(weights[7][0] )
lowerCAmelCase__ = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# output embeddings
lowerCAmelCase__ = np.asarray(weights[9][0] )
lowerCAmelCase__ = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = ReformerConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = ReformerModelWithLMHead(lowerCamelCase__ )
with open(lowerCamelCase__ , """rb""" ) as f:
lowerCAmelCase__ = pickle.load(lowerCamelCase__ )["""weights"""]
set_model_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , config.hidden_size )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : Tuple = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
"ConditionalDetrOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Any = ["ConditionalDetrFeatureExtractor"]
__lowerCAmelCase : Union[str, Any] = ["ConditionalDetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : str = [
"CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 713 | """simple docstring"""
import os
from math import logaa
def _UpperCAmelCase ( lowerCamelCase__ = "base_exp.txt" ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) ):
lowerCAmelCase__ , lowerCAmelCase__ = list(map(lowerCamelCase__ , line.split(""",""" ) ) )
if x * logaa(lowerCamelCase__ ) > largest:
lowerCAmelCase__ = x * logaa(lowerCamelCase__ )
lowerCAmelCase__ = i + 1
return result
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCAmelCase : Optional[Any] = "."
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCAmelCase : Union[str, Any] = [
"Assert",
"AssignVariableOp",
"EmptyTensorList",
"MergeV2Checkpoints",
"ReadVariableOp",
"ResourceGather",
"RestoreV2",
"SaveV2",
"ShardedFilename",
"StatefulPartitionedCall",
"StaticRegexFullMatch",
"VarHandleOp",
]
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = SavedModel()
lowerCAmelCase__ = []
with open(os.path.join(a__ , """utils""" , """tf_ops""" , """onnx.json""" ) ) as f:
lowerCAmelCase__ = json.load(a__ )["""opsets"""]
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(a__ )] )
with open(a__ , """rb""" ) as f:
saved_model.ParseFromString(f.read() )
lowerCAmelCase__ = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
lowerCAmelCase__ = sorted(a__ )
lowerCAmelCase__ = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(a__ )
if strict and len(a__ ) > 0:
raise Exception(f"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops )
elif len(a__ ) > 0:
print(f"""Found the following incompatible ops for the opset {opset}:""" )
print(*a__ , sep="""\n""" )
else:
print(f"""The saved model {saved_model_path} can properly be converted with ONNX.""" )
if __name__ == "__main__":
__lowerCAmelCase : str = argparse.ArgumentParser()
parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
parser.add_argument(
"--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
)
parser.add_argument(
"--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
)
parser.add_argument(
"--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
)
__lowerCAmelCase : int = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 714 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
while b:
lowerCAmelCase__ , lowerCAmelCase__ = b, a % b
return a
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(lowerCamelCase__ , a % b )
def _UpperCAmelCase ( ):
"""simple docstring"""
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 674 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
__lowerCAmelCase : List[str] = get_tests_dir("fixtures")
__lowerCAmelCase : str = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
__lowerCAmelCase : Union[str, Any] = get_tests_dir("fixtures/dummy-config.json")
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(__A , __A )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = WavaVecaConfig()
# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(__A ).to_dict()
config_dict.pop("""feature_extractor_type""" )
lowerCAmelCase__ = WavaVecaFeatureExtractor(**__A )
# save in new folder
model_config.save_pretrained(__A )
config.save_pretrained(__A )
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(__A )
# make sure private variable is not incorrectly saved
lowerCAmelCase__ = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(__A , __A )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(__A )
self.assertIsInstance(__A , __A )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
with self.assertRaisesRegex(
__A , """bert-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained("""bert-base""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
with self.assertRaisesRegex(
__A , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(__A , revision="""aaaaaa""" )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
with self.assertRaisesRegex(
__A , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
with self.assertRaises(__A ):
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__A ):
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
# Test feature extractor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(__A )
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(__A , trust_remote_code=__A )
self.assertEqual(reloaded_feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
try:
AutoConfig.register("""custom""" , __A )
AutoFeatureExtractor.register(__A , __A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__A ):
AutoFeatureExtractor.register(__A , __A )
# Now that the config is registered, it can be used as any other config with the auto-API
lowerCAmelCase__ = CustomFeatureExtractor.from_pretrained(__A )
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(__A )
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(__A )
self.assertIsInstance(__A , __A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self : Any ):
class a_ ( UpperCamelCase__ ):
UpperCamelCase_ : Dict = True
try:
AutoConfig.register("""custom""" , __A )
AutoFeatureExtractor.register(__A , __A )
# If remote code is not set, the default is to use local
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(feature_extractor.is_local )
# If remote code is disabled, we load the local one.
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(feature_extractor.is_local )
# If remote is enabled, we load from the Hub
lowerCAmelCase__ = AutoFeatureExtractor.from_pretrained(
"""hf-internal-testing/test_dynamic_feature_extractor""" , trust_remote_code=__A )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
self.assertTrue(not hasattr(__A , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
| 715 | """simple docstring"""
import os
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCamelCase__ ) )
lowerCAmelCase__ = os.path.join(lowerCamelCase__ , """triangle.txt""" )
with open(lowerCamelCase__ ) as f:
lowerCAmelCase__ = f.readlines()
lowerCAmelCase__ = []
for line in triangle:
lowerCAmelCase__ = []
for number in line.strip().split(""" """ ):
numbers_from_line.append(int(lowerCamelCase__ ) )
a.append(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
for j in range(len(a[i] ) ):
lowerCAmelCase__ = a[i - 1][j] if j != len(a[i - 1] ) else 0
lowerCAmelCase__ = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowerCamelCase__ , lowerCamelCase__ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
__lowerCAmelCase : int = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 716 | """simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase : Union[str, Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
__lowerCAmelCase : Optional[int] = json.load(f)
@require_torch
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Dict ):
return FSMTTokenizer.from_pretrained(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : Any ):
lowerCAmelCase__ = FSMTForConditionalGeneration.from_pretrained(snake_case__ ).to(snake_case__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Any , snake_case__ : int ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
lowerCAmelCase__ = F"""facebook/wmt19-{pair}"""
lowerCAmelCase__ = self.get_tokenizer(snake_case__ )
lowerCAmelCase__ = self.get_model(snake_case__ )
lowerCAmelCase__ = bleu_data[pair]["""src"""]
lowerCAmelCase__ = bleu_data[pair]["""tgt"""]
lowerCAmelCase__ = tokenizer(snake_case__ , return_tensors="""pt""" , truncation=snake_case__ , padding="""longest""" ).to(snake_case__ )
lowerCAmelCase__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
lowerCAmelCase__ = tokenizer.batch_decode(
snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
lowerCAmelCase__ = calculate_bleu(snake_case__ , snake_case__ )
print(snake_case__ )
self.assertGreaterEqual(scores["""bleu"""] , snake_case__ )
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowerCAmelCase : List[Any] = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}
class a_ ( UpperCamelCase_ ):
UpperCamelCase_ : Any = "ernie_m"
UpperCamelCase_ : Optional[int] = {"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self : Optional[int] , snake_case__ : Union[str, Any] = 250002 , snake_case__ : int = 768 , snake_case__ : Optional[Any] = 12 , snake_case__ : Any = 12 , snake_case__ : str = 3072 , snake_case__ : int = "gelu" , snake_case__ : List[str] = 0.1 , snake_case__ : str = 0.1 , snake_case__ : Union[str, Any] = 514 , snake_case__ : Any = 0.02 , snake_case__ : int = 1 , snake_case__ : int = 1E-05 , snake_case__ : int=None , snake_case__ : Dict=False , snake_case__ : Dict=0.0 , **snake_case__ : int , ):
super().__init__(pad_token_id=__a , **__a )
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = max_position_embeddings
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = layer_norm_eps
lowerCAmelCase__ = classifier_dropout
lowerCAmelCase__ = is_decoder
lowerCAmelCase__ = act_dropout
| 717 | """simple docstring"""
import pprint
import requests
__lowerCAmelCase : Union[str, Any] = "https://zenquotes.io/api"
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = random_quotes()
pprint.pprint(response)
| 674 | 0 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a_ :
def __init__( self : Union[str, Any] , snake_case__ : int , snake_case__ : Any=13 , snake_case__ : Optional[int]=30 , snake_case__ : Union[str, Any]=2 , snake_case__ : str=3 , snake_case__ : Tuple=True , snake_case__ : Dict=True , snake_case__ : int=32 , snake_case__ : Optional[int]=5 , snake_case__ : Optional[int]=4 , snake_case__ : str=37 , snake_case__ : Any="gelu" , snake_case__ : Union[str, Any]=0.1 , snake_case__ : List[str]=0.1 , snake_case__ : Optional[int]=10 , snake_case__ : Tuple=0.02 , snake_case__ : str=3 , snake_case__ : Union[str, Any]=0.6 , snake_case__ : Dict=None , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
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__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = mask_ratio
lowerCAmelCase__ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowerCAmelCase__ = (image_size // patch_size) ** 2
lowerCAmelCase__ = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : str ):
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__A , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : List[str] , snake_case__ : List[str] , snake_case__ : List[str] ):
lowerCAmelCase__ = ViTMAEModel(config=__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : int ):
lowerCAmelCase__ = ViTMAEForPreTraining(__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = model(__A )
lowerCAmelCase__ = (self.image_size // self.patch_size) ** 2
lowerCAmelCase__ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = ViTMAEForPreTraining(__A )
model.to(__A )
model.eval()
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(__A )
lowerCAmelCase__ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
UpperCamelCase_ : Optional[int] = {"feature-extraction": ViTMAEModel} if is_torch_available() else {}
UpperCamelCase_ : str = False
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : Tuple = False
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = ViTMAEModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=__A , has_text_modality=__A , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
pass
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(__A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__A , nn.Linear ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(__A )
lowerCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __A )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : List[Any] , snake_case__ : Tuple , snake_case__ : Optional[int] ):
# make masks reproducible
np.random.seed(2 )
lowerCAmelCase__ = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
lowerCAmelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowerCAmelCase__ = torch.from_numpy(__A )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowerCAmelCase__ = pt_noise
super().check_pt_tf_models(__A , __A , __A )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(__A )
model.to(__A )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__A , __A ) )
lowerCAmelCase__ = outputs[0].cpu().numpy()
lowerCAmelCase__ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A )
lowerCAmelCase__ = model_class.from_pretrained(__A )
model.to(__A )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__A , __A ) )
# Make sure we don't have nans
lowerCAmelCase__ = after_outputs[0].cpu().numpy()
lowerCAmelCase__ = 0
lowerCAmelCase__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__A , 1E-5 )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
pass
@slow
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = ViTMAEModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def _SCREAMING_SNAKE_CASE ( self : int ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
lowerCAmelCase__ = ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" ).to(__A )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=__A , return_tensors="""pt""" ).to(__A )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowerCAmelCase__ = ViTMAEConfig()
lowerCAmelCase__ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
lowerCAmelCase__ = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
lowerCAmelCase__ = model(**__A , noise=torch.from_numpy(__A ).to(device=__A ) )
# verify the logits
lowerCAmelCase__ = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , __A )
lowerCAmelCase__ = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(__A ) , atol=1E-4 ) )
| 718 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ ).to_dict()
config_dict.pop("""image_processor_type""" )
lowerCAmelCase__ = CLIPImageProcessor(**snake_case__ )
# save in new folder
model_config.save_pretrained(snake_case__ )
config.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
# make sure private variable is not incorrectly saved
lowerCAmelCase__ = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with self.assertRaisesRegex(
snake_case__ , """clip-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""clip-base""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
with self.assertRaisesRegex(
snake_case__ , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , revision="""aaaaaa""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
with self.assertRaisesRegex(
snake_case__ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , trust_remote_code=snake_case__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case__ ):
AutoImageProcessor.register(snake_case__ , snake_case__ )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = CustomImageProcessor.from_pretrained(snake_case__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Tuple = True
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# If remote code is not set, the default is to use local
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(snake_case__ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=_lowercase , )
assert hasattr(self , """env""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : Dict ):
lowerCAmelCase__ = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase__ = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase__ = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase__ = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=_lowercase , instance_type=self.instance_type , debugger_hook_config=_lowercase , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=_lowercase , py_version="""py36""" , )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Tuple ):
TrainingJobAnalytics(_lowercase ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : Optional[int] ):
lowerCAmelCase__ = self.create_estimator(_lowercase )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , _lowercase )
| 719 | """simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a_ :
def __init__( self : Optional[int] , snake_case__ : List[Any]=2 , snake_case__ : Any=3 , snake_case__ : Union[str, Any]=64 , snake_case__ : Any=None ):
lowerCAmelCase__ = np.random.default_rng(snake_case__ )
lowerCAmelCase__ = length
lowerCAmelCase__ = rng.normal(size=(length,) ).astype(np.floataa )
lowerCAmelCase__ = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Optional[Any] ):
return self.length
def __getitem__( self : List[str] , snake_case__ : Optional[int] ):
return {"x": self.x[i], "y": self.y[i]}
class a_ ( torch.nn.Module ):
def __init__( self : List[str] , snake_case__ : str=0 , snake_case__ : Dict=0 , snake_case__ : Any=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Any=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a[0] + self.b[0]
class a_ ( torch.nn.Module ):
def __init__( self : Any , snake_case__ : Union[str, Any]=0 , snake_case__ : Union[str, Any]=0 , snake_case__ : List[Any]=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any]=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a + self.b
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase__ = {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
lowerCAmelCase__ = load_dataset("""csv""" , data_files=lowerCamelCase__ )
lowerCAmelCase__ = datasets["""train"""].unique("""label""" )
lowerCAmelCase__ = {v: i for i, v in enumerate(lowerCamelCase__ )}
def tokenize_function(lowerCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase__ = tokenizer(
examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
if "label" in examples:
lowerCAmelCase__ = [label_to_id[l] for l in examples["""label"""]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase__ = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""sentence1""", """sentence2""", """label"""] , )
def collate_fn(lowerCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase__ = DataLoader(tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=2 )
lowerCAmelCase__ = DataLoader(tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 674 | 0 |
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__lowerCAmelCase : Dict = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__lowerCAmelCase : Optional[Any] = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F"{len(upper_files)} files contain uppercase characters:")
print("\n".join(upper_files) + "\n")
__lowerCAmelCase : Tuple = [file for file in filepaths if ' ' in file]
if space_files:
print(F"{len(space_files)} files contain space characters:")
print("\n".join(space_files) + "\n")
__lowerCAmelCase : Any = [file for file in filepaths if '-' in file]
if hyphen_files:
print(F"{len(hyphen_files)} files contain hyphen characters:")
print("\n".join(hyphen_files) + "\n")
__lowerCAmelCase : List[str] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"{len(nodir_files)} files are not in a directory:")
print("\n".join(nodir_files) + "\n")
__lowerCAmelCase : Any = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 720 | """simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = MobileBertConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = MobileBertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
lowerCAmelCase__ = load_tf_weights_in_mobilebert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = 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(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT 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."
)
__lowerCAmelCase : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class a_ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase_ : Optional[int] = ''
UpperCamelCase_ : str = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
UpperCamelCase_ : str = None # compression type in fsspec. ex: "gzip"
UpperCamelCase_ : str = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : str , snake_case__ : str = "" , snake_case__ : Optional[int] = None , snake_case__ : Optional[Any] = None , **snake_case__ : str ):
super().__init__(self , **_a )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
lowerCAmelCase__ = fsspec.open(
_a , mode="""rb""" , protocol=_a , compression=self.compression , client_kwargs={
"""requote_redirect_url""": False, # see https://github.com/huggingface/datasets/pull/5459
"""trust_env""": True, # Enable reading proxy env variables.
**(target_options or {}).pop("""client_kwargs""" , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
lowerCAmelCase__ = os.path.basename(self.file.path.split("""::""" )[0] )
lowerCAmelCase__ = (
self.compressed_name[: self.compressed_name.rindex(""".""" )]
if """.""" in self.compressed_name
else self.compressed_name
)
lowerCAmelCase__ = None
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Tuple , snake_case__ : int ):
# compressed file paths are always relative to the archive root
return super()._strip_protocol(_a ).lstrip("""/""" )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
if self.dir_cache is None:
lowerCAmelCase__ = {**self.file.fs.info(self.file.path ), """name""": self.uncompressed_name}
lowerCAmelCase__ = {f["""name"""]: f}
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : Optional[int] ):
return self.file.open().read()
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : List[Any] , snake_case__ : Any = "rb" , snake_case__ : Any=None , snake_case__ : Union[str, Any]=True , snake_case__ : Any=None , **snake_case__ : List[str] , ):
lowerCAmelCase__ = self._strip_protocol(_a )
if mode != "rb":
raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'""" )
return self.file.open()
class a_ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase_ : int = 'bz2'
UpperCamelCase_ : Optional[Any] = 'bz2'
UpperCamelCase_ : List[Any] = '.bz2'
class a_ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase_ : Tuple = 'gzip'
UpperCamelCase_ : int = 'gzip'
UpperCamelCase_ : str = '.gz'
class a_ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase_ : Union[str, Any] = 'lz4'
UpperCamelCase_ : str = 'lz4'
UpperCamelCase_ : List[str] = '.lz4'
class a_ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase_ : str = 'xz'
UpperCamelCase_ : Optional[int] = 'xz'
UpperCamelCase_ : int = '.xz'
class a_ ( __SCREAMING_SNAKE_CASE ):
UpperCamelCase_ : List[str] = 'zstd'
UpperCamelCase_ : List[str] = 'zstd'
UpperCamelCase_ : Any = '.zst'
def __init__( self : Any , snake_case__ : List[Any] , snake_case__ : Optional[Any] = "rb" , snake_case__ : List[Any] = None , snake_case__ : int = None , snake_case__ : Tuple = DEFAULT_BLOCK_SIZE , **snake_case__ : Union[str, Any] , ):
super().__init__(
fo=_a , mode=_a , target_protocol=_a , target_options=_a , block_size=_a , **_a , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
lowerCAmelCase__ = self.file.__enter__
class a_ :
def __init__( self : int , snake_case__ : List[str] ):
lowerCAmelCase__ = file_
def __enter__( self : Any ):
self._file.__enter__()
return self
def __exit__( self : Optional[Any] , *snake_case__ : Optional[Any] , **snake_case__ : Union[str, Any] ):
self._file.__exit__(*_a , **_a )
def __iter__( self : Any ):
return iter(self._file )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
return next(self._file )
def __getattr__( self : Dict , snake_case__ : Dict ):
return getattr(self._file , _a )
def fixed_enter(*snake_case__ : Optional[int] , **snake_case__ : Optional[int] ):
return WrappedFile(_enter(*_a , **_a ) )
lowerCAmelCase__ = fixed_enter
| 721 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
lowerCAmelCase__ = f"""The input value of [n={number}] has to be > 0"""
raise ValueError(lowerCamelCase__ )
else:
lowerCAmelCase__ = sylvester(number - 1 )
lowerCAmelCase__ = num - 1
lowerCAmelCase__ = num
return lower * upper + 1
if __name__ == "__main__":
print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")
| 674 | 0 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__lowerCAmelCase : List[str] = (
"""This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
warnings.warn(_lowerCamelCase , _lowerCamelCase )
requires_backends(_lowerCamelCase , """sklearn""" )
return (preds == labels).mean()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
warnings.warn(_lowerCamelCase , _lowerCamelCase )
requires_backends(_lowerCamelCase , """sklearn""" )
lowerCAmelCase__ = simple_accuracy(_lowerCamelCase , _lowerCamelCase )
lowerCAmelCase__ = fa_score(y_true=_lowerCamelCase , y_pred=_lowerCamelCase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
warnings.warn(_lowerCamelCase , _lowerCamelCase )
requires_backends(_lowerCamelCase , """sklearn""" )
lowerCAmelCase__ = pearsonr(_lowerCamelCase , _lowerCamelCase )[0]
lowerCAmelCase__ = spearmanr(_lowerCamelCase , _lowerCamelCase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
warnings.warn(_lowerCamelCase , _lowerCamelCase )
requires_backends(_lowerCamelCase , """sklearn""" )
assert len(_lowerCamelCase ) == len(_lowerCamelCase ), f"""Predictions and labels have mismatched lengths {len(_lowerCamelCase )} and {len(_lowerCamelCase )}"""
if task_name == "cola":
return {"mcc": matthews_corrcoef(_lowerCamelCase , _lowerCamelCase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
elif task_name == "mrpc":
return acc_and_fa(_lowerCamelCase , _lowerCamelCase )
elif task_name == "sts-b":
return pearson_and_spearman(_lowerCamelCase , _lowerCamelCase )
elif task_name == "qqp":
return acc_and_fa(_lowerCamelCase , _lowerCamelCase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
elif task_name == "rte":
return {"acc": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
elif task_name == "hans":
return {"acc": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
else:
raise KeyError(_lowerCamelCase )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
warnings.warn(_lowerCamelCase , _lowerCamelCase )
requires_backends(_lowerCamelCase , """sklearn""" )
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError(f"""Predictions and labels have mismatched lengths {len(_lowerCamelCase )} and {len(_lowerCamelCase )}""" )
if task_name == "xnli":
return {"acc": simple_accuracy(_lowerCamelCase , _lowerCamelCase )}
else:
raise KeyError(_lowerCamelCase )
| 700 | """simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : Optional[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Tuple = PegasusTokenizer
UpperCamelCase_ : Any = PegasusTokenizerFast
UpperCamelCase_ : int = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = PegasusTokenizer(snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : Optional[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any] ):
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = """</s>"""
lowerCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """</s>""" )
self.assertEqual(vocab_keys[-1] , """v""" )
self.assertEqual(len(snake_case__ ) , 1103 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
""" </s> <pad> <pad> <pad>"""
)
lowerCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
lowerCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowerCAmelCase__ = """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
lowerCAmelCase__ = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
lowerCAmelCase__ = tokenizer([raw_input_str] , return_tensors=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
lowerCAmelCase__ = """To ensure a smooth flow of bank resolutions."""
lowerCAmelCase__ = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
lowerCAmelCase__ = tokenizer([raw_input_str] , return_tensors=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = ["""This is going to be way too long.""" * 150, """short example"""]
lowerCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
lowerCAmelCase__ = self._large_tokenizer(snake_case__ , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
lowerCAmelCase__ = self._large_tokenizer(
text_target=snake_case__ , max_length=5 , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case__ ) == 2 # input_ids, attention_mask.
@slow
def _SCREAMING_SNAKE_CASE ( self : str ):
# fmt: off
lowerCAmelCase__ = {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=snake_case__ , model_name="""google/bigbird-pegasus-large-arxiv""" , revision="""ba85d0851d708441f91440d509690f1ab6353415""" , )
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = PegasusTokenizer
UpperCamelCase_ : Optional[int] = PegasusTokenizerFast
UpperCamelCase_ : Union[str, Any] = True
UpperCamelCase_ : Optional[int] = True
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = PegasusTokenizer(snake_case__ , offset=0 , mask_token_sent=snake_case__ , mask_token="""[MASK]""" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Dict ):
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : List[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Dict ):
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
""" <pad> <pad> <pad>"""
)
lowerCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
lowerCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
@require_torch
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = ["""This is going to be way too long.""" * 1000, """short example"""]
lowerCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
lowerCAmelCase__ = self._large_tokenizer(snake_case__ , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
lowerCAmelCase__ = self._large_tokenizer(
text_target=snake_case__ , max_length=5 , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case__ ) == 2 # input_ids, attention_mask.
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = (
"""This is an example string that is used to test the original TF implementation against the HF"""
""" implementation"""
)
lowerCAmelCase__ = self._large_tokenizer(snake_case__ ).input_ids
self.assertListEqual(
snake_case__ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1] , )
| 674 | 0 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _UpperCAmelCase ( lowerCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
random.seed(lowerCamelCase__ )
np.random.seed(lowerCamelCase__ )
torch.manual_seed(lowerCamelCase__ )
torch.cuda.manual_seed_all(lowerCamelCase__ )
# ^^ safe to call this function even if cuda is not available
class a_ :
def __init__( self : List[Any] , snake_case__ : Any , snake_case__ : Dict = 0.9999 , snake_case__ : Tuple = 0.0 , snake_case__ : Dict = 0 , snake_case__ : Any = False , snake_case__ : Dict = 1.0 , snake_case__ : Optional[int] = 2 / 3 , snake_case__ : Dict = None , snake_case__ : Tuple = None , **snake_case__ : List[Any] , ):
if isinstance(snake_case__ , torch.nn.Module ):
lowerCAmelCase__ = (
"""Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
"""Please pass the parameters of the module instead."""
)
deprecate(
"""passing a `torch.nn.Module` to `ExponentialMovingAverage`""" , """1.0.0""" , snake_case__ , standard_warn=snake_case__ , )
lowerCAmelCase__ = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
lowerCAmelCase__ = True
if kwargs.get("""max_value""" , snake_case__ ) is not None:
lowerCAmelCase__ = """The `max_value` argument is deprecated. Please use `decay` instead."""
deprecate("""max_value""" , """1.0.0""" , snake_case__ , standard_warn=snake_case__ )
lowerCAmelCase__ = kwargs["""max_value"""]
if kwargs.get("""min_value""" , snake_case__ ) is not None:
lowerCAmelCase__ = """The `min_value` argument is deprecated. Please use `min_decay` instead."""
deprecate("""min_value""" , """1.0.0""" , snake_case__ , standard_warn=snake_case__ )
lowerCAmelCase__ = kwargs["""min_value"""]
lowerCAmelCase__ = list(snake_case__ )
lowerCAmelCase__ = [p.clone().detach() for p in parameters]
if kwargs.get("""device""" , snake_case__ ) is not None:
lowerCAmelCase__ = """The `device` argument is deprecated. Please use `to` instead."""
deprecate("""device""" , """1.0.0""" , snake_case__ , standard_warn=snake_case__ )
self.to(device=kwargs["""device"""] )
lowerCAmelCase__ = None
lowerCAmelCase__ = decay
lowerCAmelCase__ = min_decay
lowerCAmelCase__ = update_after_step
lowerCAmelCase__ = use_ema_warmup
lowerCAmelCase__ = inv_gamma
lowerCAmelCase__ = power
lowerCAmelCase__ = 0
lowerCAmelCase__ = None # set in `step()`
lowerCAmelCase__ = model_cls
lowerCAmelCase__ = model_config
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Any , snake_case__ : int , snake_case__ : Union[str, Any] ):
lowerCAmelCase__ , lowerCAmelCase__ = model_cls.load_config(snake_case__ , return_unused_kwargs=snake_case__ )
lowerCAmelCase__ = model_cls.from_pretrained(snake_case__ )
lowerCAmelCase__ = cls(model.parameters() , model_cls=snake_case__ , model_config=model.config )
ema_model.load_state_dict(snake_case__ )
return ema_model
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : List[str] ):
if self.model_cls is None:
raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__.""" )
if self.model_config is None:
raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__.""" )
lowerCAmelCase__ = self.model_cls.from_config(self.model_config )
lowerCAmelCase__ = self.state_dict()
state_dict.pop("""shadow_params""" , snake_case__ )
model.register_to_config(**snake_case__ )
self.copy_to(model.parameters() )
model.save_pretrained(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Optional[int] ):
lowerCAmelCase__ = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
lowerCAmelCase__ = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
lowerCAmelCase__ = (1 + step) / (10 + step)
lowerCAmelCase__ = min(snake_case__ , self.decay )
# make sure decay is not smaller than min_decay
lowerCAmelCase__ = max(snake_case__ , self.min_decay )
return cur_decay_value
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Union[str, Any] ):
if isinstance(snake_case__ , torch.nn.Module ):
lowerCAmelCase__ = (
"""Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
"""Please pass the parameters of the module instead."""
)
deprecate(
"""passing a `torch.nn.Module` to `ExponentialMovingAverage.step`""" , """1.0.0""" , snake_case__ , standard_warn=snake_case__ , )
lowerCAmelCase__ = parameters.parameters()
lowerCAmelCase__ = list(snake_case__ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
lowerCAmelCase__ = self.get_decay(self.optimization_step )
lowerCAmelCase__ = decay
lowerCAmelCase__ = 1 - decay
lowerCAmelCase__ = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , snake_case__ ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
lowerCAmelCase__ = deepspeed.zero.GatheredParameters(snake_case__ , modifier_rank=snake_case__ )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : Optional[int] ):
lowerCAmelCase__ = list(snake_case__ )
for s_param, param in zip(self.shadow_params , snake_case__ ):
param.data.copy_(s_param.to(param.device ).data )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Optional[Any]=None , snake_case__ : List[str]=None ):
lowerCAmelCase__ = [
p.to(device=snake_case__ , dtype=snake_case__ ) if p.is_floating_point() else p.to(device=snake_case__ )
for p in self.shadow_params
]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : Optional[int] ):
lowerCAmelCase__ = [param.detach().cpu().clone() for param in parameters]
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : List[Any] ):
if self.temp_stored_params is None:
raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`""" )
for c_param, param in zip(self.temp_stored_params , snake_case__ ):
param.data.copy_(c_param.data )
# Better memory-wise.
lowerCAmelCase__ = None
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : int ):
lowerCAmelCase__ = copy.deepcopy(snake_case__ )
lowerCAmelCase__ = state_dict.get("""decay""" , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError("""Decay must be between 0 and 1""" )
lowerCAmelCase__ = state_dict.get("""min_decay""" , self.min_decay )
if not isinstance(self.min_decay , snake_case__ ):
raise ValueError("""Invalid min_decay""" )
lowerCAmelCase__ = state_dict.get("""optimization_step""" , self.optimization_step )
if not isinstance(self.optimization_step , snake_case__ ):
raise ValueError("""Invalid optimization_step""" )
lowerCAmelCase__ = state_dict.get("""update_after_step""" , self.update_after_step )
if not isinstance(self.update_after_step , snake_case__ ):
raise ValueError("""Invalid update_after_step""" )
lowerCAmelCase__ = state_dict.get("""use_ema_warmup""" , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , snake_case__ ):
raise ValueError("""Invalid use_ema_warmup""" )
lowerCAmelCase__ = state_dict.get("""inv_gamma""" , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError("""Invalid inv_gamma""" )
lowerCAmelCase__ = state_dict.get("""power""" , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError("""Invalid power""" )
lowerCAmelCase__ = state_dict.get("""shadow_params""" , snake_case__ )
if shadow_params is not None:
lowerCAmelCase__ = shadow_params
if not isinstance(self.shadow_params , snake_case__ ):
raise ValueError("""shadow_params must be a list""" )
if not all(isinstance(snake_case__ , torch.Tensor ) for p in self.shadow_params ):
raise ValueError("""shadow_params must all be Tensors""" )
| 701 | """simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : int ):
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=snake_case__ , )
assert hasattr(self , """env""" )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Optional[Any] ):
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase__ = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase__ = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase__ = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase__ = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=snake_case__ , instance_type=self.instance_type , debugger_hook_config=snake_case__ , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=snake_case__ , py_version="""py36""" , )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : str ):
TrainingJobAnalytics(snake_case__ ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : List[str] ):
# create estimator
lowerCAmelCase__ = self.create_estimator(snake_case__ )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , snake_case__ )
| 674 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = MobileBertForPreTraining(SCREAMING_SNAKE_CASE_ )
# Load weights from tf checkpoint
lowerCAmelCase__ = load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT 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."
)
__lowerCAmelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 702 | """simple docstring"""
from math import pi, sqrt
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_71.5:
raise OverflowError("""math range error""" )
elif num - int(lowerCamelCase__ ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(lowerCamelCase__ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def _UpperCAmelCase ( ):
"""simple docstring"""
assert gamma(0.5 ) == sqrt(lowerCamelCase__ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__lowerCAmelCase : Dict = 1.0
while num:
__lowerCAmelCase : Any = float(input("Gamma of: "))
print(F"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| 674 | 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 GLPNImageProcessor
class a_ ( unittest.TestCase ):
def __init__( self : List[str] , snake_case__ : str , snake_case__ : List[Any]=7 , snake_case__ : Dict=3 , snake_case__ : Optional[Any]=18 , snake_case__ : List[Any]=30 , snake_case__ : Union[str, Any]=400 , snake_case__ : Tuple=True , snake_case__ : Optional[int]=32 , snake_case__ : Dict=True , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = image_size
lowerCAmelCase__ = min_resolution
lowerCAmelCase__ = max_resolution
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size_divisor
lowerCAmelCase__ = do_rescale
def _SCREAMING_SNAKE_CASE ( self : Dict ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Dict = GLPNImageProcessor if is_vision_available() else None
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = GLPNImageProcessingTester(self )
@property
def _SCREAMING_SNAKE_CASE ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case__ , """do_resize""" ) )
self.assertTrue(hasattr(snake_case__ , """size_divisor""" ) )
self.assertTrue(hasattr(snake_case__ , """resample""" ) )
self.assertTrue(hasattr(snake_case__ , """do_rescale""" ) )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
pass
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ , numpify=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ , torchify=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 703 | """simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a_ :
def __init__( self : Optional[int] , snake_case__ : Optional[Any] , snake_case__ : Any=13 , snake_case__ : int=30 , snake_case__ : int=2 , snake_case__ : Union[str, Any]=3 , snake_case__ : Dict=True , snake_case__ : Optional[int]=True , snake_case__ : List[Any]=32 , snake_case__ : List[str]=2 , snake_case__ : Optional[Any]=4 , snake_case__ : Optional[int]=37 , snake_case__ : Tuple="gelu" , snake_case__ : str=0.1 , snake_case__ : Any=0.1 , snake_case__ : int=10 , snake_case__ : Dict=0.02 , snake_case__ : Union[str, Any]=3 , snake_case__ : str=None , snake_case__ : List[Any]=2 , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
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__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = scope
lowerCAmelCase__ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowerCAmelCase__ = (image_size // patch_size) ** 2
lowerCAmelCase__ = num_patches + 2
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
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=snake_case__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : int , snake_case__ : Optional[Any] , snake_case__ : List[str] ):
lowerCAmelCase__ = TFDeiTModel(config=snake_case__ )
lowerCAmelCase__ = model(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 , snake_case__ : Dict , snake_case__ : Dict , snake_case__ : Dict ):
lowerCAmelCase__ = TFDeiTForMaskedImageModeling(config=snake_case__ )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = TFDeiTForMaskedImageModeling(snake_case__ )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Union[str, Any] , snake_case__ : Any , snake_case__ : Tuple ):
lowerCAmelCase__ = self.type_sequence_label_size
lowerCAmelCase__ = TFDeiTForImageClassification(snake_case__ )
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = TFDeiTForImageClassification(snake_case__ )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class a_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Optional[Any] = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : Any = (
{
"feature-extraction": TFDeiTModel,
"image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[int] = False
UpperCamelCase_ : int = False
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = TFDeiTModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
lowerCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , tf.keras.layers.Dense ) )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : Union[str, Any] , snake_case__ : int , snake_case__ : Union[str, Any]=False ):
lowerCAmelCase__ = super()._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def _SCREAMING_SNAKE_CASE ( self : Any ):
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = TFDeiTModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Any ):
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=snake_case__ , return_tensors="""tf""" )
# forward pass
lowerCAmelCase__ = model(**snake_case__ )
# verify the logits
lowerCAmelCase__ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case__ )
lowerCAmelCase__ = tf.constant([-1.0266, 0.1912, -1.2861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , snake_case__ , atol=1E-4 ) )
| 674 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : List[Any] = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Union[str, Any] = [
"TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrajectoryTransformerModel",
"TrajectoryTransformerPreTrainedModel",
"load_tf_weights_in_trajectory_transformer",
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 704 | """simple docstring"""
from __future__ import annotations
from math import gcd
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 2 , lowerCamelCase__ = 1 , lowerCamelCase__ = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
return (pow(lowerCamelCase__ , 2 ) + step) % modulus
for _ in range(lowerCamelCase__ ):
# These track the position within the cycle detection logic.
lowerCAmelCase__ = seed
lowerCAmelCase__ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowerCAmelCase__ = gcd(hare - tortoise , lowerCamelCase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowerCAmelCase__ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
__lowerCAmelCase : List[str] = parser.parse_args()
__lowerCAmelCase : Dict = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"{args.num} is probably prime")
else:
__lowerCAmelCase : List[str] = args.num // divisor
print(F"{args.num} = {divisor} * {quotient}")
| 674 | 0 |
"""simple docstring"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__lowerCAmelCase : int = logging.getLogger(__name__)
__lowerCAmelCase : Optional[Any] = "Hello world! cécé herlolip"
__lowerCAmelCase : Union[str, Any] = namedtuple(
"BertAbsConfig",
[
"temp_dir",
"large",
"use_bert_emb",
"finetune_bert",
"encoder",
"share_emb",
"max_pos",
"enc_layers",
"enc_hidden_size",
"enc_heads",
"enc_ff_size",
"enc_dropout",
"dec_layers",
"dec_hidden_size",
"dec_heads",
"dec_ff_size",
"dec_dropout",
],
)
def _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowerCAmelCase__ = BertAbsConfig(
temp_dir=""".""" , finetune_bert=__snake_case , large=__snake_case , share_emb=__snake_case , use_bert_emb=__snake_case , encoder="""bert""" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
lowerCAmelCase__ = torch.load(__snake_case , lambda lowerCAmelCase_ , lowerCAmelCase_ : storage )
lowerCAmelCase__ = AbsSummarizer(__snake_case , torch.device("""cpu""" ) , __snake_case )
original.eval()
lowerCAmelCase__ = BertAbsSummarizer(__snake_case , torch.device("""cpu""" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("""convert the model""" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("""Make sure that the models\' outputs are identical""" )
lowerCAmelCase__ = BertTokenizer.from_pretrained("""bert-base-uncased""" )
# prepare the model inputs
lowerCAmelCase__ = tokenizer.encode("""This is sample éàalj\'-.""" )
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__snake_case )) )
lowerCAmelCase__ = torch.tensor(__snake_case ).unsqueeze(0 )
lowerCAmelCase__ = tokenizer.encode("""This is sample 3 éàalj\'-.""" )
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__snake_case )) )
lowerCAmelCase__ = torch.tensor(__snake_case ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
lowerCAmelCase__ = encoder_input_ids
lowerCAmelCase__ = decoder_input_ids
lowerCAmelCase__ = lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = lowerCAmelCase__ = None
lowerCAmelCase__ = lowerCAmelCase__ = None
lowerCAmelCase__ = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
lowerCAmelCase__ = original(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case )[0]
lowerCAmelCase__ = original.generator(__snake_case )
lowerCAmelCase__ = new_model(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )[0]
lowerCAmelCase__ = new_model.generator(__snake_case )
lowerCAmelCase__ = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(__snake_case ) )
lowerCAmelCase__ = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("""Maximum absolute difference beween weights: {:.2f}""".format(__snake_case ) )
lowerCAmelCase__ = torch.allclose(__snake_case , __snake_case , atol=1e-3 )
if are_identical:
logging.info("""all weights are equal up to 1e-3""" )
else:
raise ValueError("""the weights are different. The new model is likely different from the original one.""" )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("""saving the model\'s state dictionary""" )
torch.save(
new_model.state_dict() , """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin""" )
if __name__ == "__main__":
__lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
"--bertabs_checkpoint_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch dump.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model.",
)
__lowerCAmelCase : List[str] = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 705 | """simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = TapasConfig.from_json_file(lowerCamelCase__ )
# set absolute/relative position embeddings parameter
lowerCAmelCase__ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "WTQ":
# run_task_main.py hparams
lowerCAmelCase__ = 4
lowerCAmelCase__ = True
# hparam_utils.py hparams
lowerCAmelCase__ = 0.66_46_94
lowerCAmelCase__ = 0.20_79_51
lowerCAmelCase__ = 0.12_11_94
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = 0.0_35_25_13
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
lowerCAmelCase__ = 4
lowerCAmelCase__ = False
# hparam_utils.py hparams
lowerCAmelCase__ = 36.45_19
lowerCAmelCase__ = 0.90_34_21
lowerCAmelCase__ = 2_22.0_88
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = 0.76_31_41
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "TABFACT":
lowerCAmelCase__ = TapasForSequenceClassification(config=lowerCamelCase__ )
elif task == "MLM":
lowerCAmelCase__ = TapasForMaskedLM(config=lowerCamelCase__ )
elif task == "INTERMEDIATE_PRETRAINING":
lowerCAmelCase__ = TapasModel(config=lowerCamelCase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowerCamelCase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
lowerCAmelCase__ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" , model_max_length=512 )
tokenizer.save_pretrained(lowerCamelCase__ )
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
)
parser.add_argument(
"--reset_position_index_per_cell",
default=False,
action="store_true",
help="Whether to use relative position embeddings or not. Defaults to True.",
)
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--tapas_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained TAPAS 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."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 674 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__lowerCAmelCase : Tuple = {
'''cola''': 2,
'''mnli''': 3,
'''mrpc''': 2,
'''sst-2''': 2,
'''sts-b''': 1,
'''qqp''': 2,
'''qnli''': 2,
'''rte''': 2,
'''wnli''': 2,
}
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
lowerCAmelCase__ = XLNetConfig.from_json_file(lowerCAmelCase__ )
lowerCAmelCase__ = finetuning_task.lower() if finetuning_task is not None else """"""
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(f"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}""" )
lowerCAmelCase__ = finetuning_task
lowerCAmelCase__ = GLUE_TASKS_NUM_LABELS[finetuning_task]
lowerCAmelCase__ = XLNetForSequenceClassification(lowerCAmelCase__ )
elif "squad" in finetuning_task:
lowerCAmelCase__ = finetuning_task
lowerCAmelCase__ = XLNetForQuestionAnswering(lowerCAmelCase__ )
else:
lowerCAmelCase__ = XLNetLMHeadModel(lowerCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Save pytorch-model
lowerCAmelCase__ = os.path.join(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase__ = os.path.join(lowerCAmelCase__ , lowerCAmelCase__ )
print(f"""Save PyTorch model to {os.path.abspath(lowerCAmelCase__ )}""" )
torch.save(model.state_dict() , lowerCAmelCase__ )
print(f"""Save configuration file to {os.path.abspath(lowerCAmelCase__ )}""" )
with open(lowerCAmelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__lowerCAmelCase : Dict = 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(
"--xlnet_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained XLNet model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to store the PyTorch model or dataset/vocab.",
)
parser.add_argument(
"--finetuning_task",
default=None,
type=str,
help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
)
__lowerCAmelCase : int = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 706 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ = 50 ):
"""simple docstring"""
lowerCAmelCase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F"{solution() = }")
| 674 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__lowerCAmelCase : str = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n"
__lowerCAmelCase : Dict = "\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\n(https://arxiv.org/abs/2107.03374).\n"
__lowerCAmelCase : List[str] = "\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric(\"code_eval\")\n >>> test_cases = [\"assert add(2,3)==5\"]\n >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n"
__lowerCAmelCase : Optional[Any] = "\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\n\n################################################################################\\n"
__lowerCAmelCase : Any = "The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE."
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" ) ),
"""references""": datasets.Value("""string""" ),
} ) , homepage="""https://github.com/openai/human-eval""" , codebase_urls=["""https://github.com/openai/human-eval"""] , reference_urls=["""https://github.com/openai/human-eval"""] , license=_LICENSE , )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , snake_case__ : List[str] , snake_case__ : List[str] , snake_case__ : Union[str, Any]=[1, 10, 100] , snake_case__ : List[Any]=4 , snake_case__ : List[Any]=3.0 ):
if os.getenv("""HF_ALLOW_CODE_EVAL""" , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError("""This metric is currently not supported on Windows.""" )
with ThreadPoolExecutor(max_workers=a_ ) as executor:
lowerCAmelCase__ = []
lowerCAmelCase__ = Counter()
lowerCAmelCase__ = 0
lowerCAmelCase__ = defaultdict(a_ )
for task_id, (candidates, test_case) in enumerate(zip(a_ , a_ ) ):
for candidate in candidates:
lowerCAmelCase__ = candidate + "\n" + test_case
lowerCAmelCase__ = (test_program, timeout, task_id, completion_id[task_id])
lowerCAmelCase__ = executor.submit(a_ , *a_ )
futures.append(a_ )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(a_ ):
lowerCAmelCase__ = future.result()
results[result["task_id"]].append((result["""completion_id"""], result) )
lowerCAmelCase__ = [], []
for result in results.values():
result.sort()
lowerCAmelCase__ = [r[1]["passed"] for r in result]
total.append(len(a_ ) )
correct.append(sum(a_ ) )
lowerCAmelCase__ = np.array(a_ )
lowerCAmelCase__ = np.array(a_ )
lowerCAmelCase__ = k
lowerCAmelCase__ = {F"""pass@{k}""": estimate_pass_at_k(a_ , a_ , a_ ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
def estimator(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
lowerCAmelCase__ = itertools.repeat(lowerCAmelCase_ , len(lowerCAmelCase_ ) )
else:
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )
lowerCAmelCase__ = iter(lowerCAmelCase_ )
return np.array([estimator(int(lowerCAmelCase_ ) , int(lowerCAmelCase_ ) , lowerCAmelCase_ ) for n, c in zip(lowerCAmelCase_ , lowerCAmelCase_ )] )
| 707 | """simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse("0.8.3"):
raise Exception("requires gluonnlp == 0.8.3")
if version.parse(mx.__version__) != version.parse("1.5.0"):
raise Exception("requires mxnet == 1.5.0")
logging.set_verbosity_info()
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Any = "The Nymphenburg Palace is a beautiful palace in Munich!"
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {
"""attention_cell""": """multi_head""",
"""num_layers""": 4,
"""units""": 1024,
"""hidden_size""": 768,
"""max_length""": 512,
"""num_heads""": 8,
"""scaled""": True,
"""dropout""": 0.1,
"""use_residual""": True,
"""embed_size""": 1024,
"""embed_dropout""": 0.1,
"""word_embed""": None,
"""layer_norm_eps""": 1e-5,
"""token_type_vocab_size""": 2,
}
lowerCAmelCase__ = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
lowerCAmelCase__ = BERTEncoder(
attention_cell=predefined_args["""attention_cell"""] , num_layers=predefined_args["""num_layers"""] , units=predefined_args["""units"""] , hidden_size=predefined_args["""hidden_size"""] , max_length=predefined_args["""max_length"""] , num_heads=predefined_args["""num_heads"""] , scaled=predefined_args["""scaled"""] , dropout=predefined_args["""dropout"""] , output_attention=lowerCamelCase__ , output_all_encodings=lowerCamelCase__ , use_residual=predefined_args["""use_residual"""] , activation=predefined_args.get("""activation""" , """gelu""" ) , layer_norm_eps=predefined_args.get("""layer_norm_eps""" , lowerCamelCase__ ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
lowerCAmelCase__ = """openwebtext_ccnews_stories_books_cased"""
# Specify download folder to Gluonnlp's vocab
lowerCAmelCase__ = os.path.join(get_home_dir() , """models""" )
lowerCAmelCase__ = _load_vocab(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , cls=lowerCamelCase__ )
lowerCAmelCase__ = nlp.model.BERTModel(
lowerCamelCase__ , len(lowerCamelCase__ ) , units=predefined_args["""units"""] , embed_size=predefined_args["""embed_size"""] , embed_dropout=predefined_args["""embed_dropout"""] , word_embed=predefined_args["""word_embed"""] , use_pooler=lowerCamelCase__ , use_token_type_embed=lowerCamelCase__ , token_type_vocab_size=predefined_args["""token_type_vocab_size"""] , use_classifier=lowerCamelCase__ , use_decoder=lowerCamelCase__ , )
original_bort.load_parameters(lowerCamelCase__ , cast_dtype=lowerCamelCase__ , ignore_extra=lowerCamelCase__ )
lowerCAmelCase__ = original_bort._collect_params_with_prefix()
# Build our config 🤗
lowerCAmelCase__ = {
"""architectures""": ["""BertForMaskedLM"""],
"""attention_probs_dropout_prob""": predefined_args["""dropout"""],
"""hidden_act""": """gelu""",
"""hidden_dropout_prob""": predefined_args["""dropout"""],
"""hidden_size""": predefined_args["""embed_size"""],
"""initializer_range""": 0.02,
"""intermediate_size""": predefined_args["""hidden_size"""],
"""layer_norm_eps""": predefined_args["""layer_norm_eps"""],
"""max_position_embeddings""": predefined_args["""max_length"""],
"""model_type""": """bort""",
"""num_attention_heads""": predefined_args["""num_heads"""],
"""num_hidden_layers""": predefined_args["""num_layers"""],
"""pad_token_id""": 1, # 2 = BERT, 1 = RoBERTa
"""type_vocab_size""": 1, # 2 = BERT, 1 = RoBERTa
"""vocab_size""": len(lowerCamelCase__ ),
}
lowerCAmelCase__ = BertConfig.from_dict(lowerCamelCase__ )
lowerCAmelCase__ = BertForMaskedLM(lowerCamelCase__ )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(lowerCamelCase__ ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = hf_param.shape
lowerCAmelCase__ = to_torch(params[gluon_param] )
lowerCAmelCase__ = gluon_param.shape
assert (
shape_hf == shape_gluon
), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""
return gluon_param
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , """word_embed.0.weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , """encoder.position_weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , """encoder.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , """encoder.layer_norm.gamma""" )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
lowerCAmelCase__ = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
lowerCAmelCase__ = hf_bort_model.bert.encoder.layer[i]
# self attention
lowerCAmelCase__ = layer.attention.self
lowerCAmelCase__ = check_and_map_params(
self_attn.key.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.key.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight""" )
# self attention output
lowerCAmelCase__ = layer.attention.output
lowerCAmelCase__ = check_and_map_params(
self_output.dense.bias , f"""encoder.transformer_cells.{i}.proj.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_output.dense.weight , f"""encoder.transformer_cells.{i}.proj.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.layer_norm.gamma""" )
# intermediate
lowerCAmelCase__ = layer.intermediate
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias""" )
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight""" )
# output
lowerCAmelCase__ = layer.output
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma""" )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
lowerCAmelCase__ = RobertaTokenizer.from_pretrained("""roberta-base""" )
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ )["""input_ids"""]
# Get gluon output
lowerCAmelCase__ = mx.nd.array([input_ids] )
lowerCAmelCase__ = original_bort(inputs=lowerCamelCase__ , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(lowerCamelCase__ )
lowerCAmelCase__ = BertModel.from_pretrained(lowerCamelCase__ )
hf_bort_model.eval()
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ , return_tensors="""pt""" )
lowerCAmelCase__ = hf_bort_model(**lowerCamelCase__ )[0]
lowerCAmelCase__ = output_gluon[0].asnumpy()
lowerCAmelCase__ = output_hf[0].detach().numpy()
lowerCAmelCase__ = np.max(np.abs(hf_layer - gluon_layer ) ).item()
lowerCAmelCase__ = np.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-3 )
if success:
print("""✔️ Both model do output the same tensors""" )
else:
print("""❌ Both model do **NOT** output the same tensors""" )
print("""Absolute difference is:""" , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : str = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 674 | 0 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class a_ :
def __init__( self : Any , snake_case__ : Dict , snake_case__ : str , snake_case__ : Optional[Any] ):
if dst_width < 0 or dst_height < 0:
raise ValueError("""Destination width/height should be > 0""" )
lowerCAmelCase__ = img
lowerCAmelCase__ = img.shape[1]
lowerCAmelCase__ = img.shape[0]
lowerCAmelCase__ = dst_width
lowerCAmelCase__ = dst_height
lowerCAmelCase__ = self.src_w / self.dst_w
lowerCAmelCase__ = self.src_h / self.dst_h
lowerCAmelCase__ = lowerCAmelCase__ = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
for i in range(self.dst_h ):
for j in range(self.dst_w ):
lowerCAmelCase__ = self.img[self.get_y(snake_case__ )][self.get_x(snake_case__ )]
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Union[str, Any] ):
return int(self.ratio_x * x )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Tuple ):
return int(self.ratio_y * y )
if __name__ == "__main__":
__lowerCAmelCase , __lowerCAmelCase : str = 8_00, 6_00
__lowerCAmelCase : Optional[int] = imread("image_data/lena.jpg", 1)
__lowerCAmelCase : Union[str, Any] = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
F"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
)
waitKey(0)
destroyAllWindows()
| 708 | """simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self : Optional[int] ):
lowerCAmelCase__ = """"""
lowerCAmelCase__ = """"""
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
lowerCAmelCase__ = 256
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Union[str, Any] ):
lowerCAmelCase__ = cva.imread(snake_case__ , 0 )
lowerCAmelCase__ = copy.deepcopy(self.img )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="""x""" )
lowerCAmelCase__ = np.sum(snake_case__ )
for i in range(len(snake_case__ ) ):
lowerCAmelCase__ = x[i] / self.k
self.sk += prk
lowerCAmelCase__ = (self.L - 1) * self.sk
if self.rem != 0:
lowerCAmelCase__ = int(last % last )
lowerCAmelCase__ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(snake_case__ )
lowerCAmelCase__ = int(np.ma.count(self.img ) / self.img[1].size )
lowerCAmelCase__ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCAmelCase__ = self.img[j][i]
if num != self.last_list[num]:
lowerCAmelCase__ = self.last_list[num]
cva.imwrite("""output_data/output.jpg""" , self.img )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
cva.imshow("""Output-Image""" , self.img )
cva.imshow("""Input-Image""" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
__lowerCAmelCase : Dict = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
__lowerCAmelCase : Optional[int] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 674 | 0 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : Optional[int] = get_tests_dir("fixtures/test_sentencepiece.model")
__lowerCAmelCase : Tuple = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
__lowerCAmelCase : int = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class a_ ( lowerCAmelCase__ , unittest.TestCase ):
UpperCamelCase_ : Tuple = CamembertTokenizer
UpperCamelCase_ : Any = CamembertTokenizerFast
UpperCamelCase_ : List[str] = True
UpperCamelCase_ : Union[str, Any] = True
def _SCREAMING_SNAKE_CASE ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = CamembertTokenizer(_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = """<pad>"""
lowerCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>NOTUSED""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , 1004 )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1005 )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = CamembertTokenizer(_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
lowerCAmelCase__ = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = """I was born in 92000, and this is falsé."""
lowerCAmelCase__ = tokenizer.encode(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def _SCREAMING_SNAKE_CASE ( self : str ):
if not self.test_rust_tokenizer:
return
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = """I was born in 92000, and this is falsé."""
lowerCAmelCase__ = tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = tokenizer.encode(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
# fmt: off
lowerCAmelCase__ = {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
lowerCAmelCase__ = [
"""Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
"""utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
"""À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
"""pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
"""telles que la traduction et la synthèse de texte.""",
]
self.tokenizer_integration_test_util(
expected_encoding=_SCREAMING_SNAKE_CASE , model_name="""camembert-base""" , revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""" , sequences=_SCREAMING_SNAKE_CASE , )
| 709 | """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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : List[str] = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
UpperCamelCase_ : str = "CIDAS/clipseg-rd64-refined"
UpperCamelCase_ : Any = "image_segmenter"
UpperCamelCase_ : Optional[Any] = CLIPSegForImageSegmentation
UpperCamelCase_ : List[str] = ["image", "text"]
UpperCamelCase_ : int = ["image"]
def __init__( self : Tuple , *snake_case__ : str , **snake_case__ : Optional[Any] ):
requires_backends(self , ["""vision"""] )
super().__init__(*snake_case__ , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : "Image" , snake_case__ : str ):
return self.pre_processor(text=[label] , images=[image] , padding=snake_case__ , return_tensors="""pt""" )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Tuple ):
with torch.no_grad():
lowerCAmelCase__ = self.model(**snake_case__ ).logits
return logits
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : List[Any] ):
lowerCAmelCase__ = outputs.cpu().detach().numpy()
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 674 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class a_ :
UpperCamelCase_ : Optional[int] = 42
UpperCamelCase_ : List[Any] = None
# Automatically constructed
UpperCamelCase_ : Optional[int] = "dict"
UpperCamelCase_ : int = None
UpperCamelCase_ : Optional[Any] = field(default="Translation" , init=_A , repr=_A )
def __call__( self : Union[str, Any] ):
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class a_ :
UpperCamelCase_ : Optional[int] = None
UpperCamelCase_ : Dict = None
UpperCamelCase_ : List[Any] = None
# Automatically constructed
UpperCamelCase_ : int = "dict"
UpperCamelCase_ : Tuple = None
UpperCamelCase_ : List[str] = field(default="TranslationVariableLanguages" , init=_A , repr=_A )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = sorted(set(self.languages ) ) if self.languages else None
lowerCAmelCase__ = len(self.languages ) if self.languages else None
def __call__( self : List[str] ):
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , snake_case__ : Optional[int] ):
lowerCAmelCase__ = set(self.languages )
if self.languages and set(UpperCamelCase__ ) - lang_set:
raise ValueError(
F"""Some languages in example ({', '.join(sorted(set(UpperCamelCase__ ) - lang_set ) )}) are not in valid set ({', '.join(UpperCamelCase__ )}).""" )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
lowerCAmelCase__ = []
for lang, text in translation_dict.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
lowerCAmelCase__ , lowerCAmelCase__ = zip(*sorted(UpperCamelCase__ ) )
return {"language": languages, "translation": translations}
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 710 | """simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = LayoutLMTokenizer
UpperCamelCase_ : List[Any] = LayoutLMTokenizerFast
UpperCamelCase_ : Dict = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
lowerCAmelCase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ = 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 _SCREAMING_SNAKE_CASE ( self : int , **snake_case__ : Union[str, Any] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Tuple ):
lowerCAmelCase__ = """UNwant\u00E9d,running"""
lowerCAmelCase__ = """unwanted, running"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase__ = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(snake_case__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [7, 4, 5, 10, 8, 9] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
pass
| 674 | 0 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer
from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class a_ ( snake_case__ , unittest.TestCase ):
UpperCamelCase_ : Any = WavaVecaPhonemeCTCTokenizer
UpperCamelCase_ : Union[str, Any] = False
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
lowerCAmelCase__ = (
'<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː '
'ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː '
'ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 '
'oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ '
'pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ '
'yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ '
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
'ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ '
'ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ '
'uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ '
'ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ '
'ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ '
'ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4'
).split(""" """ )
lowerCAmelCase__ = dict(zip(_A , range(len(_A ) ) ) )
lowerCAmelCase__ = {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}
lowerCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_A ) + """\n""" )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Any , snake_case__ : List[Any]=False , snake_case__ : str=20 , snake_case__ : Tuple=5 ):
lowerCAmelCase__ = [(i, tokenizer.decode([i] , clean_up_tokenization_spaces=_A )) for i in range(len(_A ) )]
lowerCAmelCase__ = list(filter(lambda snake_case__ : [t[0]] == tokenizer.encode(t[1] , do_phonemize=_A ) , _A ) )
if max_length is not None and len(_A ) > max_length:
lowerCAmelCase__ = toks[:max_length]
if min_length is not None and len(_A ) < min_length and len(_A ) > 0:
while len(_A ) < min_length:
lowerCAmelCase__ = toks + toks
# toks_str = [t[1] for t in toks]
lowerCAmelCase__ = [t[0] for t in toks]
# Ensure consistency
lowerCAmelCase__ = tokenizer.decode(_A , clean_up_tokenization_spaces=_A )
if " " not in output_txt and len(_A ) > 1:
lowerCAmelCase__ = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=_A )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=_A )
)
if with_prefix_space:
lowerCAmelCase__ = ' ' + output_txt
lowerCAmelCase__ = tokenizer.encode(_A , add_special_tokens=_A )
return output_txt, output_ids
def _SCREAMING_SNAKE_CASE ( self : Any , **snake_case__ : str ):
kwargs.update(self.special_tokens_map )
return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname , **_A )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )
# check adding a single token
tokenizer.add_tokens("""xxx""" )
lowerCAmelCase__ = tokenizer("""m xxx ɪ""" , do_phonemize=_A ).input_ids
self.assertEqual(_A , [13, 392, 17] ) # xxx should be last token
tokenizer.add_tokens(["""aaa""", """bbb""", """ccc"""] )
lowerCAmelCase__ = tokenizer("""m aaa ɪ ccc""" , do_phonemize=_A ).input_ids
self.assertEqual(_A , [13, 393, 17, 395] ) # aaa and ccc should be after xxx and 2 after aaa
lowerCAmelCase__ = tokenizer("""maɪ c""" , do_phonemize=_A ).input_ids
self.assertEqual(_A , [3, 200] ) # mai should be <unk> (=3)
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )
lowerCAmelCase__ = 'Hello how are you'
lowerCAmelCase__ = tokenizer.phonemize(_A , phonemizer_lang="""en-us""" )
self.assertEqual(_A , """h ə l oʊ h aʊ ɑːɹ j uː""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )
lowerCAmelCase__ = 'Hello how are you'
lowerCAmelCase__ = tokenizer.phonemize(_A , phonemizer_lang="""en-us""" )
self.assertEqual(tokenizer(_A ).input_ids , tokenizer(_A , do_phonemize=_A ).input_ids )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )
lowerCAmelCase__ = 'Hello how are you'
lowerCAmelCase__ = tokenizer.phonemize(_A , phonemizer_lang="""en-us""" )
lowerCAmelCase__ = tokenizer.decode(tokenizer(_A ).input_ids )
self.assertEqual(_A , _A )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )
lowerCAmelCase__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, 24, 22, 5, 77],
]
lowerCAmelCase__ = tokenizer.decode(sample_ids[0] )
lowerCAmelCase__ = tokenizer.batch_decode(_A )
self.assertEqual(_A , batch_tokens[0] )
self.assertEqual(_A , ["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""] )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(
"""facebook/wav2vec2-lv-60-espeak-cv-ft""" , word_delimiter_token="""|""" )
tokenizer.add_tokens("""|""" )
lowerCAmelCase__ = 'Hello how are you'
lowerCAmelCase__ = tokenizer.phonemize(_A , phonemizer_lang="""en-us""" )
self.assertEqual(_A , """h ə l oʊ | h aʊ | ɑːɹ | j uː |""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(
"""facebook/wav2vec2-lv-60-espeak-cv-ft""" , word_delimiter_token="""|""" )
tokenizer.add_tokens("""|""" )
lowerCAmelCase__ = 'Hello how are you'
lowerCAmelCase__ = tokenizer.phonemize(_A , phonemizer_lang="""en-us""" )
self.assertEqual(tokenizer(_A ).input_ids , tokenizer(_A , do_phonemize=_A ).input_ids )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(
"""facebook/wav2vec2-lv-60-espeak-cv-ft""" , word_delimiter_token="""|""" )
tokenizer.add_tokens("""|""" )
# fmt: off
lowerCAmelCase__ = [
[11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
[tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
]
# fmt: on
# decode with word_del_token filter
lowerCAmelCase__ = tokenizer.decode(sample_ids[0] )
lowerCAmelCase__ = tokenizer.batch_decode(_A )
self.assertEqual(_A , batch_tokens[0] )
self.assertEqual(_A , ["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""] )
# decode with no word_del_token filter
lowerCAmelCase__ = tokenizer.decode(sample_ids[0] , filter_word_delimiter_token=_A )
lowerCAmelCase__ = tokenizer.batch_decode(_A , filter_word_delimiter_token=_A )
self.assertEqual(_A , batch_tokens[0] )
self.assertEqual(_A , ["""k s ɾ | ɾ l | ɭʲ""", """| j ð | s j ð s oːɹ"""] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(
"""facebook/wav2vec2-lv-60-espeak-cv-ft""" , word_delimiter_token="""|""" )
tokenizer.add_tokens("""|""" )
lowerCAmelCase__ = 'Hello how are you'
lowerCAmelCase__ = tokenizer.phonemize(_A , phonemizer_lang="""en-us""" )
lowerCAmelCase__ = tokenizer.decode(tokenizer(_A ).input_ids , filter_word_delimiter_token=_A )
self.assertEqual(_A , _A )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(
"""facebook/wav2vec2-lv-60-espeak-cv-ft""" , word_delimiter_token="""|""" )
tokenizer.add_tokens("""|""" )
lowerCAmelCase__ = 'Hello how are you'
lowerCAmelCase__ = tokenizer.phonemize(_A , phonemizer_lang="""en-us""" )
lowerCAmelCase__ = tokenizer.decode(tokenizer(_A ).input_ids , filter_word_delimiter_token=_A )
self.assertEqual(""" """.join([p.strip() for p in phonemes.split(""" |""" )] ).strip() , _A )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(
"""facebook/wav2vec2-lv-60-espeak-cv-ft""" , word_delimiter_token=_A )
lowerCAmelCase__ = 'Hello how are you'
lowerCAmelCase__ = tokenizer(_A , phonemizer_lang="""en-us""" ).input_ids
lowerCAmelCase__ = tokenizer(_A , phonemizer_lang="""fr-fr""" ).input_ids
self.assertNotEqual(_A , _A )
lowerCAmelCase__ = tokenizer.decode(_A )
lowerCAmelCase__ = tokenizer.decode(_A )
self.assertEqual(_A , """h ə l oʊ h aʊ ɑːɹ j uː""" )
self.assertEqual(_A , """ɛ l o h aʊ a ʁ j u""" )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )
lowerCAmelCase__ = 'Hello how Are you'
lowerCAmelCase__ = 'hello how are you'
lowerCAmelCase__ = tokenizer(_A ).input_ids
lowerCAmelCase__ = tokenizer(_A ).input_ids
self.assertEqual(_A , _A )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft""" )
tokenizer.add_tokens(["""!""", """?"""] )
tokenizer.add_special_tokens({"""cls_token""": """$$$"""} )
# fmt: off
lowerCAmelCase__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
[24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
]
# fmt: on
lowerCAmelCase__ = tokenizer.batch_decode(_A )
self.assertEqual(_A , ["""k s ɾ ɾ l ɭʲ!?!? $$$""", """j ð s j ð s oːɹ $$$"""] )
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : Optional[int] , snake_case__ : Any ):
lowerCAmelCase__ = [d[key] for d in offsets]
return retrieved_list
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.get_tokenizer(word_delimiter_token="""|""" )
tokenizer.add_tokens("""|""" )
# fmt: off
# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
lowerCAmelCase__ = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
# fmt: on
lowerCAmelCase__ = tokenizer.decode(_A , output_char_offsets=_A , filter_word_delimiter_token=_A )
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs.keys() ) , 2 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""char_offsets""" in outputs )
self.assertTrue(isinstance(_A , _A ) )
# check that order of chars is correct and identical for both outputs
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""char_offsets"""] , """char""" ) ) , outputs.text )
self.assertListEqual(
self.get_from_offsets(outputs["""char_offsets"""] , """char""" ) , ["""k""", """s""", """ɾ""", """ɾ""", """|""", """ɾ""", """l""", """|""", """ɭʲ"""] )
# check that offsets are actually correct for char
# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
self.assertListEqual(
self.get_from_offsets(outputs["""char_offsets"""] , """start_offset""" ) , [0, 1, 4, 7, 9, 11, 12, 15, 16] )
self.assertListEqual(
self.get_from_offsets(outputs["""char_offsets"""] , """end_offset""" ) , [1, 4, 6, 9, 10, 12, 15, 16, 17] )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.get_tokenizer(word_delimiter_token="""|""" )
def check_list_tuples_equal(snake_case__ : List[Any] , snake_case__ : Union[str, Any] ):
self.assertTrue(isinstance(_A , _A ) )
self.assertTrue(isinstance(outputs_list[0] , _A ) )
# transform list to ModelOutput
lowerCAmelCase__ = WavaVecaPhonemeCTCTokenizerOutput(
{k: [d[k] for d in outputs_list] for k in outputs_list[0]} )
self.assertListEqual(outputs_batch["""text"""] , outputs_batch_a["""text"""] )
def recursive_check(snake_case__ : Optional[Any] , snake_case__ : Any ):
if isinstance(_A , _A ):
[recursive_check(_A , _A ) for la, la in zip(_A , _A )]
self.assertEqual(_A , _A )
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch["""char_offsets"""] , outputs_batch_a["""char_offsets"""] )
# fmt: off
lowerCAmelCase__ = [
[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
lowerCAmelCase__ = tokenizer.batch_decode(_A , output_char_offsets=_A )
lowerCAmelCase__ = [tokenizer.decode(_A , output_char_offsets=_A ) for ids in sample_ids]
check_list_tuples_equal(_A , _A )
@unittest.skip("""Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes""" )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
pass
@unittest.skip("""Wav2Vec2PhonemeTokenizer always puts spaces between phonemes""" )
def _SCREAMING_SNAKE_CASE ( self : int ):
pass
@unittest.skip("""encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
pass
@unittest.skip("""Wav2Vec2PhonemeModel has no max model length => no testing""" )
def _SCREAMING_SNAKE_CASE ( self : int ):
pass
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.get_tokenizers(do_lower_case=_A )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
lowerCAmelCase__ = tokenizer.vocab_size
lowerCAmelCase__ = len(_A )
self.assertNotEqual(_A , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
lowerCAmelCase__ = ['aaaaa bbbbbb', 'cccccccccdddddddd']
lowerCAmelCase__ = tokenizer.add_tokens(_A )
lowerCAmelCase__ = tokenizer.vocab_size
lowerCAmelCase__ = len(_A )
self.assertNotEqual(_A , 0 )
self.assertEqual(_A , _A )
self.assertEqual(_A , len(_A ) )
self.assertEqual(_A , all_size + len(_A ) )
lowerCAmelCase__ = tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l""" , add_special_tokens=_A )
self.assertGreaterEqual(len(_A ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
lowerCAmelCase__ = {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
lowerCAmelCase__ = tokenizer.add_special_tokens(_A )
lowerCAmelCase__ = tokenizer.vocab_size
lowerCAmelCase__ = len(_A )
self.assertNotEqual(_A , 0 )
self.assertEqual(_A , _A )
self.assertEqual(_A , len(_A ) )
self.assertEqual(_A , all_size_a + len(_A ) )
lowerCAmelCase__ = tokenizer.encode(
""">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l""" , add_special_tokens=_A )
self.assertGreaterEqual(len(_A ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
@unittest.skip("""The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.""" )
def _SCREAMING_SNAKE_CASE ( self : str ):
pass
@unittest.skip("""The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.""" )
def _SCREAMING_SNAKE_CASE ( self : int ):
pass
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
# is not the case for Wav2Vec2PhonemeCTCTokenizer.
lowerCAmelCase__ = self.get_tokenizers(fast=_A , do_lower_case=_A )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
lowerCAmelCase__ = ['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
lowerCAmelCase__ = tokenizer.convert_tokens_to_string(_A )
self.assertIsInstance(output["""text"""] , _A )
| 711 | """simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase : Any = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
def __init__( self : List[str] , snake_case__ : int = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
lowerCAmelCase__ = primes[group]["""prime"""]
lowerCAmelCase__ = primes[group]["""generator"""]
lowerCAmelCase__ = int(hexlify(urandom(32 ) ) , base=16 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
return hex(self.__private_key )[2:]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = pow(self.generator , self.__private_key , self.prime )
return hex(snake_case__ )[2:]
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(snake_case__ , (self.prime - 1) // 2 , self.prime ) == 1
)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : str ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
if not self.is_valid_public_key(snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , self.__private_key , self.prime )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : int , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(snake_case__ , (prime - 1) // 2 , snake_case__ ) == 1
)
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : str , snake_case__ : str , snake_case__ : int = 14 ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(snake_case__ , snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , snake_case__ , snake_case__ )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : int = ProphetNetTokenizer
UpperCamelCase_ : Optional[int] = False
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
lowerCAmelCase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ = 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 _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : Any ):
lowerCAmelCase__ = """UNwant\u00E9d,running"""
lowerCAmelCase__ = """unwanted, running"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase__ = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(__a , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = BasicTokenizer(do_lower_case=__a , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
lowerCAmelCase__ = {}
for i, token in enumerate(__a ):
lowerCAmelCase__ = i
lowerCAmelCase__ = WordpieceTokenizer(vocab=__a , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )
lowerCAmelCase__ = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
lowerCAmelCase__ = [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
lowerCAmelCase__ = tokenizer(__a , padding=__a , return_tensors="""pt""" )
self.assertIsInstance(__a , __a )
lowerCAmelCase__ = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__a , __a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def _SCREAMING_SNAKE_CASE ( self : int ):
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )
lowerCAmelCase__ = tokenizer.encode("""sequence builders""" , add_special_tokens=__a )
lowerCAmelCase__ = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__a )
lowerCAmelCase__ = tokenizer.build_inputs_with_special_tokens(__a )
lowerCAmelCase__ = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 712 | """simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
lowerCAmelCase__ = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = weights[0][0][0]
lowerCAmelCase__ = np.asarray(layer_norm_a[0] )
lowerCAmelCase__ = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# lsh weights + output
lowerCAmelCase__ = weights[0][1]
if len(lowerCamelCase__ ) < 4:
set_layer_weights_in_torch_lsh(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
else:
set_layer_weights_in_torch_local(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
# intermediate weighs
lowerCAmelCase__ = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowerCamelCase__ ) == 4:
lowerCAmelCase__ = intermediate_weights[2]
# layernorm 2
lowerCAmelCase__ = np.asarray(intermediate_weights[0][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# intermediate dense
lowerCAmelCase__ = np.asarray(intermediate_weights[1][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
# intermediate out
lowerCAmelCase__ = np.asarray(intermediate_weights[4][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = torch_model.reformer
# word embeds
lowerCAmelCase__ = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCamelCase__ ) , )
if isinstance(weights[3] , lowerCamelCase__ ):
lowerCAmelCase__ = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowerCAmelCase__ = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f"""{position_embeddings[emb_idx]} emb does not match"""
lowerCAmelCase__ = nn.Parameter(torch.tensor(lowerCamelCase__ ) )
lowerCAmelCase__ = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowerCamelCase__ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowerCAmelCase__ = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# output layer norm
lowerCAmelCase__ = np.asarray(weights[7][0] )
lowerCAmelCase__ = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# output embeddings
lowerCAmelCase__ = np.asarray(weights[9][0] )
lowerCAmelCase__ = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = ReformerConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = ReformerModelWithLMHead(lowerCamelCase__ )
with open(lowerCamelCase__ , """rb""" ) as f:
lowerCAmelCase__ = pickle.load(lowerCamelCase__ )["""weights"""]
set_model_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , config.hidden_size )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase : Any = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
def __init__( self : Optional[int] , snake_case__ : int = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
lowerCAmelCase__ = primes[group]["""prime"""]
lowerCAmelCase__ = primes[group]["""generator"""]
lowerCAmelCase__ = int(hexlify(urandom(32 ) ) , base=16 )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
return hex(self.__private_key )[2:]
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = pow(self.generator , self.__private_key , self.prime )
return hex(lowerCamelCase_ )[2:]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : int ):
return (
2 <= key <= self.prime - 2
and pow(lowerCamelCase_ , (self.prime - 1) // 2 , self.prime ) == 1
)
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : str ):
lowerCAmelCase__ = int(lowerCamelCase_ , base=16 )
if not self.is_valid_public_key(lowerCamelCase_ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(lowerCamelCase_ , self.__private_key , self.prime )
return shaaaa(str(lowerCamelCase_ ).encode() ).hexdigest()
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : int , snake_case__ : int ):
return (
2 <= remote_public_key_str <= prime - 2
and pow(lowerCamelCase_ , (prime - 1) // 2 , lowerCamelCase_ ) == 1
)
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : str , snake_case__ : str , snake_case__ : int = 14 ):
lowerCAmelCase__ = int(lowerCamelCase_ , base=16 )
lowerCAmelCase__ = int(lowerCamelCase_ , base=16 )
lowerCAmelCase__ = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
return shaaaa(str(lowerCamelCase_ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 713 | """simple docstring"""
import os
from math import logaa
def _UpperCAmelCase ( lowerCamelCase__ = "base_exp.txt" ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) ):
lowerCAmelCase__ , lowerCAmelCase__ = list(map(lowerCamelCase__ , line.split(""",""" ) ) )
if x * logaa(lowerCamelCase__ ) > largest:
lowerCAmelCase__ = x * logaa(lowerCamelCase__ )
lowerCAmelCase__ = i + 1
return result
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = tempfile.mkdtemp()
# fmt: off
lowerCAmelCase__ = ["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
lowerCAmelCase__ = dict(zip(UpperCamelCase_ , range(len(UpperCamelCase_ ) ) ) )
lowerCAmelCase__ = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
lowerCAmelCase__ = {"""unk_token""": """<unk>"""}
lowerCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCamelCase_ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCamelCase_ ) )
lowerCAmelCase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
"""image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
lowerCAmelCase__ = os.path.join(self.tmpdirname , UpperCamelCase_ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(UpperCamelCase_ , UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : int ):
return CLIPTokenizer.from_pretrained(self.tmpdirname , pad_token="""!""" , **UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , **snake_case__ : Union[str, Any] ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , pad_token="""!""" , **UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( self : int , **snake_case__ : List[Any] ):
return OwlViTImageProcessor.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( self : int ):
shutil.rmtree(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCAmelCase__ = [Image.fromarray(np.moveaxis(UpperCamelCase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = OwlViTProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
lowerCAmelCase__ = OwlViTProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCamelCase_ )
lowerCAmelCase__ = OwlViTProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
lowerCAmelCase__ = OwlViTProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , UpperCamelCase_ )
self.assertIsInstance(processor_fast.tokenizer , UpperCamelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , UpperCamelCase_ )
self.assertIsInstance(processor_fast.image_processor , UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = OwlViTProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase__ = self.get_image_processor(do_normalize=UpperCamelCase_ )
lowerCAmelCase__ = OwlViTProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=UpperCamelCase_ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCamelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = OwlViTProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = image_processor(UpperCamelCase_ , return_tensors="""np""" )
lowerCAmelCase__ = processor(images=UpperCamelCase_ , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = OwlViTProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = processor(text=UpperCamelCase_ , return_tensors="""np""" )
lowerCAmelCase__ = tokenizer(UpperCamelCase_ , return_tensors="""np""" )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist() , encoded_processor[key][0].tolist() )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = OwlViTProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = processor(text=UpperCamelCase_ , images=UpperCamelCase_ )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase_ ):
processor()
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = """google/owlvit-base-patch32"""
lowerCAmelCase__ = OwlViTProcessor.from_pretrained(UpperCamelCase_ )
lowerCAmelCase__ = ["""cat""", """nasa badge"""]
lowerCAmelCase__ = processor(text=UpperCamelCase_ )
lowerCAmelCase__ = 16
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape , (2, seq_length) )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase_ ):
processor()
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = """google/owlvit-base-patch32"""
lowerCAmelCase__ = OwlViTProcessor.from_pretrained(UpperCamelCase_ )
lowerCAmelCase__ = [["""cat""", """nasa badge"""], ["""person"""]]
lowerCAmelCase__ = processor(text=UpperCamelCase_ )
lowerCAmelCase__ = 16
lowerCAmelCase__ = len(UpperCamelCase_ )
lowerCAmelCase__ = max([len(UpperCamelCase_ ) for texts in input_texts] )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape , (batch_size * num_max_text_queries, seq_length) )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase_ ):
processor()
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = """google/owlvit-base-patch32"""
lowerCAmelCase__ = OwlViTProcessor.from_pretrained(UpperCamelCase_ )
lowerCAmelCase__ = ["""cat""", """nasa badge"""]
lowerCAmelCase__ = processor(text=UpperCamelCase_ )
lowerCAmelCase__ = 16
lowerCAmelCase__ = inputs["""input_ids"""]
lowerCAmelCase__ = [
[49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask"""] )
self.assertEqual(inputs["""input_ids"""].shape , (2, seq_length) )
self.assertListEqual(list(input_ids[0] ) , predicted_ids[0] )
self.assertListEqual(list(input_ids[1] ) , predicted_ids[1] )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = OwlViTProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = processor(images=UpperCamelCase_ , query_images=UpperCamelCase_ )
self.assertListEqual(list(inputs.keys() ) , ["""query_pixel_values""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase_ ):
processor()
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = OwlViTProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
lowerCAmelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase__ = processor.batch_decode(UpperCamelCase_ )
lowerCAmelCase__ = tokenizer.batch_decode(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
| 714 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
while b:
lowerCAmelCase__ , lowerCAmelCase__ = b, a % b
return a
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(lowerCamelCase__ , a % b )
def _UpperCAmelCase ( ):
"""simple docstring"""
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 674 | 0 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = int(lowerCamelCase__ )
assert noofclusters < len(lowerCamelCase__ )
# Find out the dimensionality
lowerCAmelCase__ = len(vectors[0] )
# Will help select random centroids from among the available vectors
lowerCAmelCase__ = list(range(len(lowerCamelCase__ ) ) )
shuffle(lowerCamelCase__ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
lowerCAmelCase__ = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
lowerCAmelCase__ = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
lowerCAmelCase__ = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowerCamelCase__ )
]
##These nodes will assign the centroid Variables the appropriate
##values
lowerCAmelCase__ = tf.placeholder("""float64""" , [dim] )
lowerCAmelCase__ = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowerCamelCase__ , lowerCamelCase__ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
lowerCAmelCase__ = [tf.Variable(0 ) for i in range(len(lowerCamelCase__ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
lowerCAmelCase__ = tf.placeholder("""int32""" )
lowerCAmelCase__ = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowerCamelCase__ , lowerCamelCase__ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
lowerCAmelCase__ = tf.placeholder("""float""" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
lowerCAmelCase__ = tf.reduce_mean(lowerCamelCase__ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
lowerCAmelCase__ = tf.placeholder("""float""" , [dim] )
lowerCAmelCase__ = tf.placeholder("""float""" , [dim] )
lowerCAmelCase__ = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCamelCase__ , lowerCamelCase__ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
lowerCAmelCase__ = tf.placeholder("""float""" , [noofclusters] )
lowerCAmelCase__ = tf.argmin(lowerCamelCase__ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
lowerCAmelCase__ = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowerCamelCase__ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
lowerCAmelCase__ = 100
for _ in range(lowerCamelCase__ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(lowerCamelCase__ ) ):
lowerCAmelCase__ = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
lowerCAmelCase__ = [
sess.run(lowerCamelCase__ , feed_dict={va: vect, va: sess.run(lowerCamelCase__ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
lowerCAmelCase__ = sess.run(
lowerCamelCase__ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(lowerCamelCase__ ):
# Collect all the vectors assigned to this cluster
lowerCAmelCase__ = [
vectors[i]
for i in range(len(lowerCamelCase__ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
lowerCAmelCase__ = sess.run(
lowerCamelCase__ , feed_dict={mean_input: array(lowerCamelCase__ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
lowerCAmelCase__ = sess.run(lowerCamelCase__ )
lowerCAmelCase__ = sess.run(lowerCamelCase__ )
return centroids, assignments
| 715 | """simple docstring"""
import os
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCamelCase__ ) )
lowerCAmelCase__ = os.path.join(lowerCamelCase__ , """triangle.txt""" )
with open(lowerCamelCase__ ) as f:
lowerCAmelCase__ = f.readlines()
lowerCAmelCase__ = []
for line in triangle:
lowerCAmelCase__ = []
for number in line.strip().split(""" """ ):
numbers_from_line.append(int(lowerCamelCase__ ) )
a.append(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
for j in range(len(a[i] ) ):
lowerCAmelCase__ = a[i - 1][j] if j != len(a[i - 1] ) else 0
lowerCAmelCase__ = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowerCamelCase__ , lowerCamelCase__ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class a_ ( __UpperCamelCase ):
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase_ , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(lowercase_ , """num_attention_heads""" ) )
self.parent.assertTrue(hasattr(lowercase_ , """num_encoder_blocks""" ) )
class a_ :
def __init__( self : Optional[Any] , snake_case__ : List[Any] , snake_case__ : Tuple=13 , snake_case__ : List[Any]=64 , snake_case__ : List[Any]=3 , snake_case__ : Optional[Any]=4 , snake_case__ : Dict=[2, 2, 2, 2] , snake_case__ : Optional[Any]=[8, 4, 2, 1] , snake_case__ : str=[16, 32, 64, 128] , snake_case__ : Optional[Any]=[1, 4, 8, 16] , snake_case__ : Tuple=[1, 2, 4, 8] , snake_case__ : List[str]=True , snake_case__ : int=True , snake_case__ : Optional[int]="gelu" , snake_case__ : Dict=0.1 , snake_case__ : List[Any]=0.1 , snake_case__ : str=0.02 , snake_case__ : Optional[int]=3 , snake_case__ : List[str]=None , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = num_encoder_blocks
lowerCAmelCase__ = sr_ratios
lowerCAmelCase__ = depths
lowerCAmelCase__ = hidden_sizes
lowerCAmelCase__ = downsampling_rates
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = scope
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : Dict ):
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : Dict , snake_case__ : List[str] , snake_case__ : str ):
lowerCAmelCase__ = SegformerModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase__ = model(lowercase_ )
lowerCAmelCase__ = lowerCAmelCase__ = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : List[Any] , snake_case__ : Tuple , snake_case__ : Dict ):
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = SegformerForSemanticSegmentation(lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase__ = model(lowercase_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
lowerCAmelCase__ = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , snake_case__ : str , snake_case__ : Union[str, Any] , snake_case__ : int ):
lowerCAmelCase__ = 1
lowerCAmelCase__ = SegformerForSemanticSegmentation(config=lowercase_ )
model.to(lowercase_ )
model.eval()
lowerCAmelCase__ = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(lowercase_ )
lowerCAmelCase__ = model(lowercase_ , labels=lowercase_ )
self.parent.assertGreater(result.loss , 0.0 )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : List[str] = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
UpperCamelCase_ : Tuple = (
{
"feature-extraction": SegformerModel,
"image-classification": SegformerForImageClassification,
"image-segmentation": SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase_ : int = True
UpperCamelCase_ : int = False
UpperCamelCase_ : Tuple = False
UpperCamelCase_ : Optional[Any] = False
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = SegformerModelTester(self )
lowerCAmelCase__ = SegformerConfigTester(self , config_class=lowercase_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase_ )
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*lowercase_ )
@unittest.skip("""SegFormer does not use inputs_embeds""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods""" )
def _SCREAMING_SNAKE_CASE ( self : str ):
pass
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(lowercase_ )
lowerCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = True
for model_class in self.all_model_classes:
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
lowerCAmelCase__ = outputs.attentions
lowerCAmelCase__ = sum(self.model_tester.depths )
self.assertEqual(len(lowercase_ ) , lowercase_ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
lowerCAmelCase__ = outputs.attentions
self.assertEqual(len(lowercase_ ) , lowercase_ )
# verify the first attentions (first block, first layer)
lowerCAmelCase__ = (self.model_tester.image_size // 4) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
lowerCAmelCase__ = (self.model_tester.image_size // 32) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
lowerCAmelCase__ = len(lowercase_ )
# Check attention is always last and order is fine
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
self.assertEqual(out_len + 1 , len(lowercase_ ) )
lowerCAmelCase__ = outputs.attentions
self.assertEqual(len(lowercase_ ) , lowercase_ )
# verify the first attentions (first block, first layer)
lowerCAmelCase__ = (self.model_tester.image_size // 4) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
def check_hidden_states_output(snake_case__ : Union[str, Any] , snake_case__ : Dict , snake_case__ : Optional[int] ):
lowerCAmelCase__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
lowerCAmelCase__ = outputs.hidden_states
lowerCAmelCase__ = self.model_tester.num_encoder_blocks
self.assertEqual(len(lowercase_ ) , lowercase_ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def _SCREAMING_SNAKE_CASE ( self : Any ):
if not self.model_tester.is_training:
return
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = True
for model_class in self.all_model_classes:
if model_class in get_values(lowercase_ ):
continue
lowerCAmelCase__ = model_class(lowercase_ )
model.to(lowercase_ )
model.train()
lowerCAmelCase__ = self._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
lowerCAmelCase__ = model(**lowercase_ ).loss
loss.backward()
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ):
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = SegformerModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class a_ ( unittest.TestCase ):
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=lowercase_ , align=lowercase_ , do_random_crop=lowercase_ )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
lowercase_ )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowercase_ , return_tensors="""pt""" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(lowercase_ )
with torch.no_grad():
lowerCAmelCase__ = model(lowercase_ )
lowerCAmelCase__ = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , lowercase_ )
lowerCAmelCase__ = torch.tensor(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , lowercase_ , atol=1E-4 ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=lowercase_ , align=lowercase_ , do_random_crop=lowercase_ )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained(
"""nvidia/segformer-b1-finetuned-cityscapes-1024-1024""" ).to(lowercase_ )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowercase_ , return_tensors="""pt""" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(lowercase_ )
with torch.no_grad():
lowerCAmelCase__ = model(lowercase_ )
lowerCAmelCase__ = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , lowercase_ )
lowerCAmelCase__ = torch.tensor(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , lowercase_ , atol=1E-1 ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=lowercase_ , align=lowercase_ , do_random_crop=lowercase_ )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
lowercase_ )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowercase_ , return_tensors="""pt""" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(lowercase_ )
with torch.no_grad():
lowerCAmelCase__ = model(lowercase_ )
lowerCAmelCase__ = outputs.logits.detach().cpu()
lowerCAmelCase__ = image_processor.post_process_semantic_segmentation(outputs=lowercase_ , target_sizes=[(500, 300)] )
lowerCAmelCase__ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , lowercase_ )
lowerCAmelCase__ = image_processor.post_process_semantic_segmentation(outputs=lowercase_ )
lowerCAmelCase__ = torch.Size((128, 128) )
self.assertEqual(segmentation[0].shape , lowercase_ )
| 716 | """simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase : Union[str, Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
__lowerCAmelCase : Optional[int] = json.load(f)
@require_torch
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Dict ):
return FSMTTokenizer.from_pretrained(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : Any ):
lowerCAmelCase__ = FSMTForConditionalGeneration.from_pretrained(snake_case__ ).to(snake_case__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Any , snake_case__ : int ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
lowerCAmelCase__ = F"""facebook/wmt19-{pair}"""
lowerCAmelCase__ = self.get_tokenizer(snake_case__ )
lowerCAmelCase__ = self.get_model(snake_case__ )
lowerCAmelCase__ = bleu_data[pair]["""src"""]
lowerCAmelCase__ = bleu_data[pair]["""tgt"""]
lowerCAmelCase__ = tokenizer(snake_case__ , return_tensors="""pt""" , truncation=snake_case__ , padding="""longest""" ).to(snake_case__ )
lowerCAmelCase__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
lowerCAmelCase__ = tokenizer.batch_decode(
snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
lowerCAmelCase__ = calculate_bleu(snake_case__ , snake_case__ )
print(snake_case__ )
self.assertGreaterEqual(scores["""bleu"""] , snake_case__ )
| 674 | 0 |
"""simple docstring"""
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 a_ ( __lowerCAmelCase ):
UpperCamelCase_ : int = ['''image_processor''', '''tokenizer''']
UpperCamelCase_ : str = '''Pix2StructImageProcessor'''
UpperCamelCase_ : Tuple = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self : Any , snake_case__ : Optional[Any] , snake_case__ : Dict ):
lowerCAmelCase__ = False
super().__init__(lowerCAmelCase_ , lowerCAmelCase_ )
def __call__( self : int , snake_case__ : Optional[Any]=None , snake_case__ : Any = None , snake_case__ : List[str] = True , snake_case__ : List[Any] = False , snake_case__ : List[str] = None , snake_case__ : List[Any] = None , snake_case__ : Union[str, Any] = 2048 , snake_case__ : List[Any] = 0 , snake_case__ : List[str] = None , snake_case__ : int = None , snake_case__ : Optional[int] = False , snake_case__ : List[Any] = False , snake_case__ : Tuple = False , snake_case__ : List[str] = False , snake_case__ : Union[str, Any] = False , snake_case__ : Dict = True , snake_case__ : Any = None , **snake_case__ : Optional[Any] , ):
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None and not self.image_processor.is_vqa:
lowerCAmelCase__ = self.tokenizer
lowerCAmelCase__ = self.tokenizer(
text=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ , stride=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , return_overflowing_tokens=lowerCAmelCase_ , return_special_tokens_mask=lowerCAmelCase_ , return_offsets_mapping=lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ , return_length=lowerCAmelCase_ , verbose=lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
lowerCAmelCase__ = self.image_processor(
lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , max_patches=lowerCAmelCase_ , **lowerCAmelCase_ )
else:
# add pixel_values and bbox
lowerCAmelCase__ = self.image_processor(
lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , max_patches=lowerCAmelCase_ , header_text=lowerCAmelCase_ , **lowerCAmelCase_ )
if text is not None and not self.image_processor.is_vqa:
lowerCAmelCase__ = self.tokenizer(
text=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ , padding=lowerCAmelCase_ , truncation=lowerCAmelCase_ , max_length=lowerCAmelCase_ , stride=lowerCAmelCase_ , pad_to_multiple_of=lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , return_overflowing_tokens=lowerCAmelCase_ , return_special_tokens_mask=lowerCAmelCase_ , return_offsets_mapping=lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ , return_length=lowerCAmelCase_ , verbose=lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ , )
if "attention_mask" in text_encoding:
lowerCAmelCase__ = text_encoding.pop("""attention_mask""" )
if "input_ids" in text_encoding:
lowerCAmelCase__ = text_encoding.pop("""input_ids""" )
else:
lowerCAmelCase__ = None
if text_encoding is not None:
encoding_image_processor.update(lowerCAmelCase_ )
return encoding_image_processor
def _SCREAMING_SNAKE_CASE ( self : int , *snake_case__ : int , **snake_case__ : str ):
return self.tokenizer.batch_decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Dict , *snake_case__ : str , **snake_case__ : Optional[int] ):
return self.tokenizer.decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer.model_input_names
lowerCAmelCase__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 717 | """simple docstring"""
import pprint
import requests
__lowerCAmelCase : Union[str, Any] = "https://zenquotes.io/api"
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = random_quotes()
pprint.pprint(response)
| 674 | 0 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
__lowerCAmelCase : Tuple = 'true'
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=82 , lowerCamelCase__=16 ):
"""simple docstring"""
set_seed(42 )
lowerCAmelCase__ = RegressionModel()
lowerCAmelCase__ = deepcopy(lowerCamelCase__ )
lowerCAmelCase__ = RegressionDataset(length=lowerCamelCase__ )
lowerCAmelCase__ = DataLoader(lowerCamelCase__ , batch_size=lowerCamelCase__ )
model.to(accelerator.device )
lowerCAmelCase__ = accelerator.prepare(lowerCamelCase__ , lowerCamelCase__ )
return model, ddp_model, dataloader
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=False ):
"""simple docstring"""
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
lowerCAmelCase__ = load_dataset("""glue""" , """mrpc""" , split="""validation""" )
def tokenize_function(lowerCamelCase__ ):
lowerCAmelCase__ = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ )
return outputs
with accelerator.main_process_first():
lowerCAmelCase__ = dataset.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
lowerCAmelCase__ = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase__ ):
if use_longest:
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return DataLoader(lowerCamelCase__ , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=16 )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = Accelerator(dispatch_batches=lowerCamelCase__ , split_batches=lowerCamelCase__ )
lowerCAmelCase__ = get_dataloader(lowerCamelCase__ , not dispatch_batches )
lowerCAmelCase__ = AutoModelForSequenceClassification.from_pretrained(
"""hf-internal-testing/mrpc-bert-base-cased""" , return_dict=lowerCamelCase__ )
lowerCAmelCase__ = accelerator.prepare(lowerCamelCase__ , lowerCamelCase__ )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = []
for batch in dataloader:
lowerCAmelCase__ = batch.values()
with torch.no_grad():
lowerCAmelCase__ = model(lowerCamelCase__ )
lowerCAmelCase__ = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowerCAmelCase__ = [], []
for logit, targ in logits_and_targets:
logits.append(lowerCamelCase__ )
targs.append(lowerCamelCase__ )
lowerCAmelCase__ = torch.cat(lowerCamelCase__ ), torch.cat(lowerCamelCase__ )
return logits, targs
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=82 , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=16 ):
"""simple docstring"""
lowerCAmelCase__ = get_basic_setup(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = generate_predictions(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
assert (
len(lowerCamelCase__ ) == num_samples
), f"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(lowerCamelCase__ )}"""
def _UpperCAmelCase ( lowerCamelCase__ = False , lowerCamelCase__ = False ):
"""simple docstring"""
lowerCAmelCase__ = evaluate.load("""glue""" , """mrpc""" )
lowerCAmelCase__ = get_mrpc_setup(lowerCamelCase__ , lowerCamelCase__ )
# First do baseline
lowerCAmelCase__ = setup['''no''']
model.to(lowerCamelCase__ )
model.eval()
for batch in dataloader:
batch.to(lowerCamelCase__ )
with torch.inference_mode():
lowerCAmelCase__ = model(**lowerCamelCase__ )
lowerCAmelCase__ = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=lowerCamelCase__ , references=batch["""labels"""] )
lowerCAmelCase__ = metric.compute()
# Then do distributed
lowerCAmelCase__ = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowerCAmelCase__ = model(**lowerCamelCase__ )
lowerCAmelCase__ = outputs.logits.argmax(dim=-1 )
lowerCAmelCase__ = batch['''labels''']
lowerCAmelCase__ = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=lowerCamelCase__ , references=lowerCamelCase__ )
lowerCAmelCase__ = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Accelerator(split_batches=lowerCamelCase__ , dispatch_batches=lowerCamelCase__ )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("""**Testing gather_for_metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(lowerCamelCase__ , lowerCamelCase__ )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test torch metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowerCAmelCase__ = Accelerator(split_batches=lowerCamelCase__ , dispatch_batches=lowerCamelCase__ )
if accelerator.is_local_main_process:
print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(lowerCamelCase__ , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test last batch is not dropped when perfectly divisible**""" )
lowerCAmelCase__ = Accelerator()
test_torch_metrics(lowerCamelCase__ , 512 )
accelerator.state._reset_state()
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 718 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ ).to_dict()
config_dict.pop("""image_processor_type""" )
lowerCAmelCase__ = CLIPImageProcessor(**snake_case__ )
# save in new folder
model_config.save_pretrained(snake_case__ )
config.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
# make sure private variable is not incorrectly saved
lowerCAmelCase__ = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with self.assertRaisesRegex(
snake_case__ , """clip-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""clip-base""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
with self.assertRaisesRegex(
snake_case__ , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , revision="""aaaaaa""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
with self.assertRaisesRegex(
snake_case__ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , trust_remote_code=snake_case__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case__ ):
AutoImageProcessor.register(snake_case__ , snake_case__ )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = CustomImageProcessor.from_pretrained(snake_case__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Tuple = True
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# If remote code is not set, the default is to use local
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(snake_case__ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ) -> list:
"""simple docstring"""
lowerCAmelCase__ = [0] * len(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
# use last results for better performance - dynamic programming
lowerCAmelCase__ = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
lowerCAmelCase__ = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
lowerCAmelCase__ = j
return prefix_result
def _UpperCAmelCase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
return max(prefix_function(lowerCamelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 719 | """simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a_ :
def __init__( self : Optional[int] , snake_case__ : List[Any]=2 , snake_case__ : Any=3 , snake_case__ : Union[str, Any]=64 , snake_case__ : Any=None ):
lowerCAmelCase__ = np.random.default_rng(snake_case__ )
lowerCAmelCase__ = length
lowerCAmelCase__ = rng.normal(size=(length,) ).astype(np.floataa )
lowerCAmelCase__ = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Optional[Any] ):
return self.length
def __getitem__( self : List[str] , snake_case__ : Optional[int] ):
return {"x": self.x[i], "y": self.y[i]}
class a_ ( torch.nn.Module ):
def __init__( self : List[str] , snake_case__ : str=0 , snake_case__ : Dict=0 , snake_case__ : Any=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Any=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a[0] + self.b[0]
class a_ ( torch.nn.Module ):
def __init__( self : Any , snake_case__ : Union[str, Any]=0 , snake_case__ : Union[str, Any]=0 , snake_case__ : List[Any]=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any]=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a + self.b
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase__ = {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
lowerCAmelCase__ = load_dataset("""csv""" , data_files=lowerCamelCase__ )
lowerCAmelCase__ = datasets["""train"""].unique("""label""" )
lowerCAmelCase__ = {v: i for i, v in enumerate(lowerCamelCase__ )}
def tokenize_function(lowerCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase__ = tokenizer(
examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
if "label" in examples:
lowerCAmelCase__ = [label_to_id[l] for l in examples["""label"""]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase__ = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""sentence1""", """sentence2""", """label"""] , )
def collate_fn(lowerCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase__ = DataLoader(tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=2 )
lowerCAmelCase__ = DataLoader(tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
A , A = len(__a ), len(grid[0] )
if (
min(__a , __a ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
A = 0
count += depth_first_search(__a , row + 1 , __a , __a )
count += depth_first_search(__a , row - 1 , __a , __a )
count += depth_first_search(__a , __a , col + 1 , __a )
count += depth_first_search(__a , __a , col - 1 , __a )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 720 | """simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = MobileBertConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = MobileBertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
lowerCAmelCase__ = load_tf_weights_in_mobilebert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = 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(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT 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."
)
__lowerCAmelCase : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
__lowerCAmelCase : Any = logging.get_logger(__name__)
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
lowerCAmelCase__ = XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__ )
lowerCAmelCase__ = XLMProphetNetForConditionalGeneration.from_pretrained(
lowerCamelCase__ , output_loading_info=lowerCamelCase__ )
else:
lowerCAmelCase__ = ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__ )
lowerCAmelCase__ = ProphetNetForConditionalGeneration.from_pretrained(
lowerCamelCase__ , output_loading_info=lowerCamelCase__ )
lowerCAmelCase__ = ["key_proj", "value_proj", "query_proj"]
lowerCAmelCase__ = {
"self_attn": "ngram_self_attn",
"cross_attn": "encoder_attn",
"cross_attn_layer_norm": "encoder_attn_layer_norm",
"feed_forward_layer_norm": "final_layer_norm",
"feed_forward": "",
"intermediate": "fc1",
"output": "fc2",
"key_proj": "k_proj",
"query_proj": "q_proj",
"value_proj": "v_proj",
"word_embeddings": "embed_tokens",
"embeddings_layer_norm": "emb_layer_norm",
"relative_pos_embeddings": "relative_linear",
"ngram_embeddings": "ngram_input_embed",
"position_embeddings": "embed_positions",
}
for key in loading_info["missing_keys"]:
lowerCAmelCase__ = key.split(""".""" )
if attributes[0] == "lm_head":
lowerCAmelCase__ = prophet
lowerCAmelCase__ = prophet_old
else:
lowerCAmelCase__ = prophet.prophetnet
lowerCAmelCase__ = prophet_old.model
lowerCAmelCase__ = False
for attribute in attributes:
if attribute in mapping:
lowerCAmelCase__ = mapping[attribute]
if not hasattr(lowerCamelCase__ , lowerCamelCase__ ) and len(lowerCamelCase__ ) > 0:
lowerCAmelCase__ = attribute
elif hasattr(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowerCAmelCase__ = old_model.weight
logger.info(f"""{attribute} is initialized.""" )
lowerCAmelCase__ = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowerCAmelCase__ = old_model.bias
logger.info(f"""{attribute} is initialized""" )
lowerCAmelCase__ = True
break
elif attribute in special_keys and hasattr(lowerCamelCase__ , """in_proj_weight""" ):
lowerCAmelCase__ = old_model.in_proj_weight.shape[0] // 3
lowerCAmelCase__ = getattr(lowerCamelCase__ , lowerCamelCase__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowerCAmelCase__ = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowerCAmelCase__ = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowerCAmelCase__ = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowerCAmelCase__ = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowerCAmelCase__ = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowerCAmelCase__ = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowerCAmelCase__ = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowerCAmelCase__ = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowerCAmelCase__ = True
break
if attribute.isdigit():
lowerCAmelCase__ = model[int(lowerCamelCase__ )]
lowerCAmelCase__ = old_model[int(lowerCamelCase__ )]
else:
lowerCAmelCase__ = getattr(lowerCamelCase__ , lowerCamelCase__ )
if old_attribute == "":
lowerCAmelCase__ = old_model
else:
if not hasattr(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError(f"""{old_model} does not have {old_attribute}""" )
lowerCAmelCase__ = getattr(lowerCamelCase__ , lowerCamelCase__ )
if not is_key_init:
raise ValueError(f"""{key} was not correctly initialized!""" )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
prophet.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : int = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 721 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
lowerCAmelCase__ = f"""The input value of [n={number}] has to be > 0"""
raise ValueError(lowerCamelCase__ )
else:
lowerCAmelCase__ = sylvester(number - 1 )
lowerCAmelCase__ = num - 1
lowerCAmelCase__ = num
return lower * upper + 1
if __name__ == "__main__":
print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")
| 674 | 0 |
"""simple docstring"""
class a_ : # Public class to implement a graph
def __init__( self : str , snake_case__ : int , snake_case__ : int , snake_case__ : list[list[bool]] ):
lowerCAmelCase__ = row
lowerCAmelCase__ = col
lowerCAmelCase__ = graph
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : int , snake_case__ : int , snake_case__ : list[list[bool]] ):
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : int , snake_case__ : int , snake_case__ : list[list[bool]] ):
lowerCAmelCase__ = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
lowerCAmelCase__ = [-1, 0, 1, -1, 1, -1, 0, 1]
lowerCAmelCase__ = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , UpperCAmelCase__ ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , UpperCAmelCase__ )
def _SCREAMING_SNAKE_CASE ( self : int ): # And finally, count all islands.
lowerCAmelCase__ = [[False for j in range(self.COL )] for i in range(self.ROW )]
lowerCAmelCase__ = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
count += 1
return count
| 700 | """simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : Optional[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Tuple = PegasusTokenizer
UpperCamelCase_ : Any = PegasusTokenizerFast
UpperCamelCase_ : int = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = PegasusTokenizer(snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : Optional[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any] ):
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = """</s>"""
lowerCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """</s>""" )
self.assertEqual(vocab_keys[-1] , """v""" )
self.assertEqual(len(snake_case__ ) , 1103 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
""" </s> <pad> <pad> <pad>"""
)
lowerCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
lowerCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowerCAmelCase__ = """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
lowerCAmelCase__ = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
lowerCAmelCase__ = tokenizer([raw_input_str] , return_tensors=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
lowerCAmelCase__ = """To ensure a smooth flow of bank resolutions."""
lowerCAmelCase__ = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
lowerCAmelCase__ = tokenizer([raw_input_str] , return_tensors=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = ["""This is going to be way too long.""" * 150, """short example"""]
lowerCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
lowerCAmelCase__ = self._large_tokenizer(snake_case__ , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
lowerCAmelCase__ = self._large_tokenizer(
text_target=snake_case__ , max_length=5 , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case__ ) == 2 # input_ids, attention_mask.
@slow
def _SCREAMING_SNAKE_CASE ( self : str ):
# fmt: off
lowerCAmelCase__ = {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=snake_case__ , model_name="""google/bigbird-pegasus-large-arxiv""" , revision="""ba85d0851d708441f91440d509690f1ab6353415""" , )
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = PegasusTokenizer
UpperCamelCase_ : Optional[int] = PegasusTokenizerFast
UpperCamelCase_ : Union[str, Any] = True
UpperCamelCase_ : Optional[int] = True
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = PegasusTokenizer(snake_case__ , offset=0 , mask_token_sent=snake_case__ , mask_token="""[MASK]""" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Dict ):
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : List[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Dict ):
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
""" <pad> <pad> <pad>"""
)
lowerCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
lowerCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
@require_torch
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = ["""This is going to be way too long.""" * 1000, """short example"""]
lowerCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
lowerCAmelCase__ = self._large_tokenizer(snake_case__ , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
lowerCAmelCase__ = self._large_tokenizer(
text_target=snake_case__ , max_length=5 , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case__ ) == 2 # input_ids, attention_mask.
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = (
"""This is an example string that is used to test the original TF implementation against the HF"""
""" implementation"""
)
lowerCAmelCase__ = self._large_tokenizer(snake_case__ ).input_ids
self.assertListEqual(
snake_case__ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1] , )
| 674 | 0 |
"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__lowerCAmelCase : Any = False
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : str = "ybelkada/fonts"
def _UpperCAmelCase ( ) -> Dict:
"""simple docstring"""
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
f"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
"""Pix2StructImageProcessor. Please upgrade torch.""" )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
"""simple docstring"""
requires_backends(_lowerCAmelCase , ["""torch"""] )
_check_torch_version()
lowerCAmelCase__ = image_tensor.unsqueeze(0 )
lowerCAmelCase__ = torch.nn.functional.unfold(_lowerCAmelCase , (patch_height, patch_width) , stride=(patch_height, patch_width) )
lowerCAmelCase__ = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , _lowerCAmelCase , _lowerCAmelCase , -1 )
lowerCAmelCase__ = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 36 , lowerCamelCase__ = "black" , lowerCamelCase__ = "white" , lowerCamelCase__ = 5 , lowerCamelCase__ = 5 , lowerCamelCase__ = 5 , lowerCamelCase__ = 5 , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> int:
"""simple docstring"""
requires_backends(_lowerCAmelCase , """vision""" )
# Add new lines so that each line is no more than 80 characters.
lowerCAmelCase__ = textwrap.TextWrapper(width=80 )
lowerCAmelCase__ = wrapper.wrap(text=_lowerCAmelCase )
lowerCAmelCase__ = """\n""".join(_lowerCAmelCase )
if font_bytes is not None and font_path is None:
lowerCAmelCase__ = io.BytesIO(_lowerCAmelCase )
elif font_path is not None:
lowerCAmelCase__ = font_path
else:
lowerCAmelCase__ = hf_hub_download(_lowerCAmelCase , """Arial.TTF""" )
lowerCAmelCase__ = ImageFont.truetype(_lowerCAmelCase , encoding="""UTF-8""" , size=_lowerCAmelCase )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
lowerCAmelCase__ = ImageDraw.Draw(Image.new("""RGB""" , (1, 1) , _lowerCAmelCase ) )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = temp_draw.textbbox((0, 0) , _lowerCAmelCase , _lowerCAmelCase )
# Create the actual image with a bit of padding around the text.
lowerCAmelCase__ = text_width + left_padding + right_padding
lowerCAmelCase__ = text_height + top_padding + bottom_padding
lowerCAmelCase__ = Image.new("""RGB""" , (image_width, image_height) , _lowerCAmelCase )
lowerCAmelCase__ = ImageDraw.Draw(_lowerCAmelCase )
draw.text(xy=(left_padding, top_padding) , text=_lowerCAmelCase , fill=_lowerCAmelCase , font=_lowerCAmelCase )
return image
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
requires_backends(_lowerCAmelCase , """vision""" )
# Convert to PIL image if necessary
lowerCAmelCase__ = to_pil_image(_lowerCAmelCase )
lowerCAmelCase__ = render_text(_lowerCAmelCase , **_lowerCAmelCase )
lowerCAmelCase__ = max(header_image.width , image.width )
lowerCAmelCase__ = int(image.height * (new_width / image.width) )
lowerCAmelCase__ = int(header_image.height * (new_width / header_image.width) )
lowerCAmelCase__ = Image.new("""RGB""" , (new_width, new_height + new_header_height) , """white""" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
lowerCAmelCase__ = to_numpy_array(_lowerCAmelCase )
if infer_channel_dimension_format(_lowerCAmelCase ) == ChannelDimension.LAST:
lowerCAmelCase__ = to_channel_dimension_format(_lowerCAmelCase , ChannelDimension.LAST )
return new_image
class a_ ( __UpperCAmelCase ):
UpperCamelCase_ : List[Any] = ["flattened_patches"]
def __init__( self : str , snake_case__ : bool = True , snake_case__ : bool = True , snake_case__ : Dict[str, int] = None , snake_case__ : int = 2048 , snake_case__ : bool = False , **snake_case__ : int , ):
super().__init__(**_lowerCamelCase )
lowerCAmelCase__ = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = do_convert_rgb
lowerCAmelCase__ = max_patches
lowerCAmelCase__ = is_vqa
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : np.ndarray , snake_case__ : int , snake_case__ : dict , **snake_case__ : Union[str, Any] ):
requires_backends(self.extract_flattened_patches , """torch""" )
_check_torch_version()
# convert to torch
lowerCAmelCase__ = to_channel_dimension_format(_lowerCamelCase , ChannelDimension.FIRST )
lowerCAmelCase__ = torch.from_numpy(_lowerCamelCase )
lowerCAmelCase__ , lowerCAmelCase__ = patch_size["""height"""], patch_size["""width"""]
lowerCAmelCase__ , lowerCAmelCase__ = get_image_size(_lowerCamelCase )
# maximize scale s.t.
lowerCAmelCase__ = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
lowerCAmelCase__ = max(min(math.floor(scale * image_height / patch_height ) , _lowerCamelCase ) , 1 )
lowerCAmelCase__ = max(min(math.floor(scale * image_width / patch_width ) , _lowerCamelCase ) , 1 )
lowerCAmelCase__ = max(num_feasible_rows * patch_height , 1 )
lowerCAmelCase__ = max(num_feasible_cols * patch_width , 1 )
lowerCAmelCase__ = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="""bilinear""" , align_corners=_lowerCamelCase , antialias=_lowerCamelCase , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
lowerCAmelCase__ = torch_extract_patches(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
lowerCAmelCase__ = patches.shape
lowerCAmelCase__ = patches_shape[1]
lowerCAmelCase__ = patches_shape[2]
lowerCAmelCase__ = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
lowerCAmelCase__ = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
lowerCAmelCase__ = torch.arange(_lowerCamelCase ).reshape([rows, 1] ).repeat(1 , _lowerCamelCase ).reshape([rows * columns, 1] )
lowerCAmelCase__ = torch.arange(_lowerCamelCase ).reshape([1, columns] ).repeat(_lowerCamelCase , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
lowerCAmelCase__ = row_ids.to(torch.floataa )
lowerCAmelCase__ = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
lowerCAmelCase__ = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
lowerCAmelCase__ = torch.nn.functional.pad(_lowerCamelCase , [0, 0, 0, max_patches - (rows * columns)] ).float()
lowerCAmelCase__ = to_numpy_array(_lowerCamelCase )
return result
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : np.ndarray , snake_case__ : Optional[Union[str, ChannelDimension]] = None , **snake_case__ : str ):
if image.dtype == np.uinta:
lowerCAmelCase__ = image.astype(np.floataa )
# take mean across the whole `image`
lowerCAmelCase__ = np.mean(_lowerCamelCase )
lowerCAmelCase__ = np.std(_lowerCamelCase )
lowerCAmelCase__ = max(_lowerCamelCase , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase , **_lowerCamelCase )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : ImageInput , snake_case__ : Optional[str] = None , snake_case__ : bool = None , snake_case__ : Optional[bool] = None , snake_case__ : Optional[int] = None , snake_case__ : Optional[Dict[str, int]] = None , snake_case__ : Optional[Union[str, TensorType]] = None , snake_case__ : ChannelDimension = ChannelDimension.FIRST , **snake_case__ : int , ):
lowerCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowerCAmelCase__ = patch_size if patch_size is not None else self.patch_size
lowerCAmelCase__ = max_patches if max_patches is not None else self.max_patches
lowerCAmelCase__ = self.is_vqa
if kwargs.get("""data_format""" , _lowerCamelCase ) is not None:
raise ValueError("""data_format is not an accepted input as the outputs are """ )
lowerCAmelCase__ = make_list_of_images(_lowerCamelCase )
if not valid_images(_lowerCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowerCAmelCase__ = [convert_to_rgb(_lowerCamelCase ) for image in images]
# All transformations expect numpy arrays.
lowerCAmelCase__ = [to_numpy_array(_lowerCamelCase ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("""A header text must be provided for VQA models.""" )
lowerCAmelCase__ = kwargs.pop("""font_bytes""" , _lowerCamelCase )
lowerCAmelCase__ = kwargs.pop("""font_path""" , _lowerCamelCase )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
lowerCAmelCase__ = [header_text] * len(_lowerCamelCase )
lowerCAmelCase__ = [
render_header(_lowerCamelCase , header_text[i] , font_bytes=_lowerCamelCase , font_path=_lowerCamelCase )
for i, image in enumerate(_lowerCamelCase )
]
if do_normalize:
lowerCAmelCase__ = [self.normalize(image=_lowerCamelCase ) for image in images]
# convert to torch tensor and permute
lowerCAmelCase__ = [
self.extract_flattened_patches(image=_lowerCamelCase , max_patches=_lowerCamelCase , patch_size=_lowerCamelCase )
for image in images
]
# create attention mask in numpy
lowerCAmelCase__ = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
lowerCAmelCase__ = BatchFeature(
data={"""flattened_patches""": images, """attention_mask""": attention_masks} , tensor_type=_lowerCamelCase )
return encoded_outputs
| 701 | """simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : int ):
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=snake_case__ , )
assert hasattr(self , """env""" )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Optional[Any] ):
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase__ = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase__ = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase__ = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase__ = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=snake_case__ , instance_type=self.instance_type , debugger_hook_config=snake_case__ , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=snake_case__ , py_version="""py36""" , )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : str ):
TrainingJobAnalytics(snake_case__ ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : List[str] ):
# create estimator
lowerCAmelCase__ = self.create_estimator(snake_case__ )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , snake_case__ )
| 674 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class a_ ( lowerCamelCase__ ):
UpperCamelCase_ : Tuple = 42
class a_ ( lowerCamelCase__ , lowerCamelCase__ ):
@register_to_config
def __init__( self : int , snake_case__ : List[Any] = 16 , snake_case__ : List[Any] = 88 , snake_case__ : Optional[int] = None , snake_case__ : Tuple = None , snake_case__ : List[Any] = 1 , snake_case__ : Dict = 0.0 , snake_case__ : Optional[Any] = 32 , snake_case__ : Optional[Any] = None , snake_case__ : List[str] = False , snake_case__ : List[str] = None , snake_case__ : Any = "geglu" , snake_case__ : str = True , snake_case__ : str = True , ):
super().__init__()
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = attention_head_dim
lowerCAmelCase__ = num_attention_heads * attention_head_dim
lowerCAmelCase__ = in_channels
lowerCAmelCase__ = torch.nn.GroupNorm(num_groups=__lowerCamelCase , num_channels=__lowerCamelCase , eps=1E-6 , affine=__lowerCamelCase )
lowerCAmelCase__ = nn.Linear(__lowerCamelCase , __lowerCamelCase )
# 3. Define transformers blocks
lowerCAmelCase__ = nn.ModuleList(
[
BasicTransformerBlock(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , dropout=__lowerCamelCase , cross_attention_dim=__lowerCamelCase , activation_fn=__lowerCamelCase , attention_bias=__lowerCamelCase , double_self_attention=__lowerCamelCase , norm_elementwise_affine=__lowerCamelCase , )
for d in range(__lowerCamelCase )
] )
lowerCAmelCase__ = nn.Linear(__lowerCamelCase , __lowerCamelCase )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : int=None , snake_case__ : List[str]=None , snake_case__ : Any=None , snake_case__ : Dict=1 , snake_case__ : int=None , snake_case__ : List[Any] = True , ):
lowerCAmelCase__ = hidden_states.shape
lowerCAmelCase__ = batch_frames // num_frames
lowerCAmelCase__ = hidden_states
lowerCAmelCase__ = hidden_states[None, :].reshape(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowerCAmelCase__ = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
lowerCAmelCase__ = self.norm(__lowerCamelCase )
lowerCAmelCase__ = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , __lowerCamelCase , __lowerCamelCase )
lowerCAmelCase__ = self.proj_in(__lowerCamelCase )
# 2. Blocks
for block in self.transformer_blocks:
lowerCAmelCase__ = block(
__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , timestep=__lowerCamelCase , cross_attention_kwargs=__lowerCamelCase , class_labels=__lowerCamelCase , )
# 3. Output
lowerCAmelCase__ = self.proj_out(__lowerCamelCase )
lowerCAmelCase__ = (
hidden_states[None, None, :]
.reshape(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
lowerCAmelCase__ = hidden_states.reshape(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowerCAmelCase__ = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=__lowerCamelCase )
| 702 | """simple docstring"""
from math import pi, sqrt
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_71.5:
raise OverflowError("""math range error""" )
elif num - int(lowerCamelCase__ ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(lowerCamelCase__ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def _UpperCAmelCase ( ):
"""simple docstring"""
assert gamma(0.5 ) == sqrt(lowerCamelCase__ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__lowerCAmelCase : Dict = 1.0
while num:
__lowerCAmelCase : Any = float(input("Gamma of: "))
print(F"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| 674 | 0 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] = "▁"
__lowerCAmelCase : List[str] = {"vocab_file": "prophetnet.tokenizer"}
__lowerCAmelCase : int = {
"vocab_file": {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
),
}
}
__lowerCAmelCase : Optional[int] = {
"microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}
__lowerCAmelCase : int = {
"microsoft/xprophetnet-large-wiki100-cased": 5_12,
}
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = collections.OrderedDict()
with open(UpperCamelCase__ , """r""" , encoding="""utf-8""" ) as reader:
lowerCAmelCase__ = reader.readlines()
for index, token in enumerate(UpperCamelCase__ ):
lowerCAmelCase__ = token.rstrip("""\n""" )
lowerCAmelCase__ = index
return vocab
class a_ ( UpperCAmelCase_ ):
UpperCamelCase_ : List[str] = VOCAB_FILES_NAMES
UpperCamelCase_ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : Optional[int] = ["input_ids", "attention_mask"]
def __init__( self : List[str] , snake_case__ : Dict , snake_case__ : Union[str, Any]="[SEP]" , snake_case__ : Dict="[SEP]" , snake_case__ : int="[SEP]" , snake_case__ : List[Any]="[UNK]" , snake_case__ : List[Any]="[PAD]" , snake_case__ : Optional[Any]="[CLS]" , snake_case__ : Union[str, Any]="[MASK]" , snake_case__ : Optional[Dict[str, Any]] = None , **snake_case__ : List[Any] , ):
lowerCAmelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowercase , eos_token=_lowercase , sep_token=_lowercase , unk_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , sp_model_kwargs=self.sp_model_kwargs , **_lowercase , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
lowerCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowercase ) )
lowerCAmelCase__ = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
lowerCAmelCase__ = {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}
for i in range(10 ):
lowerCAmelCase__ = F"""[unused{i}]"""
lowerCAmelCase__ = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
lowerCAmelCase__ = 12
lowerCAmelCase__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(_lowercase )
def __getstate__( self : Optional[int] ):
lowerCAmelCase__ = self.__dict__.copy()
lowerCAmelCase__ = None
return state
def __setstate__( self : Optional[int] , snake_case__ : Dict ):
lowerCAmelCase__ = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCAmelCase__ = {}
lowerCAmelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None , snake_case__ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase , token_ids_a=_lowercase , already_has_special_tokens=_lowercase )
if token_ids_a is None:
return ([0] * len(_lowercase )) + [1]
return ([0] * len(_lowercase )) + [1] + ([0] * len(_lowercase )) + [1]
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
lowerCAmelCase__ = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _SCREAMING_SNAKE_CASE ( self : int ):
return len(self.sp_model ) + self.fairseq_offset
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = {self.convert_ids_to_tokens(_lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : str ):
return self.sp_model.encode(_lowercase , out_type=_lowercase )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : List[str] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowerCAmelCase__ = self.sp_model.PieceToId(_lowercase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , snake_case__ : str ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : List[Any] ):
lowerCAmelCase__ = """""".join(_lowercase ).replace(_lowercase , """ """ ).strip()
return out_string
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : str , snake_case__ : Optional[str] = None ):
if not os.path.isdir(_lowercase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCAmelCase__ = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowercase , """wb""" ) as fi:
lowerCAmelCase__ = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (out_vocab_file,)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
lowerCAmelCase__ = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 703 | """simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a_ :
def __init__( self : Optional[int] , snake_case__ : Optional[Any] , snake_case__ : Any=13 , snake_case__ : int=30 , snake_case__ : int=2 , snake_case__ : Union[str, Any]=3 , snake_case__ : Dict=True , snake_case__ : Optional[int]=True , snake_case__ : List[Any]=32 , snake_case__ : List[str]=2 , snake_case__ : Optional[Any]=4 , snake_case__ : Optional[int]=37 , snake_case__ : Tuple="gelu" , snake_case__ : str=0.1 , snake_case__ : Any=0.1 , snake_case__ : int=10 , snake_case__ : Dict=0.02 , snake_case__ : Union[str, Any]=3 , snake_case__ : str=None , snake_case__ : List[Any]=2 , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
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__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = scope
lowerCAmelCase__ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowerCAmelCase__ = (image_size // patch_size) ** 2
lowerCAmelCase__ = num_patches + 2
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
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=snake_case__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : int , snake_case__ : Optional[Any] , snake_case__ : List[str] ):
lowerCAmelCase__ = TFDeiTModel(config=snake_case__ )
lowerCAmelCase__ = model(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 , snake_case__ : Dict , snake_case__ : Dict , snake_case__ : Dict ):
lowerCAmelCase__ = TFDeiTForMaskedImageModeling(config=snake_case__ )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = TFDeiTForMaskedImageModeling(snake_case__ )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Union[str, Any] , snake_case__ : Any , snake_case__ : Tuple ):
lowerCAmelCase__ = self.type_sequence_label_size
lowerCAmelCase__ = TFDeiTForImageClassification(snake_case__ )
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = TFDeiTForImageClassification(snake_case__ )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class a_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Optional[Any] = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : Any = (
{
"feature-extraction": TFDeiTModel,
"image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[int] = False
UpperCamelCase_ : int = False
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = TFDeiTModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
lowerCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , tf.keras.layers.Dense ) )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : Union[str, Any] , snake_case__ : int , snake_case__ : Union[str, Any]=False ):
lowerCAmelCase__ = super()._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def _SCREAMING_SNAKE_CASE ( self : Any ):
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = TFDeiTModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Any ):
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=snake_case__ , return_tensors="""tf""" )
# forward pass
lowerCAmelCase__ = model(**snake_case__ )
# verify the logits
lowerCAmelCase__ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case__ )
lowerCAmelCase__ = tf.constant([-1.0266, 0.1912, -1.2861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , snake_case__ , atol=1E-4 ) )
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__SCREAMING_SNAKE_CASE ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
lowerCAmelCase__ = 0, 0
# length[i] shows the length of palindromic substring with center i
lowerCAmelCase__ = [1 for i in range(len(__SCREAMING_SNAKE_CASE ) )]
# for each character in new_string find corresponding palindromic string
lowerCAmelCase__ = 0
for j in range(len(__SCREAMING_SNAKE_CASE ) ):
lowerCAmelCase__ = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__SCREAMING_SNAKE_CASE )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
lowerCAmelCase__ = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
lowerCAmelCase__ = j - k + 1 # noqa: E741
lowerCAmelCase__ = j + k - 1
# update max_length and start position
if max_length < length[j]:
lowerCAmelCase__ = length[j]
lowerCAmelCase__ = j
# create that string
lowerCAmelCase__ = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704 | """simple docstring"""
from __future__ import annotations
from math import gcd
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 2 , lowerCamelCase__ = 1 , lowerCamelCase__ = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
return (pow(lowerCamelCase__ , 2 ) + step) % modulus
for _ in range(lowerCamelCase__ ):
# These track the position within the cycle detection logic.
lowerCAmelCase__ = seed
lowerCAmelCase__ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowerCAmelCase__ = gcd(hare - tortoise , lowerCamelCase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowerCAmelCase__ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
__lowerCAmelCase : List[str] = parser.parse_args()
__lowerCAmelCase : Dict = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"{args.num} is probably prime")
else:
__lowerCAmelCase : List[str] = args.num // divisor
print(F"{args.num} = {divisor} * {quotient}")
| 674 | 0 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowerCAmelCase__ = TaConfig.from_json_file(lowerCAmelCase_ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = TaForConditionalGeneration(lowerCAmelCase_ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
__lowerCAmelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 705 | """simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = TapasConfig.from_json_file(lowerCamelCase__ )
# set absolute/relative position embeddings parameter
lowerCAmelCase__ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "WTQ":
# run_task_main.py hparams
lowerCAmelCase__ = 4
lowerCAmelCase__ = True
# hparam_utils.py hparams
lowerCAmelCase__ = 0.66_46_94
lowerCAmelCase__ = 0.20_79_51
lowerCAmelCase__ = 0.12_11_94
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = 0.0_35_25_13
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
lowerCAmelCase__ = 4
lowerCAmelCase__ = False
# hparam_utils.py hparams
lowerCAmelCase__ = 36.45_19
lowerCAmelCase__ = 0.90_34_21
lowerCAmelCase__ = 2_22.0_88
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = 0.76_31_41
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "TABFACT":
lowerCAmelCase__ = TapasForSequenceClassification(config=lowerCamelCase__ )
elif task == "MLM":
lowerCAmelCase__ = TapasForMaskedLM(config=lowerCamelCase__ )
elif task == "INTERMEDIATE_PRETRAINING":
lowerCAmelCase__ = TapasModel(config=lowerCamelCase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowerCamelCase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
lowerCAmelCase__ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" , model_max_length=512 )
tokenizer.save_pretrained(lowerCamelCase__ )
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
)
parser.add_argument(
"--reset_position_index_per_cell",
default=False,
action="store_true",
help="Whether to use relative position embeddings or not. Defaults to True.",
)
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--tapas_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained TAPAS 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."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 674 | 0 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a_ ( __lowercase ):
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case__ , """embed_dim""" ) )
self.parent.assertTrue(hasattr(snake_case__ , """num_heads""" ) )
class a_ :
def __init__( self : List[Any] , snake_case__ : Dict , snake_case__ : Optional[int]=13 , snake_case__ : Any=64 , snake_case__ : List[str]=3 , snake_case__ : int=[16, 48, 96] , snake_case__ : str=[1, 3, 6] , snake_case__ : Optional[Any]=[1, 2, 10] , snake_case__ : Dict=[7, 3, 3] , snake_case__ : List[Any]=[4, 2, 2] , snake_case__ : Optional[Any]=[2, 1, 1] , snake_case__ : Tuple=[2, 2, 2] , snake_case__ : Union[str, Any]=[False, False, True] , snake_case__ : int=[0.0, 0.0, 0.0] , snake_case__ : str=0.02 , snake_case__ : str=1E-12 , snake_case__ : Optional[int]=True , snake_case__ : List[str]=True , snake_case__ : List[Any]=2 , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_sizes
lowerCAmelCase__ = patch_stride
lowerCAmelCase__ = patch_padding
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = embed_dim
lowerCAmelCase__ = num_heads
lowerCAmelCase__ = stride_kv
lowerCAmelCase__ = depth
lowerCAmelCase__ = cls_token
lowerCAmelCase__ = attention_drop_rate
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = layer_norm_eps
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : List[Any] , snake_case__ : Optional[Any] , snake_case__ : List[Any] ):
lowerCAmelCase__ = CvtModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase__ = model(snake_case__ )
lowerCAmelCase__ = (self.image_size, self.image_size)
lowerCAmelCase__ = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
lowerCAmelCase__ = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
lowerCAmelCase__ = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Tuple , snake_case__ : Optional[Any] , snake_case__ : Optional[Any] ):
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = CvtForImageClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class a_ ( __lowercase , __lowercase , unittest.TestCase ):
UpperCamelCase_ : Optional[int] = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
UpperCamelCase_ : Tuple = (
{"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
if is_torch_available()
else {}
)
UpperCamelCase_ : Optional[int] = False
UpperCamelCase_ : List[Any] = False
UpperCamelCase_ : Any = False
UpperCamelCase_ : int = False
UpperCamelCase_ : Any = False
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = CvtModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
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 _SCREAMING_SNAKE_CASE ( self : int ):
return
@unittest.skip(reason="""Cvt does not output attentions""" )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def _SCREAMING_SNAKE_CASE ( self : int ):
pass
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
def check_hidden_states_output(snake_case__ : str , snake_case__ : str , snake_case__ : List[str] ):
lowerCAmelCase__ = model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(snake_case__ , snake_case__ ) )
lowerCAmelCase__ = outputs.hidden_states
lowerCAmelCase__ = len(self.model_tester.depth )
self.assertEqual(len(snake_case__ ) , snake_case__ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
pass
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = CvtModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def _SCREAMING_SNAKE_CASE ( self : int ):
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case__ )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=snake_case__ , return_tensors="""pt""" ).to(snake_case__ )
# forward pass
with torch.no_grad():
lowerCAmelCase__ = model(**snake_case__ )
# verify the logits
lowerCAmelCase__ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case__ )
lowerCAmelCase__ = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case__ , atol=1E-4 ) )
| 706 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ = 50 ):
"""simple docstring"""
lowerCAmelCase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F"{solution() = }")
| 674 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase : Dict = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
"self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
"self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
"self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
"self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
"self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
"self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
"self_attn.rotary_emb": "encoder.embed_positions",
"self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
"conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
"conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
"conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
"conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
"conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
"ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
"ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
"ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
"ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
"ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
"ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
"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 : Dict = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
for attribute in key.split(""".""" ):
lowerCAmelCase__ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
lowerCAmelCase__ = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
lowerCAmelCase__ = 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":
lowerCAmelCase__ = value
elif weight_type == "weight_g":
lowerCAmelCase__ = value
elif weight_type == "weight_v":
lowerCAmelCase__ = value
elif weight_type == "bias":
lowerCAmelCase__ = value
elif weight_type == "running_mean":
lowerCAmelCase__ = value
elif weight_type == "running_var":
lowerCAmelCase__ = value
elif weight_type == "num_batches_tracked":
lowerCAmelCase__ = value
elif weight_type == "inv_freq":
lowerCAmelCase__ = value
else:
lowerCAmelCase__ = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ = fairseq_model.state_dict()
lowerCAmelCase__ = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowerCAmelCase__ = False
if "conv_layers" in name:
load_conv_layer(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == """group""" , )
lowerCAmelCase__ = True
else:
for key, mapped_key in MAPPING.items():
lowerCAmelCase__ = """wav2vec2_conformer.""" + 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]:
lowerCAmelCase__ = True
if "*" in mapped_key:
lowerCAmelCase__ = name.split(_SCREAMING_SNAKE_CASE )[0].split(""".""" )[-2]
lowerCAmelCase__ = mapped_key.replace("""*""" , _SCREAMING_SNAKE_CASE )
if "pos_bias_u" in name:
lowerCAmelCase__ = None
elif "pos_bias_v" in name:
lowerCAmelCase__ = None
elif "weight_g" in name:
lowerCAmelCase__ = """weight_g"""
elif "weight_v" in name:
lowerCAmelCase__ = """weight_v"""
elif "bias" in name:
lowerCAmelCase__ = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowerCAmelCase__ = """weight"""
elif "running_mean" in name:
lowerCAmelCase__ = """running_mean"""
elif "inv_freq" in name:
lowerCAmelCase__ = """inv_freq"""
elif "running_var" in name:
lowerCAmelCase__ = """running_var"""
elif "num_batches_tracked" in name:
lowerCAmelCase__ = """num_batches_tracked"""
else:
lowerCAmelCase__ = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(f"""Unused weights: {unused_weights}""" )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = full_name.split("""conv_layers.""" )[-1]
lowerCAmelCase__ = name.split(""".""" )
lowerCAmelCase__ = int(items[0] )
lowerCAmelCase__ = 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.""" )
lowerCAmelCase__ = 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.""" )
lowerCAmelCase__ = 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.""" )
lowerCAmelCase__ = 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.""" )
lowerCAmelCase__ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=True ):
"""simple docstring"""
if config_path is not None:
lowerCAmelCase__ = WavaVecaConformerConfig.from_pretrained(_SCREAMING_SNAKE_CASE , hidden_act="""swish""" )
else:
lowerCAmelCase__ = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowerCAmelCase__ = """rotary"""
if is_finetuned:
if dict_path:
lowerCAmelCase__ = Dictionary.load(_SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCAmelCase__ = target_dict.pad_index
lowerCAmelCase__ = target_dict.bos_index
lowerCAmelCase__ = target_dict.eos_index
lowerCAmelCase__ = len(target_dict.symbols )
lowerCAmelCase__ = os.path.join(_SCREAMING_SNAKE_CASE , """vocab.json""" )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE ) )
return
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = target_dict.indices
# fairseq has the <pad> and <s> switched
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = WavaVecaCTCTokenizer(
_SCREAMING_SNAKE_CASE , 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=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ = True if config.feat_extract_norm == """layer""" else False
lowerCAmelCase__ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ = WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = WavaVecaConformerForCTC(_SCREAMING_SNAKE_CASE )
else:
lowerCAmelCase__ = WavaVecaConformerForPreTraining(_SCREAMING_SNAKE_CASE )
if is_finetuned:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
lowerCAmelCase__ = argparse.Namespace(task="""audio_pretraining""" )
lowerCAmelCase__ = fairseq.tasks.setup_task(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = model[0].eval()
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , not is_finetuned )
hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__lowerCAmelCase : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
__lowerCAmelCase : Optional[Any] = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 707 | """simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse("0.8.3"):
raise Exception("requires gluonnlp == 0.8.3")
if version.parse(mx.__version__) != version.parse("1.5.0"):
raise Exception("requires mxnet == 1.5.0")
logging.set_verbosity_info()
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Any = "The Nymphenburg Palace is a beautiful palace in Munich!"
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {
"""attention_cell""": """multi_head""",
"""num_layers""": 4,
"""units""": 1024,
"""hidden_size""": 768,
"""max_length""": 512,
"""num_heads""": 8,
"""scaled""": True,
"""dropout""": 0.1,
"""use_residual""": True,
"""embed_size""": 1024,
"""embed_dropout""": 0.1,
"""word_embed""": None,
"""layer_norm_eps""": 1e-5,
"""token_type_vocab_size""": 2,
}
lowerCAmelCase__ = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
lowerCAmelCase__ = BERTEncoder(
attention_cell=predefined_args["""attention_cell"""] , num_layers=predefined_args["""num_layers"""] , units=predefined_args["""units"""] , hidden_size=predefined_args["""hidden_size"""] , max_length=predefined_args["""max_length"""] , num_heads=predefined_args["""num_heads"""] , scaled=predefined_args["""scaled"""] , dropout=predefined_args["""dropout"""] , output_attention=lowerCamelCase__ , output_all_encodings=lowerCamelCase__ , use_residual=predefined_args["""use_residual"""] , activation=predefined_args.get("""activation""" , """gelu""" ) , layer_norm_eps=predefined_args.get("""layer_norm_eps""" , lowerCamelCase__ ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
lowerCAmelCase__ = """openwebtext_ccnews_stories_books_cased"""
# Specify download folder to Gluonnlp's vocab
lowerCAmelCase__ = os.path.join(get_home_dir() , """models""" )
lowerCAmelCase__ = _load_vocab(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , cls=lowerCamelCase__ )
lowerCAmelCase__ = nlp.model.BERTModel(
lowerCamelCase__ , len(lowerCamelCase__ ) , units=predefined_args["""units"""] , embed_size=predefined_args["""embed_size"""] , embed_dropout=predefined_args["""embed_dropout"""] , word_embed=predefined_args["""word_embed"""] , use_pooler=lowerCamelCase__ , use_token_type_embed=lowerCamelCase__ , token_type_vocab_size=predefined_args["""token_type_vocab_size"""] , use_classifier=lowerCamelCase__ , use_decoder=lowerCamelCase__ , )
original_bort.load_parameters(lowerCamelCase__ , cast_dtype=lowerCamelCase__ , ignore_extra=lowerCamelCase__ )
lowerCAmelCase__ = original_bort._collect_params_with_prefix()
# Build our config 🤗
lowerCAmelCase__ = {
"""architectures""": ["""BertForMaskedLM"""],
"""attention_probs_dropout_prob""": predefined_args["""dropout"""],
"""hidden_act""": """gelu""",
"""hidden_dropout_prob""": predefined_args["""dropout"""],
"""hidden_size""": predefined_args["""embed_size"""],
"""initializer_range""": 0.02,
"""intermediate_size""": predefined_args["""hidden_size"""],
"""layer_norm_eps""": predefined_args["""layer_norm_eps"""],
"""max_position_embeddings""": predefined_args["""max_length"""],
"""model_type""": """bort""",
"""num_attention_heads""": predefined_args["""num_heads"""],
"""num_hidden_layers""": predefined_args["""num_layers"""],
"""pad_token_id""": 1, # 2 = BERT, 1 = RoBERTa
"""type_vocab_size""": 1, # 2 = BERT, 1 = RoBERTa
"""vocab_size""": len(lowerCamelCase__ ),
}
lowerCAmelCase__ = BertConfig.from_dict(lowerCamelCase__ )
lowerCAmelCase__ = BertForMaskedLM(lowerCamelCase__ )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(lowerCamelCase__ ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = hf_param.shape
lowerCAmelCase__ = to_torch(params[gluon_param] )
lowerCAmelCase__ = gluon_param.shape
assert (
shape_hf == shape_gluon
), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""
return gluon_param
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , """word_embed.0.weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , """encoder.position_weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , """encoder.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , """encoder.layer_norm.gamma""" )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
lowerCAmelCase__ = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
lowerCAmelCase__ = hf_bort_model.bert.encoder.layer[i]
# self attention
lowerCAmelCase__ = layer.attention.self
lowerCAmelCase__ = check_and_map_params(
self_attn.key.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.key.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight""" )
# self attention output
lowerCAmelCase__ = layer.attention.output
lowerCAmelCase__ = check_and_map_params(
self_output.dense.bias , f"""encoder.transformer_cells.{i}.proj.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_output.dense.weight , f"""encoder.transformer_cells.{i}.proj.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.layer_norm.gamma""" )
# intermediate
lowerCAmelCase__ = layer.intermediate
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias""" )
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight""" )
# output
lowerCAmelCase__ = layer.output
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma""" )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
lowerCAmelCase__ = RobertaTokenizer.from_pretrained("""roberta-base""" )
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ )["""input_ids"""]
# Get gluon output
lowerCAmelCase__ = mx.nd.array([input_ids] )
lowerCAmelCase__ = original_bort(inputs=lowerCamelCase__ , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(lowerCamelCase__ )
lowerCAmelCase__ = BertModel.from_pretrained(lowerCamelCase__ )
hf_bort_model.eval()
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ , return_tensors="""pt""" )
lowerCAmelCase__ = hf_bort_model(**lowerCamelCase__ )[0]
lowerCAmelCase__ = output_gluon[0].asnumpy()
lowerCAmelCase__ = output_hf[0].detach().numpy()
lowerCAmelCase__ = np.max(np.abs(hf_layer - gluon_layer ) ).item()
lowerCAmelCase__ = np.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-3 )
if success:
print("""✔️ Both model do output the same tensors""" )
else:
print("""❌ Both model do **NOT** output the same tensors""" )
print("""Absolute difference is:""" , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : str = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 708 | """simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self : Optional[int] ):
lowerCAmelCase__ = """"""
lowerCAmelCase__ = """"""
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
lowerCAmelCase__ = 256
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Union[str, Any] ):
lowerCAmelCase__ = cva.imread(snake_case__ , 0 )
lowerCAmelCase__ = copy.deepcopy(self.img )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="""x""" )
lowerCAmelCase__ = np.sum(snake_case__ )
for i in range(len(snake_case__ ) ):
lowerCAmelCase__ = x[i] / self.k
self.sk += prk
lowerCAmelCase__ = (self.L - 1) * self.sk
if self.rem != 0:
lowerCAmelCase__ = int(last % last )
lowerCAmelCase__ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(snake_case__ )
lowerCAmelCase__ = int(np.ma.count(self.img ) / self.img[1].size )
lowerCAmelCase__ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCAmelCase__ = self.img[j][i]
if num != self.last_list[num]:
lowerCAmelCase__ = self.last_list[num]
cva.imwrite("""output_data/output.jpg""" , self.img )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
cva.imshow("""Output-Image""" , self.img )
cva.imshow("""Input-Image""" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
__lowerCAmelCase : Dict = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
__lowerCAmelCase : Optional[int] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 674 | 0 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = AutoConfig.from_pretrained(lowerCAmelCase__ )
lowerCAmelCase__ = FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__ )
lowerCAmelCase__ = checkpoints.load_tax_checkpoint(lowerCAmelCase__ )
lowerCAmelCase__ = 'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']
if config.model_type == "t5":
lowerCAmelCase__ = 'SelfAttention'
if config.model_type == "longt5" and config.encoder_attention_type == "local":
lowerCAmelCase__ = 'LocalSelfAttention'
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ = 'TransientGlobalSelfAttention'
else:
raise ValueError(
"""Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`"""
""" attribute with a value from [\'local\', \'transient-global].""" )
# Encoder
for layer_index in range(config.num_layers ):
lowerCAmelCase__ = f"""layers_{str(lowerCAmelCase__ )}"""
# Self-Attention
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']
# Layer Normalization
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']
if split_mlp_wi:
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
else:
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
lowerCAmelCase__ = tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
lowerCAmelCase__ = flax_model.params['encoder']['block'][str(lowerCAmelCase__ )]['layer']
lowerCAmelCase__ = tax_attention_key
lowerCAmelCase__ = tax_attention_out
lowerCAmelCase__ = tax_attention_query
lowerCAmelCase__ = tax_attention_value
lowerCAmelCase__ = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ = tax_global_layer_norm
if split_mlp_wi:
lowerCAmelCase__ = tax_mlp_wi_a
lowerCAmelCase__ = tax_mlp_wi_a
else:
lowerCAmelCase__ = tax_mlp_wi
lowerCAmelCase__ = tax_mlp_wo
lowerCAmelCase__ = tax_mlp_layer_norm
lowerCAmelCase__ = flax_model_encoder_layer_block
# Only for layer 0:
lowerCAmelCase__ = tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
lowerCAmelCase__ = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowerCAmelCase__ = tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
lowerCAmelCase__ = tax_encoder_global_rel_embedding
# Assigning
lowerCAmelCase__ = tax_model['target']['encoder']['encoder_norm']['scale']
lowerCAmelCase__ = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
lowerCAmelCase__ = f"""layers_{str(lowerCAmelCase__ )}"""
# Self-Attention
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']
# Layer Normalization
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
'scale'
]
# Encoder-Decoder-Attention
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
lowerCAmelCase__ = tax_enc_dec_attention_module['key']['kernel']
lowerCAmelCase__ = tax_enc_dec_attention_module['out']['kernel']
lowerCAmelCase__ = tax_enc_dec_attention_module['query']['kernel']
lowerCAmelCase__ = tax_enc_dec_attention_module['value']['kernel']
# Layer Normalization
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']
# MLP
if split_mlp_wi:
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
else:
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
lowerCAmelCase__ = tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
lowerCAmelCase__ = flax_model.params['decoder']['block'][str(lowerCAmelCase__ )]['layer']
lowerCAmelCase__ = tax_attention_key
lowerCAmelCase__ = tax_attention_out
lowerCAmelCase__ = tax_attention_query
lowerCAmelCase__ = tax_attention_value
lowerCAmelCase__ = tax_pre_attention_layer_norm
lowerCAmelCase__ = tax_enc_dec_attention_key
lowerCAmelCase__ = tax_enc_dec_attention_out
lowerCAmelCase__ = tax_enc_dec_attention_query
lowerCAmelCase__ = tax_enc_dec_attention_value
lowerCAmelCase__ = tax_cross_layer_norm
if split_mlp_wi:
lowerCAmelCase__ = tax_mlp_wi_a
lowerCAmelCase__ = tax_mlp_wi_a
else:
lowerCAmelCase__ = tax_mlp_wi
lowerCAmelCase__ = tax_mlp_wo
lowerCAmelCase__ = txa_mlp_layer_norm
lowerCAmelCase__ = flax_model_decoder_layer_block
# Decoder Normalization
lowerCAmelCase__ = tax_model['target']['decoder']['decoder_norm']['scale']
lowerCAmelCase__ = txa_decoder_norm
# Only for layer 0:
lowerCAmelCase__ = tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
lowerCAmelCase__ = tax_decoder_rel_embedding
# Token Embeddings
lowerCAmelCase__ = tax_model['target']['token_embedder']['embedding']
lowerCAmelCase__ = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
lowerCAmelCase__ = tax_model['target']['decoder']['logits_dense']['kernel']
flax_model.save_pretrained(lowerCAmelCase__ )
print("""T5X Model was sucessfully converted!""" )
if __name__ == "__main__":
__lowerCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
)
parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
parser.add_argument(
"--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
)
__lowerCAmelCase : Dict = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 709 | """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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : List[str] = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
UpperCamelCase_ : str = "CIDAS/clipseg-rd64-refined"
UpperCamelCase_ : Any = "image_segmenter"
UpperCamelCase_ : Optional[Any] = CLIPSegForImageSegmentation
UpperCamelCase_ : List[str] = ["image", "text"]
UpperCamelCase_ : int = ["image"]
def __init__( self : Tuple , *snake_case__ : str , **snake_case__ : Optional[Any] ):
requires_backends(self , ["""vision"""] )
super().__init__(*snake_case__ , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : "Image" , snake_case__ : str ):
return self.pre_processor(text=[label] , images=[image] , padding=snake_case__ , return_tensors="""pt""" )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Tuple ):
with torch.no_grad():
lowerCAmelCase__ = self.model(**snake_case__ ).logits
return logits
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : List[Any] ):
lowerCAmelCase__ = outputs.cpu().detach().numpy()
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 674 | 0 |
"""simple docstring"""
import math
def _UpperCAmelCase ( lowerCamelCase__ = 100 ):
"""simple docstring"""
lowerCAmelCase__ = sum(i * i for i in range(1 , n + 1 ) )
lowerCAmelCase__ = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(F"{solution() = }")
| 710 | """simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = LayoutLMTokenizer
UpperCamelCase_ : List[Any] = LayoutLMTokenizerFast
UpperCamelCase_ : Dict = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
lowerCAmelCase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ = 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 _SCREAMING_SNAKE_CASE ( self : int , **snake_case__ : Union[str, Any] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Tuple ):
lowerCAmelCase__ = """UNwant\u00E9d,running"""
lowerCAmelCase__ = """unwanted, running"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase__ = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(snake_case__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [7, 4, 5, 10, 8, 9] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
pass
| 674 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] = torch.device("cpu")
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase__ = Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__ ).raw )
return im
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02] )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = dct.pop(lowerCamelCase__ )
lowerCAmelCase__ = val
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = []
for k in state_dict.keys():
lowerCAmelCase__ = k
if ".pwconv" in k:
lowerCAmelCase__ = k_new.replace(""".pwconv""" , """.point_wise_conv""" )
if ".dwconv" in k:
lowerCAmelCase__ = k_new.replace(""".dwconv""" , """.depth_wise_conv""" )
if ".Proj." in k:
lowerCAmelCase__ = k_new.replace(""".Proj.""" , """.proj.""" )
if "patch_embed" in k_new:
lowerCAmelCase__ = k_new.replace("""patch_embed""" , """swiftformer.patch_embed.patch_embedding""" )
if "network" in k_new:
lowerCAmelCase__ = k_new.split(""".""" )
if ls[2].isdigit():
lowerCAmelCase__ = """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:] )
else:
lowerCAmelCase__ = k_new.replace("""network""" , """swiftformer.encoder.network""" )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
lowerCAmelCase__ = 1000
lowerCAmelCase__ = """huggingface/label-files"""
lowerCAmelCase__ = """imagenet-1k-id2label.json"""
lowerCAmelCase__ = json.load(open(hf_hub_download(lowerCamelCase__ , lowerCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
lowerCAmelCase__ = {int(lowerCamelCase__ ): v for k, v in idalabel.items()}
lowerCAmelCase__ = idalabel
lowerCAmelCase__ = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
lowerCAmelCase__ = [3, 3, 6, 4]
lowerCAmelCase__ = [48, 56, 112, 220]
elif swiftformer_name == "swiftformer_s":
lowerCAmelCase__ = [3, 3, 9, 6]
lowerCAmelCase__ = [48, 64, 168, 224]
elif swiftformer_name == "swiftformer_l1":
lowerCAmelCase__ = [4, 3, 10, 5]
lowerCAmelCase__ = [48, 96, 192, 384]
elif swiftformer_name == "swiftformer_l3":
lowerCAmelCase__ = [4, 4, 12, 6]
lowerCAmelCase__ = [64, 128, 320, 512]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith("""https""" ):
lowerCAmelCase__ = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location="""cpu""" , check_hash=lowerCamelCase__ )
else:
lowerCAmelCase__ = torch.load(lowerCamelCase__ , map_location="""cpu""" )
lowerCAmelCase__ = checkpoint
lowerCAmelCase__ = create_rename_keys(lowerCamelCase__ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# load HuggingFace model
lowerCAmelCase__ = SwiftFormerForImageClassification(lowerCamelCase__ ).eval()
hf_model.load_state_dict(lowerCamelCase__ )
# prepare test inputs
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = ViTImageProcessor.from_pretrained("""preprocessor_config""" )
lowerCAmelCase__ = processor(images=lowerCamelCase__ , return_tensors="""pt""" )
# compare outputs from both models
lowerCAmelCase__ = get_expected_output(lowerCamelCase__ )
lowerCAmelCase__ = hf_model(inputs["""pixel_values"""] ).logits
assert hf_logits.shape == torch.Size([1, 1000] )
assert torch.allclose(hf_logits[0, 0:5] , lowerCamelCase__ , atol=1e-3 )
Path(lowerCamelCase__ ).mkdir(exist_ok=lowerCamelCase__ )
print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""" )
hf_model.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swiftformer_name",
default="swiftformer_xs",
choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
type=str,
help="Name of the SwiftFormer model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="./converted_outputs/",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")
__lowerCAmelCase : Tuple = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 711 | """simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase : Any = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
def __init__( self : List[str] , snake_case__ : int = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
lowerCAmelCase__ = primes[group]["""prime"""]
lowerCAmelCase__ = primes[group]["""generator"""]
lowerCAmelCase__ = int(hexlify(urandom(32 ) ) , base=16 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
return hex(self.__private_key )[2:]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = pow(self.generator , self.__private_key , self.prime )
return hex(snake_case__ )[2:]
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(snake_case__ , (self.prime - 1) // 2 , self.prime ) == 1
)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : str ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
if not self.is_valid_public_key(snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , self.__private_key , self.prime )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : int , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(snake_case__ , (prime - 1) // 2 , snake_case__ ) == 1
)
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : str , snake_case__ : str , snake_case__ : int = 14 ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(snake_case__ , snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , snake_case__ , snake_case__ )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=__UpperCAmelCase ):
UpperCamelCase_ : Union[str, Any] = ["keras_nlp"]
def __init__( self : Dict , *snake_case__ : List[str] , **snake_case__ : Tuple ):
requires_backends(self , ["""keras_nlp"""] )
| 712 | """simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
lowerCAmelCase__ = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = weights[0][0][0]
lowerCAmelCase__ = np.asarray(layer_norm_a[0] )
lowerCAmelCase__ = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# lsh weights + output
lowerCAmelCase__ = weights[0][1]
if len(lowerCamelCase__ ) < 4:
set_layer_weights_in_torch_lsh(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
else:
set_layer_weights_in_torch_local(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
# intermediate weighs
lowerCAmelCase__ = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowerCamelCase__ ) == 4:
lowerCAmelCase__ = intermediate_weights[2]
# layernorm 2
lowerCAmelCase__ = np.asarray(intermediate_weights[0][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# intermediate dense
lowerCAmelCase__ = np.asarray(intermediate_weights[1][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
# intermediate out
lowerCAmelCase__ = np.asarray(intermediate_weights[4][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = torch_model.reformer
# word embeds
lowerCAmelCase__ = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCamelCase__ ) , )
if isinstance(weights[3] , lowerCamelCase__ ):
lowerCAmelCase__ = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowerCAmelCase__ = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f"""{position_embeddings[emb_idx]} emb does not match"""
lowerCAmelCase__ = nn.Parameter(torch.tensor(lowerCamelCase__ ) )
lowerCAmelCase__ = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowerCamelCase__ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowerCAmelCase__ = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# output layer norm
lowerCAmelCase__ = np.asarray(weights[7][0] )
lowerCAmelCase__ = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# output embeddings
lowerCAmelCase__ = np.asarray(weights[9][0] )
lowerCAmelCase__ = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = ReformerConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = ReformerModelWithLMHead(lowerCamelCase__ )
with open(lowerCamelCase__ , """rb""" ) as f:
lowerCAmelCase__ = pickle.load(lowerCamelCase__ )["""weights"""]
set_model_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , config.hidden_size )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
__lowerCAmelCase : str = {value: key for key, value in encode_dict.items()}
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = ''''''
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if set(a_ ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only \'A\', \'B\' and spaces""" )
lowerCAmelCase__ = ''''''
for word in coded.split():
while len(a_ ) != 0:
decoded += decode_dict[word[:5]]
lowerCAmelCase__ = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 713 | """simple docstring"""
import os
from math import logaa
def _UpperCAmelCase ( lowerCamelCase__ = "base_exp.txt" ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) ):
lowerCAmelCase__ , lowerCAmelCase__ = list(map(lowerCamelCase__ , line.split(""",""" ) ) )
if x * logaa(lowerCamelCase__ ) > largest:
lowerCAmelCase__ = x * logaa(lowerCamelCase__ )
lowerCAmelCase__ = i + 1
return result
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__lowerCAmelCase : List[Any] = logging.get_logger("transformers.models.speecht5")
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
hf_model.apply_weight_norm()
lowerCAmelCase__ = checkpoint["""input_conv.weight_g"""]
lowerCAmelCase__ = checkpoint["""input_conv.weight_v"""]
lowerCAmelCase__ = checkpoint["""input_conv.bias"""]
for i in range(len(config.upsample_rates ) ):
lowerCAmelCase__ = checkpoint[f"""upsamples.{i}.1.weight_g"""]
lowerCAmelCase__ = checkpoint[f"""upsamples.{i}.1.weight_v"""]
lowerCAmelCase__ = checkpoint[f"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
lowerCAmelCase__ = checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
lowerCAmelCase__ = checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
lowerCAmelCase__ = checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]
lowerCAmelCase__ = checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
lowerCAmelCase__ = checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
lowerCAmelCase__ = checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]
lowerCAmelCase__ = checkpoint["""output_conv.1.weight_g"""]
lowerCAmelCase__ = checkpoint["""output_conv.1.weight_v"""]
lowerCAmelCase__ = checkpoint["""output_conv.1.bias"""]
hf_model.remove_weight_norm()
@torch.no_grad()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , ):
"""simple docstring"""
if config_path is not None:
lowerCAmelCase__ = SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase__ )
else:
lowerCAmelCase__ = SpeechTaHifiGanConfig()
lowerCAmelCase__ = SpeechTaHifiGan(UpperCAmelCase__ )
lowerCAmelCase__ = torch.load(UpperCAmelCase__ )
load_weights(orig_checkpoint["""model"""]["""generator"""] , UpperCAmelCase__ , UpperCAmelCase__ )
lowerCAmelCase__ = np.load(UpperCAmelCase__ )
lowerCAmelCase__ = stats[0].reshape(-1 )
lowerCAmelCase__ = stats[1].reshape(-1 )
lowerCAmelCase__ = torch.from_numpy(UpperCAmelCase__ ).float()
lowerCAmelCase__ = torch.from_numpy(UpperCAmelCase__ ).float()
model.save_pretrained(UpperCAmelCase__ )
if repo_id:
print("""Pushing to the hub...""" )
model.push_to_hub(UpperCAmelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
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."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 714 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
while b:
lowerCAmelCase__ , lowerCAmelCase__ = b, a % b
return a
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(lowerCamelCase__ , a % b )
def _UpperCAmelCase ( ):
"""simple docstring"""
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 674 | 0 |
"""simple docstring"""
class a_ :
def __init__( self : Optional[int] , snake_case__ : Dict , snake_case__ : Optional[Any] , snake_case__ : List[Any] ):
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = graph
self._normalize_graph(__UpperCamelCase , __UpperCamelCase )
lowerCAmelCase__ = len(__UpperCamelCase )
lowerCAmelCase__ = None
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any] , snake_case__ : List[str] ):
if sources is int:
lowerCAmelCase__ = [sources]
if sinks is int:
lowerCAmelCase__ = [sinks]
if len(__UpperCamelCase ) == 0 or len(__UpperCamelCase ) == 0:
return
lowerCAmelCase__ = sources[0]
lowerCAmelCase__ = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(__UpperCamelCase ) > 1 or len(__UpperCamelCase ) > 1:
lowerCAmelCase__ = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
lowerCAmelCase__ = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
lowerCAmelCase__ = max_input_flow
lowerCAmelCase__ = 0
lowerCAmelCase__ = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
lowerCAmelCase__ = max_input_flow
lowerCAmelCase__ = size - 1
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
if self.maximum_flow_algorithm is None:
raise Exception("""You need to set maximum flow algorithm before.""" )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : List[str] ):
lowerCAmelCase__ = algorithm(self )
class a_ :
def __init__( self : Optional[Any] , snake_case__ : int ):
lowerCAmelCase__ = flow_network
lowerCAmelCase__ = flow_network.verticesCount
lowerCAmelCase__ = flow_network.sourceIndex
lowerCAmelCase__ = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
lowerCAmelCase__ = flow_network.graph
lowerCAmelCase__ = False
def _SCREAMING_SNAKE_CASE ( self : int ):
if not self.executed:
self._algorithm()
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : int ):
pass
class a_ ( __UpperCamelCase ):
def __init__( self : Union[str, Any] , snake_case__ : Tuple ):
super().__init__(__UpperCamelCase )
# use this to save your result
lowerCAmelCase__ = -1
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
if not self.executed:
raise Exception("""You should execute algorithm before using its result!""" )
return self.maximum_flow
class a_ ( __UpperCamelCase ):
def __init__( self : Dict , snake_case__ : List[str] ):
super().__init__(__UpperCamelCase )
lowerCAmelCase__ = [[0] * self.verticies_count for i in range(self.verticies_count )]
lowerCAmelCase__ = [0] * self.verticies_count
lowerCAmelCase__ = [0] * self.verticies_count
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
lowerCAmelCase__ = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
lowerCAmelCase__ = 0
while i < len(__UpperCamelCase ):
lowerCAmelCase__ = vertices_list[i]
lowerCAmelCase__ = self.heights[vertex_index]
self.process_vertex(__UpperCamelCase )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(__UpperCamelCase ) )
lowerCAmelCase__ = 0
else:
i += 1
lowerCAmelCase__ = sum(self.preflow[self.source_index] )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , snake_case__ : List[str] ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(__UpperCamelCase , __UpperCamelCase )
self.relabel(__UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : Dict , snake_case__ : List[Any] ):
lowerCAmelCase__ = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , snake_case__ : int ):
lowerCAmelCase__ = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
lowerCAmelCase__ = self.heights[to_index]
if min_height is not None:
lowerCAmelCase__ = min_height + 1
if __name__ == "__main__":
__lowerCAmelCase : List[str] = [0]
__lowerCAmelCase : str = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__lowerCAmelCase : Tuple = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__lowerCAmelCase : List[Any] = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__lowerCAmelCase : Optional[Any] = flow_network.find_maximum_flow()
print(F"maximum flow is {maximum_flow}")
| 715 | """simple docstring"""
import os
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCamelCase__ ) )
lowerCAmelCase__ = os.path.join(lowerCamelCase__ , """triangle.txt""" )
with open(lowerCamelCase__ ) as f:
lowerCAmelCase__ = f.readlines()
lowerCAmelCase__ = []
for line in triangle:
lowerCAmelCase__ = []
for number in line.strip().split(""" """ ):
numbers_from_line.append(int(lowerCamelCase__ ) )
a.append(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
for j in range(len(a[i] ) ):
lowerCAmelCase__ = a[i - 1][j] if j != len(a[i - 1] ) else 0
lowerCAmelCase__ = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowerCamelCase__ , lowerCamelCase__ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.max(_UpperCamelCase , axis=-1 , keepdims=_UpperCamelCase )
lowerCAmelCase__ = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=_UpperCamelCase )
class a_ ( __UpperCamelCase ):
def _SCREAMING_SNAKE_CASE ( self : str , **snake_case__ : List[Any] ):
lowerCAmelCase__ = {}
if "second_text" in kwargs:
lowerCAmelCase__ = kwargs["""second_text"""]
return preprocess_kwargs, {}, {}
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Union[str, Any] , snake_case__ : Union[str, Any]=None ):
return self.tokenizer(snake_case__ , text_pair=snake_case__ , return_tensors=self.framework )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Optional[int] ):
return self.model(**snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Optional[Any] ):
lowerCAmelCase__ = model_outputs.logits[0].numpy()
lowerCAmelCase__ = softmax(snake_case__ )
lowerCAmelCase__ = np.argmax(snake_case__ )
lowerCAmelCase__ = self.model.config.idalabel[best_class]
lowerCAmelCase__ = probabilities[best_class].item()
lowerCAmelCase__ = logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 716 | """simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase : Union[str, Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
__lowerCAmelCase : Optional[int] = json.load(f)
@require_torch
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Dict ):
return FSMTTokenizer.from_pretrained(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : Any ):
lowerCAmelCase__ = FSMTForConditionalGeneration.from_pretrained(snake_case__ ).to(snake_case__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Any , snake_case__ : int ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
lowerCAmelCase__ = F"""facebook/wmt19-{pair}"""
lowerCAmelCase__ = self.get_tokenizer(snake_case__ )
lowerCAmelCase__ = self.get_model(snake_case__ )
lowerCAmelCase__ = bleu_data[pair]["""src"""]
lowerCAmelCase__ = bleu_data[pair]["""tgt"""]
lowerCAmelCase__ = tokenizer(snake_case__ , return_tensors="""pt""" , truncation=snake_case__ , padding="""longest""" ).to(snake_case__ )
lowerCAmelCase__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
lowerCAmelCase__ = tokenizer.batch_decode(
snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
lowerCAmelCase__ = calculate_bleu(snake_case__ , snake_case__ )
print(snake_case__ )
self.assertGreaterEqual(scores["""bleu"""] , snake_case__ )
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ = position
lowerCAmelCase__ = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
lowerCAmelCase__ = []
for position in positions:
lowerCAmelCase__ , lowerCAmelCase__ = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(lowerCamelCase__ )
return permissible_positions
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return not any(elem == 0 for row in board for elem in row )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if is_complete(lowerCamelCase__ ):
return True
for position in get_valid_pos(lowerCamelCase__ , len(lowerCamelCase__ ) ):
lowerCAmelCase__ , lowerCAmelCase__ = position
if board[y][x] == 0:
lowerCAmelCase__ = curr + 1
if open_knight_tour_helper(lowerCamelCase__ , lowerCamelCase__ , curr + 1 ):
return True
lowerCAmelCase__ = 0
return False
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = [[0 for i in range(lowerCamelCase__ )] for j in range(lowerCamelCase__ )]
for i in range(lowerCamelCase__ ):
for j in range(lowerCamelCase__ ):
lowerCAmelCase__ = 1
if open_knight_tour_helper(lowerCamelCase__ , (i, j) , 1 ):
return board
lowerCAmelCase__ = 0
lowerCAmelCase__ = f"""Open Kight Tour cannot be performed on a board of size {n}"""
raise ValueError(lowerCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 717 | """simple docstring"""
import pprint
import requests
__lowerCAmelCase : Union[str, Any] = "https://zenquotes.io/api"
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = random_quotes()
pprint.pprint(response)
| 674 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class a_ ( __lowerCAmelCase ):
UpperCamelCase_ : Tuple = ["pixel_values"]
def __init__( self : Tuple , snake_case__ : bool = True , snake_case__ : Optional[Dict[str, int]] = None , snake_case__ : PILImageResampling = PILImageResampling.BILINEAR , snake_case__ : bool = True , snake_case__ : Dict[str, int] = None , snake_case__ : bool = True , snake_case__ : Union[int, float] = 1 / 255 , snake_case__ : bool = True , snake_case__ : Optional[Union[float, List[float]]] = None , snake_case__ : Optional[Union[float, List[float]]] = None , **snake_case__ : Tuple , ):
super().__init__(**lowerCamelCase__ )
lowerCAmelCase__ = size if size is not None else {'''shortest_edge''': 256}
lowerCAmelCase__ = get_size_dict(lowerCamelCase__ , default_to_square=lowerCamelCase__ )
lowerCAmelCase__ = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowerCAmelCase__ = get_size_dict(lowerCamelCase__ )
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size
lowerCAmelCase__ = resample
lowerCAmelCase__ = do_center_crop
lowerCAmelCase__ = crop_size
lowerCAmelCase__ = do_rescale
lowerCAmelCase__ = rescale_factor
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : np.ndarray , snake_case__ : Dict[str, int] , snake_case__ : PILImageResampling = PILImageResampling.BICUBIC , snake_case__ : Optional[Union[str, ChannelDimension]] = None , **snake_case__ : Optional[int] , ):
lowerCAmelCase__ = get_size_dict(lowerCamelCase__ , default_to_square=lowerCamelCase__ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
lowerCAmelCase__ = get_resize_output_image_size(lowerCamelCase__ , size=size["""shortest_edge"""] , default_to_square=lowerCamelCase__ )
return resize(lowerCamelCase__ , size=lowerCamelCase__ , resample=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : np.ndarray , snake_case__ : Dict[str, int] , snake_case__ : Optional[Union[str, ChannelDimension]] = None , **snake_case__ : List[str] , ):
lowerCAmelCase__ = get_size_dict(lowerCamelCase__ )
return center_crop(lowerCamelCase__ , size=(size["""height"""], size["""width"""]) , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : np.ndarray , snake_case__ : float , snake_case__ : Optional[Union[str, ChannelDimension]] = None , **snake_case__ : Dict ):
return rescale(lowerCamelCase__ , scale=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : np.ndarray , snake_case__ : Union[float, List[float]] , snake_case__ : Union[float, List[float]] , snake_case__ : Optional[Union[str, ChannelDimension]] = None , **snake_case__ : Union[str, Any] , ):
return normalize(lowerCamelCase__ , mean=lowerCamelCase__ , std=lowerCamelCase__ , data_format=lowerCamelCase__ , **lowerCamelCase__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : ImageInput , snake_case__ : Optional[bool] = None , snake_case__ : Dict[str, int] = None , snake_case__ : PILImageResampling = None , snake_case__ : bool = None , snake_case__ : Dict[str, int] = None , snake_case__ : Optional[bool] = None , snake_case__ : Optional[float] = None , snake_case__ : Optional[bool] = None , snake_case__ : Optional[Union[float, List[float]]] = None , snake_case__ : Optional[Union[float, List[float]]] = None , snake_case__ : Optional[Union[str, TensorType]] = None , snake_case__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **snake_case__ : List[str] , ):
lowerCAmelCase__ = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ = size if size is not None else self.size
lowerCAmelCase__ = get_size_dict(lowerCamelCase__ , default_to_square=lowerCamelCase__ )
lowerCAmelCase__ = resample if resample is not None else self.resample
lowerCAmelCase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase__ = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase__ = get_size_dict(lowerCamelCase__ )
lowerCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ = image_std if image_std is not None else self.image_std
lowerCAmelCase__ = make_list_of_images(lowerCamelCase__ )
if not valid_images(lowerCamelCase__ ):
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.
lowerCAmelCase__ = [to_numpy_array(lowerCamelCase__ ) for image in images]
if do_resize:
lowerCAmelCase__ = [self.resize(image=lowerCamelCase__ , size=lowerCamelCase__ , resample=lowerCamelCase__ ) for image in images]
if do_center_crop:
lowerCAmelCase__ = [self.center_crop(image=lowerCamelCase__ , size=lowerCamelCase__ ) for image in images]
if do_rescale:
lowerCAmelCase__ = [self.rescale(image=lowerCamelCase__ , scale=lowerCamelCase__ ) for image in images]
if do_normalize:
lowerCAmelCase__ = [self.normalize(image=lowerCamelCase__ , mean=lowerCamelCase__ , std=lowerCamelCase__ ) for image in images]
lowerCAmelCase__ = [to_channel_dimension_format(lowerCamelCase__ , lowerCamelCase__ ) for image in images]
lowerCAmelCase__ = {'''pixel_values''': images}
return BatchFeature(data=lowerCamelCase__ , tensor_type=lowerCamelCase__ )
| 718 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ ).to_dict()
config_dict.pop("""image_processor_type""" )
lowerCAmelCase__ = CLIPImageProcessor(**snake_case__ )
# save in new folder
model_config.save_pretrained(snake_case__ )
config.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
# make sure private variable is not incorrectly saved
lowerCAmelCase__ = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with self.assertRaisesRegex(
snake_case__ , """clip-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""clip-base""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
with self.assertRaisesRegex(
snake_case__ , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , revision="""aaaaaa""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
with self.assertRaisesRegex(
snake_case__ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , trust_remote_code=snake_case__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case__ ):
AutoImageProcessor.register(snake_case__ , snake_case__ )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = CustomImageProcessor.from_pretrained(snake_case__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Tuple = True
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# If remote code is not set, the default is to use local
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(snake_case__ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class a_ :
def __init__( self : Any , snake_case__ : int , snake_case__ : Dict=13 , snake_case__ : Union[str, Any]=7 , snake_case__ : int=True , snake_case__ : Dict=True , snake_case__ : str=False , snake_case__ : Optional[int]=True , snake_case__ : Dict=99 , snake_case__ : Dict=32 , snake_case__ : Tuple=5 , snake_case__ : int=4 , snake_case__ : List[str]=37 , snake_case__ : List[Any]="gelu" , snake_case__ : str=0.1 , snake_case__ : Tuple=0.1 , snake_case__ : Union[str, Any]=512 , snake_case__ : Tuple=16 , snake_case__ : str=2 , snake_case__ : Optional[Any]=0.02 , snake_case__ : Optional[Any]=3 , snake_case__ : Tuple=4 , snake_case__ : Dict=None , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = seq_length
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_input_mask
lowerCAmelCase__ = use_token_type_ids
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = max_position_embeddings
lowerCAmelCase__ = type_vocab_size
lowerCAmelCase__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = num_choices
lowerCAmelCase__ = scope
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ = None
if self.use_input_mask:
lowerCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ = None
if self.use_token_type_ids:
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _SCREAMING_SNAKE_CASE ( self : str ):
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case__ , initializer_range=self.initializer_range , use_stable_embedding=snake_case__ , )
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Dict , snake_case__ : int , snake_case__ : Union[str, Any] , snake_case__ : Tuple , snake_case__ : List[str] , snake_case__ : str , snake_case__ : Dict ):
lowerCAmelCase__ = OpenLlamaModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase__ = model(snake_case__ , attention_mask=snake_case__ )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , snake_case__ : Any , snake_case__ : List[str] , snake_case__ : Any , snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] , snake_case__ : Union[str, Any] , snake_case__ : int , snake_case__ : Any , snake_case__ : Optional[Any] , ):
lowerCAmelCase__ = True
lowerCAmelCase__ = OpenLlamaModel(snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase__ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , )
lowerCAmelCase__ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , )
lowerCAmelCase__ = model(snake_case__ , attention_mask=snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Tuple , snake_case__ : Optional[int] , snake_case__ : int , snake_case__ : Dict , snake_case__ : Union[str, Any] , snake_case__ : Optional[int] , snake_case__ : Any , snake_case__ : List[Any] , snake_case__ : Dict , ):
lowerCAmelCase__ = OpenLlamaForCausalLM(config=snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase__ = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Optional[Any] , snake_case__ : str , snake_case__ : int , snake_case__ : List[Any] , snake_case__ : str , snake_case__ : Optional[int] , snake_case__ : Dict , snake_case__ : List[str] , snake_case__ : Dict , ):
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = OpenLlamaForCausalLM(config=snake_case__ )
model.to(snake_case__ )
model.eval()
# first forward pass
lowerCAmelCase__ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , use_cache=snake_case__ , )
lowerCAmelCase__ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowerCAmelCase__ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowerCAmelCase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase__ = torch.cat([input_mask, next_mask] , dim=-1 )
lowerCAmelCase__ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , output_hidden_states=snake_case__ , )['''hidden_states'''][0]
lowerCAmelCase__ = model(
snake_case__ , attention_mask=snake_case__ , encoder_hidden_states=snake_case__ , encoder_attention_mask=snake_case__ , past_key_values=snake_case__ , output_hidden_states=snake_case__ , )['''hidden_states'''][0]
# select random slice
lowerCAmelCase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase__ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase__ = 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(snake_case__ , snake_case__ , atol=1E-3 ) )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
(
lowerCAmelCase__
) = config_and_inputs
lowerCAmelCase__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class a_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
UpperCamelCase_ : Union[str, Any] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
UpperCamelCase_ : List[str] = (OpenLlamaForCausalLM,) if is_torch_available() else ()
UpperCamelCase_ : List[str] = (
{
"feature-extraction": OpenLlamaModel,
"text-classification": OpenLlamaForSequenceClassification,
"text-generation": OpenLlamaForCausalLM,
"zero-shot": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[int] = False
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = OpenLlamaModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCAmelCase__ = type
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = 3
lowerCAmelCase__ = input_dict['''input_ids''']
lowerCAmelCase__ = input_ids.ne(1 ).to(snake_case__ )
lowerCAmelCase__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase__ = OpenLlamaForSequenceClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase__ = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = 3
lowerCAmelCase__ = '''single_label_classification'''
lowerCAmelCase__ = input_dict['''input_ids''']
lowerCAmelCase__ = input_ids.ne(1 ).to(snake_case__ )
lowerCAmelCase__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowerCAmelCase__ = OpenLlamaForSequenceClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase__ = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = 3
lowerCAmelCase__ = '''multi_label_classification'''
lowerCAmelCase__ = input_dict['''input_ids''']
lowerCAmelCase__ = input_ids.ne(1 ).to(snake_case__ )
lowerCAmelCase__ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowerCAmelCase__ = OpenLlamaForSequenceClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase__ = model(snake_case__ , attention_mask=snake_case__ , labels=snake_case__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , snake_case__ : Any ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = ids_tensor([1, 10] , config.vocab_size )
lowerCAmelCase__ = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase__ = OpenLlamaModel(snake_case__ )
original_model.to(snake_case__ )
original_model.eval()
lowerCAmelCase__ = original_model(snake_case__ ).last_hidden_state
lowerCAmelCase__ = original_model(snake_case__ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowerCAmelCase__ = {'''type''': scaling_type, '''factor''': 10.0}
lowerCAmelCase__ = OpenLlamaModel(snake_case__ )
scaled_model.to(snake_case__ )
scaled_model.eval()
lowerCAmelCase__ = scaled_model(snake_case__ ).last_hidden_state
lowerCAmelCase__ = scaled_model(snake_case__ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case__ , snake_case__ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(snake_case__ , snake_case__ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case__ , snake_case__ , atol=1E-5 ) )
| 719 | """simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a_ :
def __init__( self : Optional[int] , snake_case__ : List[Any]=2 , snake_case__ : Any=3 , snake_case__ : Union[str, Any]=64 , snake_case__ : Any=None ):
lowerCAmelCase__ = np.random.default_rng(snake_case__ )
lowerCAmelCase__ = length
lowerCAmelCase__ = rng.normal(size=(length,) ).astype(np.floataa )
lowerCAmelCase__ = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Optional[Any] ):
return self.length
def __getitem__( self : List[str] , snake_case__ : Optional[int] ):
return {"x": self.x[i], "y": self.y[i]}
class a_ ( torch.nn.Module ):
def __init__( self : List[str] , snake_case__ : str=0 , snake_case__ : Dict=0 , snake_case__ : Any=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Any=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a[0] + self.b[0]
class a_ ( torch.nn.Module ):
def __init__( self : Any , snake_case__ : Union[str, Any]=0 , snake_case__ : Union[str, Any]=0 , snake_case__ : List[Any]=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any]=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a + self.b
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase__ = {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
lowerCAmelCase__ = load_dataset("""csv""" , data_files=lowerCamelCase__ )
lowerCAmelCase__ = datasets["""train"""].unique("""label""" )
lowerCAmelCase__ = {v: i for i, v in enumerate(lowerCamelCase__ )}
def tokenize_function(lowerCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase__ = tokenizer(
examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
if "label" in examples:
lowerCAmelCase__ = [label_to_id[l] for l in examples["""label"""]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase__ = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""sentence1""", """sentence2""", """label"""] , )
def collate_fn(lowerCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase__ = DataLoader(tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=2 )
lowerCAmelCase__ = DataLoader(tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 674 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class a_ ( lowercase_ ):
UpperCamelCase_ : int = '''poolformer'''
def __init__( self : Any , snake_case__ : str=3 , snake_case__ : Tuple=16 , snake_case__ : List[Any]=16 , snake_case__ : List[Any]=3 , snake_case__ : Optional[Any]=4.0 , snake_case__ : Optional[int]=[2, 2, 6, 2] , snake_case__ : Dict=[64, 128, 320, 512] , snake_case__ : int=[7, 3, 3, 3] , snake_case__ : Optional[Any]=[4, 2, 2, 2] , snake_case__ : Dict=[2, 1, 1, 1] , snake_case__ : List[str]=4 , snake_case__ : int=0.0 , snake_case__ : Optional[int]="gelu" , snake_case__ : Optional[int]=True , snake_case__ : Union[str, Any]=1E-5 , snake_case__ : Dict=0.02 , **snake_case__ : Any , ):
A = num_channels
A = patch_size
A = stride
A = padding
A = pool_size
A = hidden_sizes
A = mlp_ratio
A = depths
A = patch_sizes
A = strides
A = num_encoder_blocks
A = drop_path_rate
A = hidden_act
A = use_layer_scale
A = layer_scale_init_value
A = initializer_range
super().__init__(**snake_case__ )
class a_ ( lowercase_ ):
UpperCamelCase_ : Dict = version.parse("1.11" )
@property
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
return 2E-3
| 720 | """simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = MobileBertConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = MobileBertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
lowerCAmelCase__ = load_tf_weights_in_mobilebert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = 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(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT 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."
)
__lowerCAmelCase : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
__lowerCAmelCase : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
__lowerCAmelCase : str = 2_56
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Tuple = ["""melgan"""]
def __init__( self : Optional[int] , snake_case__ : SpectrogramNotesEncoder , snake_case__ : SpectrogramContEncoder , snake_case__ : TaFilmDecoder , snake_case__ : DDPMScheduler , snake_case__ : OnnxRuntimeModel if is_onnx_available() else Any , ):
super().__init__()
# From MELGAN
lowerCAmelCase__ = math.log(1E-5 ) # Matches MelGAN training.
lowerCAmelCase__ = 4.0 # Largest value for most examples
lowerCAmelCase__ = 128
self.register_modules(
notes_encoder=snake_case__ , continuous_encoder=snake_case__ , decoder=snake_case__ , scheduler=snake_case__ , melgan=snake_case__ , )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Tuple , snake_case__ : Dict=(-1.0, 1.0) , snake_case__ : Dict=False ):
lowerCAmelCase__ , lowerCAmelCase__ = output_range
if clip:
lowerCAmelCase__ = torch.clip(snake_case__ , self.min_value , self.max_value )
# Scale to [0, 1].
lowerCAmelCase__ = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Optional[Any] , snake_case__ : int=(-1.0, 1.0) , snake_case__ : Optional[Any]=False ):
lowerCAmelCase__ , lowerCAmelCase__ = input_range
lowerCAmelCase__ = torch.clip(snake_case__ , snake_case__ , snake_case__ ) if clip else outputs
# Scale to [0, 1].
lowerCAmelCase__ = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : Tuple ):
lowerCAmelCase__ = input_tokens > 0
lowerCAmelCase__ , lowerCAmelCase__ = self.notes_encoder(
encoder_input_tokens=snake_case__ , encoder_inputs_mask=snake_case__ )
lowerCAmelCase__ , lowerCAmelCase__ = self.continuous_encoder(
encoder_inputs=snake_case__ , encoder_inputs_mask=snake_case__ )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Optional[Any] , snake_case__ : Any , snake_case__ : Dict ):
lowerCAmelCase__ = noise_time
if not torch.is_tensor(snake_case__ ):
lowerCAmelCase__ = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device )
elif torch.is_tensor(snake_case__ ) and len(timesteps.shape ) == 0:
lowerCAmelCase__ = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowerCAmelCase__ = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device )
lowerCAmelCase__ = self.decoder(
encodings_and_masks=snake_case__ , decoder_input_tokens=snake_case__ , decoder_noise_time=snake_case__ )
return logits
@torch.no_grad()
def __call__( self : Tuple , snake_case__ : List[List[int]] , snake_case__ : Optional[torch.Generator] = None , snake_case__ : int = 100 , snake_case__ : bool = True , snake_case__ : str = "numpy" , snake_case__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , snake_case__ : int = 1 , ):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(snake_case__ , snake_case__ ) or callback_steps <= 0)
):
raise ValueError(
F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
F""" {type(snake_case__ )}.""" )
lowerCAmelCase__ = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa )
lowerCAmelCase__ = np.zeros([1, 0, self.n_dims] , np.floataa )
lowerCAmelCase__ = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=snake_case__ , device=self.device )
for i, encoder_input_tokens in enumerate(snake_case__ ):
if i == 0:
lowerCAmelCase__ = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device , dtype=self.decoder.dtype )
# The first chunk has no previous context.
lowerCAmelCase__ = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=snake_case__ , device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
lowerCAmelCase__ = ones
lowerCAmelCase__ = self.scale_features(
snake_case__ , output_range=[-1.0, 1.0] , clip=snake_case__ )
lowerCAmelCase__ = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) , continuous_inputs=snake_case__ , continuous_mask=snake_case__ , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
lowerCAmelCase__ = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=snake_case__ , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(snake_case__ )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowerCAmelCase__ = self.decode(
encodings_and_masks=snake_case__ , input_tokens=snake_case__ , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
lowerCAmelCase__ = self.scheduler.step(snake_case__ , snake_case__ , snake_case__ , generator=snake_case__ ).prev_sample
lowerCAmelCase__ = self.scale_to_features(snake_case__ , input_range=[-1.0, 1.0] )
lowerCAmelCase__ = mel[:1]
lowerCAmelCase__ = mel.cpu().float().numpy()
lowerCAmelCase__ = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(snake_case__ , snake_case__ )
logger.info("""Generated segment""" , snake_case__ )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
"""Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.""" )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
"""Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.""" )
if output_type == "numpy":
lowerCAmelCase__ = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
lowerCAmelCase__ = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=snake_case__ )
| 721 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
lowerCAmelCase__ = f"""The input value of [n={number}] has to be > 0"""
raise ValueError(lowerCamelCase__ )
else:
lowerCAmelCase__ = sylvester(number - 1 )
lowerCAmelCase__ = num - 1
lowerCAmelCase__ = num
return lower * upper + 1
if __name__ == "__main__":
print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")
| 674 | 0 |
"""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 _SCREAMING_SNAKE_CASE ( self : List[str] ):
torch.manual_seed(0 )
lowerCAmelCase__ = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase__ = 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 )
lowerCAmelCase__ = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0001 , beta_end=0.02 , thresholding=snake_case__ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def _SCREAMING_SNAKE_CASE ( self : str ):
torch.manual_seed(0 )
lowerCAmelCase__ = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase__ = 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 )
lowerCAmelCase__ = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0001 , beta_end=0.02 , thresholding=snake_case__ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase__ = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
lowerCAmelCase__ = 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 _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.get_dummy_components()
lowerCAmelCase__ = self.pipeline_class(**snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
lowerCAmelCase__ = self.get_dummy_inputs(snake_case__ )
lowerCAmelCase__ = inputs["""prompt"""]
lowerCAmelCase__ = inputs["""generator"""]
lowerCAmelCase__ = inputs["""num_inference_steps"""]
lowerCAmelCase__ = inputs["""output_type"""]
if "image" in inputs:
lowerCAmelCase__ = inputs["""image"""]
else:
lowerCAmelCase__ = None
if "mask_image" in inputs:
lowerCAmelCase__ = inputs["""mask_image"""]
else:
lowerCAmelCase__ = None
if "original_image" in inputs:
lowerCAmelCase__ = inputs["""original_image"""]
else:
lowerCAmelCase__ = None
lowerCAmelCase__ , lowerCAmelCase__ = pipe.encode_prompt(snake_case__ )
# inputs with prompt converted to embeddings
lowerCAmelCase__ = {
"""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:
lowerCAmelCase__ = image
if mask_image is not None:
lowerCAmelCase__ = mask_image
if original_image is not None:
lowerCAmelCase__ = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase__ = pipe(**snake_case__ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(snake_case__ )
lowerCAmelCase__ = self.pipeline_class.from_pretrained(snake_case__ )
pipe_loaded.to(snake_case__ )
pipe_loaded.set_progress_bar_config(disable=snake_case__ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(snake_case__ , snake_case__ ) is None , F"""`{optional_component}` did not stay set to None after loading.""" , )
lowerCAmelCase__ = self.get_dummy_inputs(snake_case__ )
lowerCAmelCase__ = inputs["""generator"""]
lowerCAmelCase__ = inputs["""num_inference_steps"""]
lowerCAmelCase__ = inputs["""output_type"""]
# inputs with prompt converted to embeddings
lowerCAmelCase__ = {
"""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:
lowerCAmelCase__ = image
if mask_image is not None:
lowerCAmelCase__ = mask_image
if original_image is not None:
lowerCAmelCase__ = original_image
lowerCAmelCase__ = pipe_loaded(**snake_case__ )[0]
lowerCAmelCase__ = np.abs(to_np(snake_case__ ) - to_np(snake_case__ ) ).max()
self.assertLess(snake_case__ , 1E-4 )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.get_dummy_components()
lowerCAmelCase__ = self.pipeline_class(**snake_case__ )
pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
lowerCAmelCase__ = self.get_dummy_inputs(snake_case__ )
lowerCAmelCase__ = pipe(**snake_case__ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(snake_case__ )
lowerCAmelCase__ = self.pipeline_class.from_pretrained(snake_case__ )
pipe_loaded.to(snake_case__ )
pipe_loaded.set_progress_bar_config(disable=snake_case__ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowerCAmelCase__ = self.get_dummy_inputs(snake_case__ )
lowerCAmelCase__ = pipe_loaded(**snake_case__ )[0]
lowerCAmelCase__ = np.abs(to_np(snake_case__ ) - to_np(snake_case__ ) ).max()
self.assertLess(snake_case__ , 1E-4 )
| 700 | """simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : Optional[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Tuple = PegasusTokenizer
UpperCamelCase_ : Any = PegasusTokenizerFast
UpperCamelCase_ : int = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = PegasusTokenizer(snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : Optional[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any] ):
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = """</s>"""
lowerCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """</s>""" )
self.assertEqual(vocab_keys[-1] , """v""" )
self.assertEqual(len(snake_case__ ) , 1103 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
""" </s> <pad> <pad> <pad>"""
)
lowerCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
lowerCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowerCAmelCase__ = """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
lowerCAmelCase__ = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
lowerCAmelCase__ = tokenizer([raw_input_str] , return_tensors=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
lowerCAmelCase__ = """To ensure a smooth flow of bank resolutions."""
lowerCAmelCase__ = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
lowerCAmelCase__ = tokenizer([raw_input_str] , return_tensors=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = ["""This is going to be way too long.""" * 150, """short example"""]
lowerCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
lowerCAmelCase__ = self._large_tokenizer(snake_case__ , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
lowerCAmelCase__ = self._large_tokenizer(
text_target=snake_case__ , max_length=5 , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case__ ) == 2 # input_ids, attention_mask.
@slow
def _SCREAMING_SNAKE_CASE ( self : str ):
# fmt: off
lowerCAmelCase__ = {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=snake_case__ , model_name="""google/bigbird-pegasus-large-arxiv""" , revision="""ba85d0851d708441f91440d509690f1ab6353415""" , )
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = PegasusTokenizer
UpperCamelCase_ : Optional[int] = PegasusTokenizerFast
UpperCamelCase_ : Union[str, Any] = True
UpperCamelCase_ : Optional[int] = True
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = PegasusTokenizer(snake_case__ , offset=0 , mask_token_sent=snake_case__ , mask_token="""[MASK]""" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Dict ):
return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , **snake_case__ : List[Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Dict ):
return ("This is a test", "This is a test")
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ = (
"""Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
""" <pad> <pad> <pad>"""
)
lowerCAmelCase__ = rust_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
lowerCAmelCase__ = py_tokenizer([raw_input_str] , return_tensors=snake_case__ , add_special_tokens=snake_case__ ).input_ids[0]
self.assertListEqual(snake_case__ , snake_case__ )
@require_torch
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = ["""This is going to be way too long.""" * 1000, """short example"""]
lowerCAmelCase__ = ["""not super long but more than 5 tokens""", """tiny"""]
lowerCAmelCase__ = self._large_tokenizer(snake_case__ , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
lowerCAmelCase__ = self._large_tokenizer(
text_target=snake_case__ , max_length=5 , padding=snake_case__ , truncation=snake_case__ , return_tensors="""pt""" )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case__ ) == 2 # input_ids, attention_mask.
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = (
"""This is an example string that is used to test the original TF implementation against the HF"""
""" implementation"""
)
lowerCAmelCase__ = self._large_tokenizer(snake_case__ ).input_ids
self.assertListEqual(
snake_case__ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1] , )
| 674 | 0 |
"""simple docstring"""
__lowerCAmelCase : List[str] = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
| 701 | """simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : int ):
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=snake_case__ , )
assert hasattr(self , """env""" )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Optional[Any] ):
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase__ = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase__ = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase__ = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase__ = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=snake_case__ , instance_type=self.instance_type , debugger_hook_config=snake_case__ , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=snake_case__ , py_version="""py36""" , )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : str ):
TrainingJobAnalytics(snake_case__ ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : List[str] ):
# create estimator
lowerCAmelCase__ = self.create_estimator(snake_case__ )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , snake_case__ )
| 674 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a_ :
def __init__( self : Optional[int] , snake_case__ : List[Any]=2 , snake_case__ : Any=3 , snake_case__ : Union[str, Any]=64 , snake_case__ : Any=None ):
lowerCAmelCase__ = np.random.default_rng(snake_case__ )
lowerCAmelCase__ = length
lowerCAmelCase__ = rng.normal(size=(length,) ).astype(np.floataa )
lowerCAmelCase__ = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Optional[Any] ):
return self.length
def __getitem__( self : List[str] , snake_case__ : Optional[int] ):
return {"x": self.x[i], "y": self.y[i]}
class a_ ( torch.nn.Module ):
def __init__( self : List[str] , snake_case__ : str=0 , snake_case__ : Dict=0 , snake_case__ : Any=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Any=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a[0] + self.b[0]
class a_ ( torch.nn.Module ):
def __init__( self : Any , snake_case__ : Union[str, Any]=0 , snake_case__ : Union[str, Any]=0 , snake_case__ : List[Any]=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any]=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a + self.b
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase__ = {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
lowerCAmelCase__ = load_dataset("""csv""" , data_files=lowerCamelCase__ )
lowerCAmelCase__ = datasets["""train"""].unique("""label""" )
lowerCAmelCase__ = {v: i for i, v in enumerate(lowerCamelCase__ )}
def tokenize_function(lowerCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase__ = tokenizer(
examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
if "label" in examples:
lowerCAmelCase__ = [label_to_id[l] for l in examples["""label"""]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase__ = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""sentence1""", """sentence2""", """label"""] , )
def collate_fn(lowerCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase__ = DataLoader(tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=2 )
lowerCAmelCase__ = DataLoader(tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 702 | """simple docstring"""
from math import pi, sqrt
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_71.5:
raise OverflowError("""math range error""" )
elif num - int(lowerCamelCase__ ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(lowerCamelCase__ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def _UpperCAmelCase ( ):
"""simple docstring"""
assert gamma(0.5 ) == sqrt(lowerCamelCase__ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__lowerCAmelCase : Dict = 1.0
while num:
__lowerCAmelCase : Any = float(input("Gamma of: "))
print(F"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| 674 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCAmelCase : Tuple = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : int = [
"CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"CTRLForSequenceClassification",
"CTRLLMHeadModel",
"CTRLModel",
"CTRLPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Optional[Any] = [
"TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFCTRLForSequenceClassification",
"TFCTRLLMHeadModel",
"TFCTRLModel",
"TFCTRLPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 703 | """simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a_ :
def __init__( self : Optional[int] , snake_case__ : Optional[Any] , snake_case__ : Any=13 , snake_case__ : int=30 , snake_case__ : int=2 , snake_case__ : Union[str, Any]=3 , snake_case__ : Dict=True , snake_case__ : Optional[int]=True , snake_case__ : List[Any]=32 , snake_case__ : List[str]=2 , snake_case__ : Optional[Any]=4 , snake_case__ : Optional[int]=37 , snake_case__ : Tuple="gelu" , snake_case__ : str=0.1 , snake_case__ : Any=0.1 , snake_case__ : int=10 , snake_case__ : Dict=0.02 , snake_case__ : Union[str, Any]=3 , snake_case__ : str=None , snake_case__ : List[Any]=2 , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
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__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = scope
lowerCAmelCase__ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowerCAmelCase__ = (image_size // patch_size) ** 2
lowerCAmelCase__ = num_patches + 2
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
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=snake_case__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : int , snake_case__ : Optional[Any] , snake_case__ : List[str] ):
lowerCAmelCase__ = TFDeiTModel(config=snake_case__ )
lowerCAmelCase__ = model(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 , snake_case__ : Dict , snake_case__ : Dict , snake_case__ : Dict ):
lowerCAmelCase__ = TFDeiTForMaskedImageModeling(config=snake_case__ )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = TFDeiTForMaskedImageModeling(snake_case__ )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Union[str, Any] , snake_case__ : Any , snake_case__ : Tuple ):
lowerCAmelCase__ = self.type_sequence_label_size
lowerCAmelCase__ = TFDeiTForImageClassification(snake_case__ )
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase__ = 1
lowerCAmelCase__ = TFDeiTForImageClassification(snake_case__ )
lowerCAmelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class a_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Optional[Any] = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : Any = (
{
"feature-extraction": TFDeiTModel,
"image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[Any] = False
UpperCamelCase_ : Optional[int] = False
UpperCamelCase_ : int = False
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = TFDeiTModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
pass
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
lowerCAmelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , tf.keras.layers.Dense ) )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : Union[str, Any] , snake_case__ : int , snake_case__ : Union[str, Any]=False ):
lowerCAmelCase__ = super()._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def _SCREAMING_SNAKE_CASE ( self : Any ):
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = TFDeiTModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class a_ ( unittest.TestCase ):
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Any ):
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=snake_case__ , return_tensors="""tf""" )
# forward pass
lowerCAmelCase__ = model(**snake_case__ )
# verify the logits
lowerCAmelCase__ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case__ )
lowerCAmelCase__ = tf.constant([-1.0266, 0.1912, -1.2861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , snake_case__ , atol=1E-4 ) )
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = """"""
for i in table:
res += inp[i - 1]
return res
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return data[1:] + data[0]
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = """"""
for i in range(len(lowerCamelCase__ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = int("""0b""" + data[0] + data[-1] , 2 )
lowerCAmelCase__ = int("""0b""" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = message[:4]
lowerCAmelCase__ = message[4:]
lowerCAmelCase__ = apply_table(lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = xor(lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = apply_sbox(lowerCamelCase__ , temp[:4] ) # noqa: E741
lowerCAmelCase__ = apply_sbox(lowerCamelCase__ , temp[4:] )
lowerCAmelCase__ = """0""" * (2 - len(lowerCamelCase__ )) + l # noqa: E741
lowerCAmelCase__ = """0""" * (2 - len(lowerCamelCase__ )) + r
lowerCAmelCase__ = apply_table(l + r , lowerCamelCase__ )
lowerCAmelCase__ = xor(lowerCamelCase__ , lowerCamelCase__ )
return temp + right
if __name__ == "__main__":
__lowerCAmelCase : List[str] = input("Enter 10 bit key: ")
__lowerCAmelCase : Optional[int] = input("Enter 8 bit message: ")
__lowerCAmelCase : str = [6, 3, 7, 4, 8, 5, 10, 9]
__lowerCAmelCase : Union[str, Any] = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
__lowerCAmelCase : Tuple = [2, 4, 3, 1]
__lowerCAmelCase : Dict = [2, 6, 3, 1, 4, 8, 5, 7]
__lowerCAmelCase : str = [4, 1, 3, 5, 7, 2, 8, 6]
__lowerCAmelCase : List[str] = [4, 1, 2, 3, 2, 3, 4, 1]
__lowerCAmelCase : Union[str, Any] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__lowerCAmelCase : str = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__lowerCAmelCase : List[Any] = apply_table(key, paa_table)
__lowerCAmelCase : Dict = temp[:5]
__lowerCAmelCase : List[Any] = temp[5:]
__lowerCAmelCase : int = left_shift(left)
__lowerCAmelCase : Optional[Any] = left_shift(right)
__lowerCAmelCase : List[Any] = apply_table(left + right, pa_table)
__lowerCAmelCase : Tuple = left_shift(left)
__lowerCAmelCase : Dict = left_shift(right)
__lowerCAmelCase : Any = left_shift(left)
__lowerCAmelCase : Optional[Any] = left_shift(right)
__lowerCAmelCase : int = apply_table(left + right, pa_table)
# encryption
__lowerCAmelCase : Union[str, Any] = apply_table(message, IP)
__lowerCAmelCase : Dict = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase : List[Any] = temp[4:] + temp[:4]
__lowerCAmelCase : List[str] = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase : Any = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
__lowerCAmelCase : Union[str, Any] = apply_table(CT, IP)
__lowerCAmelCase : int = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase : List[str] = temp[4:] + temp[:4]
__lowerCAmelCase : List[str] = function(expansion, sa, sa, keya, temp)
__lowerCAmelCase : List[str] = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 704 | """simple docstring"""
from __future__ import annotations
from math import gcd
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 2 , lowerCamelCase__ = 1 , lowerCamelCase__ = 3 , ):
"""simple docstring"""
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> int:
return (pow(lowerCamelCase__ , 2 ) + step) % modulus
for _ in range(lowerCamelCase__ ):
# These track the position within the cycle detection logic.
lowerCAmelCase__ = seed
lowerCAmelCase__ = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCAmelCase__ = rand_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
lowerCAmelCase__ = gcd(hare - tortoise , lowerCamelCase__ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
lowerCAmelCase__ = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
__lowerCAmelCase : List[str] = parser.parse_args()
__lowerCAmelCase : Dict = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"{args.num} is probably prime")
else:
__lowerCAmelCase : List[str] = args.num // divisor
print(F"{args.num} = {divisor} * {quotient}")
| 674 | 0 |
"""simple docstring"""
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 a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = tempfile.mkdtemp()
# fmt: off
lowerCAmelCase__ = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
# fmt: on
lowerCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
lowerCAmelCase__ = {
"""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],
}
lowerCAmelCase__ = os.path.join(self.tmpdirname , snake_case__ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict , **snake_case__ : str ):
return BertTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple , **snake_case__ : Dict ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
shutil.rmtree(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCAmelCase__ = [Image.fromarray(np.moveaxis(snake_case__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = VisionTextDualEncoderProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ = 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 , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase__ = self.get_image_processor(do_normalize=snake_case__ , padding_value=1.0 )
lowerCAmelCase__ = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=snake_case__ , 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 , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = VisionTextDualEncoderProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = image_processor(snake_case__ , return_tensors="""np""" )
lowerCAmelCase__ = processor(images=snake_case__ , 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 _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = VisionTextDualEncoderProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = processor(text=snake_case__ )
lowerCAmelCase__ = tokenizer(snake_case__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = VisionTextDualEncoderProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = processor(text=snake_case__ , images=snake_case__ )
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(snake_case__ ):
processor()
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = VisionTextDualEncoderProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
lowerCAmelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase__ = processor.batch_decode(snake_case__ )
lowerCAmelCase__ = tokenizer.batch_decode(snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = self.get_image_processor()
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = VisionTextDualEncoderProcessor(tokenizer=snake_case__ , image_processor=snake_case__ )
lowerCAmelCase__ = """lower newer"""
lowerCAmelCase__ = self.prepare_image_inputs()
lowerCAmelCase__ = processor(text=snake_case__ , images=snake_case__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 705 | """simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = TapasConfig.from_json_file(lowerCamelCase__ )
# set absolute/relative position embeddings parameter
lowerCAmelCase__ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "WTQ":
# run_task_main.py hparams
lowerCAmelCase__ = 4
lowerCAmelCase__ = True
# hparam_utils.py hparams
lowerCAmelCase__ = 0.66_46_94
lowerCAmelCase__ = 0.20_79_51
lowerCAmelCase__ = 0.12_11_94
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = 0.0_35_25_13
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
lowerCAmelCase__ = 4
lowerCAmelCase__ = False
# hparam_utils.py hparams
lowerCAmelCase__ = 36.45_19
lowerCAmelCase__ = 0.90_34_21
lowerCAmelCase__ = 2_22.0_88
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = 0.76_31_41
lowerCAmelCase__ = TapasForQuestionAnswering(config=lowerCamelCase__ )
elif task == "TABFACT":
lowerCAmelCase__ = TapasForSequenceClassification(config=lowerCamelCase__ )
elif task == "MLM":
lowerCAmelCase__ = TapasForMaskedLM(config=lowerCamelCase__ )
elif task == "INTERMEDIATE_PRETRAINING":
lowerCAmelCase__ = TapasModel(config=lowerCamelCase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowerCamelCase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
lowerCAmelCase__ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" , model_max_length=512 )
tokenizer.save_pretrained(lowerCamelCase__ )
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
)
parser.add_argument(
"--reset_position_index_per_cell",
default=False,
action="store_true",
help="Whether to use relative position embeddings or not. Defaults to True.",
)
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--tapas_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained TAPAS 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."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 674 | 0 |
"""simple docstring"""
from math import pi, sqrt
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(lowerCamelCase__ ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(lowerCamelCase__ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def _UpperCAmelCase ( ):
"""simple docstring"""
assert gamma(0.5 ) == sqrt(lowerCamelCase__ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__lowerCAmelCase : Dict = 1.0
while num:
__lowerCAmelCase : Any = float(input("Gamma of: "))
print(F"gamma({num}) = {gamma(num)}")
print("\nEnter 0 to exit...")
| 706 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ = 50 ):
"""simple docstring"""
lowerCAmelCase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F"{solution() = }")
| 674 | 0 |
"""simple docstring"""
import os
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.join(os.path.dirname(lowerCamelCase__ ) , """num.txt""" )
with open(lowerCamelCase__ ) as file_hand:
return str(sum(int(lowerCamelCase__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 707 | """simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse("0.8.3"):
raise Exception("requires gluonnlp == 0.8.3")
if version.parse(mx.__version__) != version.parse("1.5.0"):
raise Exception("requires mxnet == 1.5.0")
logging.set_verbosity_info()
__lowerCAmelCase : Any = logging.get_logger(__name__)
__lowerCAmelCase : Any = "The Nymphenburg Palace is a beautiful palace in Munich!"
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {
"""attention_cell""": """multi_head""",
"""num_layers""": 4,
"""units""": 1024,
"""hidden_size""": 768,
"""max_length""": 512,
"""num_heads""": 8,
"""scaled""": True,
"""dropout""": 0.1,
"""use_residual""": True,
"""embed_size""": 1024,
"""embed_dropout""": 0.1,
"""word_embed""": None,
"""layer_norm_eps""": 1e-5,
"""token_type_vocab_size""": 2,
}
lowerCAmelCase__ = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
lowerCAmelCase__ = BERTEncoder(
attention_cell=predefined_args["""attention_cell"""] , num_layers=predefined_args["""num_layers"""] , units=predefined_args["""units"""] , hidden_size=predefined_args["""hidden_size"""] , max_length=predefined_args["""max_length"""] , num_heads=predefined_args["""num_heads"""] , scaled=predefined_args["""scaled"""] , dropout=predefined_args["""dropout"""] , output_attention=lowerCamelCase__ , output_all_encodings=lowerCamelCase__ , use_residual=predefined_args["""use_residual"""] , activation=predefined_args.get("""activation""" , """gelu""" ) , layer_norm_eps=predefined_args.get("""layer_norm_eps""" , lowerCamelCase__ ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
lowerCAmelCase__ = """openwebtext_ccnews_stories_books_cased"""
# Specify download folder to Gluonnlp's vocab
lowerCAmelCase__ = os.path.join(get_home_dir() , """models""" )
lowerCAmelCase__ = _load_vocab(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , cls=lowerCamelCase__ )
lowerCAmelCase__ = nlp.model.BERTModel(
lowerCamelCase__ , len(lowerCamelCase__ ) , units=predefined_args["""units"""] , embed_size=predefined_args["""embed_size"""] , embed_dropout=predefined_args["""embed_dropout"""] , word_embed=predefined_args["""word_embed"""] , use_pooler=lowerCamelCase__ , use_token_type_embed=lowerCamelCase__ , token_type_vocab_size=predefined_args["""token_type_vocab_size"""] , use_classifier=lowerCamelCase__ , use_decoder=lowerCamelCase__ , )
original_bort.load_parameters(lowerCamelCase__ , cast_dtype=lowerCamelCase__ , ignore_extra=lowerCamelCase__ )
lowerCAmelCase__ = original_bort._collect_params_with_prefix()
# Build our config 🤗
lowerCAmelCase__ = {
"""architectures""": ["""BertForMaskedLM"""],
"""attention_probs_dropout_prob""": predefined_args["""dropout"""],
"""hidden_act""": """gelu""",
"""hidden_dropout_prob""": predefined_args["""dropout"""],
"""hidden_size""": predefined_args["""embed_size"""],
"""initializer_range""": 0.02,
"""intermediate_size""": predefined_args["""hidden_size"""],
"""layer_norm_eps""": predefined_args["""layer_norm_eps"""],
"""max_position_embeddings""": predefined_args["""max_length"""],
"""model_type""": """bort""",
"""num_attention_heads""": predefined_args["""num_heads"""],
"""num_hidden_layers""": predefined_args["""num_layers"""],
"""pad_token_id""": 1, # 2 = BERT, 1 = RoBERTa
"""type_vocab_size""": 1, # 2 = BERT, 1 = RoBERTa
"""vocab_size""": len(lowerCamelCase__ ),
}
lowerCAmelCase__ = BertConfig.from_dict(lowerCamelCase__ )
lowerCAmelCase__ = BertForMaskedLM(lowerCamelCase__ )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(lowerCamelCase__ ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = hf_param.shape
lowerCAmelCase__ = to_torch(params[gluon_param] )
lowerCAmelCase__ = gluon_param.shape
assert (
shape_hf == shape_gluon
), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""
return gluon_param
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , """word_embed.0.weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , """encoder.position_weight""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , """encoder.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , """encoder.layer_norm.gamma""" )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
lowerCAmelCase__ = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
lowerCAmelCase__ = hf_bort_model.bert.encoder.layer[i]
# self attention
lowerCAmelCase__ = layer.attention.self
lowerCAmelCase__ = check_and_map_params(
self_attn.key.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.key.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.query.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.bias.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_attn.value.weight.data , f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight""" )
# self attention output
lowerCAmelCase__ = layer.attention.output
lowerCAmelCase__ = check_and_map_params(
self_output.dense.bias , f"""encoder.transformer_cells.{i}.proj.bias""" )
lowerCAmelCase__ = check_and_map_params(
self_output.dense.weight , f"""encoder.transformer_cells.{i}.proj.weight""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
self_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.layer_norm.gamma""" )
# intermediate
lowerCAmelCase__ = layer.intermediate
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias""" )
lowerCAmelCase__ = check_and_map_params(
intermediate.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight""" )
# output
lowerCAmelCase__ = layer.output
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.bias , f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.dense.weight , f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.bias , f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta""" )
lowerCAmelCase__ = check_and_map_params(
bert_output.LayerNorm.weight , f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma""" )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
lowerCAmelCase__ = RobertaTokenizer.from_pretrained("""roberta-base""" )
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ )["""input_ids"""]
# Get gluon output
lowerCAmelCase__ = mx.nd.array([input_ids] )
lowerCAmelCase__ = original_bort(inputs=lowerCamelCase__ , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(lowerCamelCase__ )
lowerCAmelCase__ = BertModel.from_pretrained(lowerCamelCase__ )
hf_bort_model.eval()
lowerCAmelCase__ = tokenizer.encode_plus(lowerCamelCase__ , return_tensors="""pt""" )
lowerCAmelCase__ = hf_bort_model(**lowerCamelCase__ )[0]
lowerCAmelCase__ = output_gluon[0].asnumpy()
lowerCAmelCase__ = output_hf[0].detach().numpy()
lowerCAmelCase__ = np.max(np.abs(hf_layer - gluon_layer ) ).item()
lowerCAmelCase__ = np.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-3 )
if success:
print("""✔️ Both model do output the same tensors""" )
else:
print("""❌ Both model do **NOT** output the same tensors""" )
print("""Absolute difference is:""" , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : str = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 674 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : Dict = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Union[str, Any] = XLNetTokenizer
UpperCamelCase_ : Optional[int] = XLNetTokenizerFast
UpperCamelCase_ : int = True
UpperCamelCase_ : Union[str, Any] = True
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ = XLNetTokenizer(snake_case__ , keep_accents=snake_case__ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = """<s>"""
lowerCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = 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(snake_case__ ) , 1006 )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = XLNetTokenizer(snake_case__ , keep_accents=snake_case__ )
lowerCAmelCase__ = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(snake_case__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [285, 46, 10, 170, 382] )
lowerCAmelCase__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
snake_case__ , [
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""",
""".""",
] , )
lowerCAmelCase__ = tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(snake_case__ , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
lowerCAmelCase__ = tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [
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 _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = XLNetTokenizer(snake_case__ , do_lower_case=snake_case__ )
lowerCAmelCase__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
snake_case__ , [
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 _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = XLNetTokenizer(snake_case__ , do_lower_case=snake_case__ )
lowerCAmelCase__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
snake_case__ , [
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 _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = XLNetTokenizer.from_pretrained("""xlnet-base-cased""" )
lowerCAmelCase__ = tokenizer.encode("""sequence builders""" , add_special_tokens=snake_case__ )
lowerCAmelCase__ = tokenizer.encode("""multi-sequence build""" , add_special_tokens=snake_case__ )
lowerCAmelCase__ = tokenizer.build_inputs_with_special_tokens(snake_case__ )
lowerCAmelCase__ = tokenizer.build_inputs_with_special_tokens(snake_case__ , snake_case__ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
# fmt: off
lowerCAmelCase__ = {"""input_ids""": [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 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, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 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, 13894, 3380, 23, 95, 18, 17634, 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=snake_case__ , model_name="""xlnet-base-cased""" , revision="""c841166438c31ec7ca9a106dee7bb312b73ae511""" , )
| 708 | """simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self : Optional[int] ):
lowerCAmelCase__ = """"""
lowerCAmelCase__ = """"""
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
lowerCAmelCase__ = 256
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Union[str, Any] ):
lowerCAmelCase__ = cva.imread(snake_case__ , 0 )
lowerCAmelCase__ = copy.deepcopy(self.img )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = plt.hist(self.img.ravel() , 256 , [0, 256] , label="""x""" )
lowerCAmelCase__ = np.sum(snake_case__ )
for i in range(len(snake_case__ ) ):
lowerCAmelCase__ = x[i] / self.k
self.sk += prk
lowerCAmelCase__ = (self.L - 1) * self.sk
if self.rem != 0:
lowerCAmelCase__ = int(last % last )
lowerCAmelCase__ = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(snake_case__ )
lowerCAmelCase__ = int(np.ma.count(self.img ) / self.img[1].size )
lowerCAmelCase__ = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowerCAmelCase__ = self.img[j][i]
if num != self.last_list[num]:
lowerCAmelCase__ = self.last_list[num]
cva.imwrite("""output_data/output.jpg""" , self.img )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
cva.imshow("""Output-Image""" , self.img )
cva.imshow("""Input-Image""" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
__lowerCAmelCase : Dict = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
__lowerCAmelCase : Optional[int] = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 674 | 0 |
"""simple docstring"""
from typing import List
import numpy as np
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {key: len(lowerCamelCase__ ) for key, value in gen_kwargs.items() if isinstance(lowerCamelCase__ , lowerCamelCase__ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
"""Sharding is ambiguous for this dataset: """
+ """we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"""
+ """\n""".join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items() )
+ """\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, """
+ """and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."""
) )
lowerCAmelCase__ = max(lists_lengths.values() , default=0 )
return max(1 , lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = []
for group_idx in range(lowerCamelCase__ ):
lowerCAmelCase__ = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
lowerCAmelCase__ = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
lowerCAmelCase__ = range(lowerCamelCase__ , start + num_shards_to_add )
shards_indices_per_group.append(lowerCamelCase__ )
return shards_indices_per_group
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = _number_of_shards_in_gen_kwargs(lowerCamelCase__ )
if num_shards == 1:
return [dict(lowerCamelCase__ )]
else:
lowerCAmelCase__ = _distribute_shards(num_shards=lowerCamelCase__ , max_num_jobs=lowerCamelCase__ )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(lowerCamelCase__ , lowerCamelCase__ )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(lowerCamelCase__ ) )
]
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , lowerCamelCase__ )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {len(lowerCamelCase__ ) for value in gen_kwargs.values() if isinstance(lowerCamelCase__ , lowerCamelCase__ )}
lowerCAmelCase__ = {}
for size in list_sizes:
lowerCAmelCase__ = list(range(lowerCamelCase__ ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
lowerCAmelCase__ = dict(lowerCamelCase__ )
for key, value in shuffled_kwargs.items():
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = [value[i] for i in indices_per_size[len(lowerCamelCase__ )]]
return shuffled_kwargs
| 709 | """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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : List[str] = (
"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
)
UpperCamelCase_ : str = "CIDAS/clipseg-rd64-refined"
UpperCamelCase_ : Any = "image_segmenter"
UpperCamelCase_ : Optional[Any] = CLIPSegForImageSegmentation
UpperCamelCase_ : List[str] = ["image", "text"]
UpperCamelCase_ : int = ["image"]
def __init__( self : Tuple , *snake_case__ : str , **snake_case__ : Optional[Any] ):
requires_backends(self , ["""vision"""] )
super().__init__(*snake_case__ , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : "Image" , snake_case__ : str ):
return self.pre_processor(text=[label] , images=[image] , padding=snake_case__ , return_tensors="""pt""" )
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Tuple ):
with torch.no_grad():
lowerCAmelCase__ = self.model(**snake_case__ ).logits
return logits
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : List[Any] ):
lowerCAmelCase__ = outputs.cpu().detach().numpy()
lowerCAmelCase__ = 0
lowerCAmelCase__ = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 674 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : torch.FloatTensor
UpperCamelCase_ : Optional[torch.FloatTensor] = None
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=0.9_99 , lowerCamelCase__="cosine" , ):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowerCamelCase__ ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowerCamelCase__ ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
lowerCAmelCase__ = []
for i in range(lowerCamelCase__ ):
lowerCAmelCase__ = i / num_diffusion_timesteps
lowerCAmelCase__ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowerCamelCase__ ) / alpha_bar_fn(lowerCamelCase__ ) , lowerCamelCase__ ) )
return torch.tensor(lowerCamelCase__ , dtype=torch.floataa )
class a_ ( __UpperCamelCase , __UpperCamelCase ):
@register_to_config
def __init__( self : List[Any] , snake_case__ : int = 1000 , snake_case__ : str = "fixed_small_log" , snake_case__ : bool = True , snake_case__ : Optional[float] = 1.0 , snake_case__ : str = "epsilon" , snake_case__ : str = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )
lowerCAmelCase__ = betas_for_alpha_bar(snake_case__ )
lowerCAmelCase__ = 1.0 - self.betas
lowerCAmelCase__ = torch.cumprod(self.alphas , dim=0 )
lowerCAmelCase__ = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
lowerCAmelCase__ = 1.0
# setable values
lowerCAmelCase__ = None
lowerCAmelCase__ = torch.from_numpy(np.arange(0 , snake_case__ )[::-1].copy() )
lowerCAmelCase__ = variance_type
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : torch.FloatTensor , snake_case__ : Optional[int] = None ):
return sample
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : int , snake_case__ : Union[str, torch.device] = None ):
lowerCAmelCase__ = num_inference_steps
lowerCAmelCase__ = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
lowerCAmelCase__ = (np.arange(0 , snake_case__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
lowerCAmelCase__ = torch.from_numpy(snake_case__ ).to(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : Optional[int] , snake_case__ : List[str]=None , snake_case__ : Optional[int]=None , snake_case__ : Tuple=None ):
if prev_timestep is None:
lowerCAmelCase__ = t - 1
lowerCAmelCase__ = self.alphas_cumprod[t]
lowerCAmelCase__ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
lowerCAmelCase__ = 1 - alpha_prod_t
lowerCAmelCase__ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
lowerCAmelCase__ = self.betas[t]
else:
lowerCAmelCase__ = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowerCAmelCase__ = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
lowerCAmelCase__ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
lowerCAmelCase__ = torch.log(torch.clamp(snake_case__ , min=1E-20 ) )
lowerCAmelCase__ = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
lowerCAmelCase__ = variance.log()
lowerCAmelCase__ = beta.log()
lowerCAmelCase__ = (predicted_variance + 1) / 2
lowerCAmelCase__ = frac * max_log + (1 - frac) * min_log
return variance
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : torch.FloatTensor , snake_case__ : int , snake_case__ : torch.FloatTensor , snake_case__ : Optional[int] = None , snake_case__ : Optional[int]=None , snake_case__ : bool = True , ):
lowerCAmelCase__ = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
lowerCAmelCase__ , lowerCAmelCase__ = torch.split(snake_case__ , sample.shape[1] , dim=1 )
else:
lowerCAmelCase__ = None
# 1. compute alphas, betas
if prev_timestep is None:
lowerCAmelCase__ = t - 1
lowerCAmelCase__ = self.alphas_cumprod[t]
lowerCAmelCase__ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
lowerCAmelCase__ = 1 - alpha_prod_t
lowerCAmelCase__ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
lowerCAmelCase__ = self.betas[t]
lowerCAmelCase__ = self.alphas[t]
else:
lowerCAmelCase__ = 1 - alpha_prod_t / alpha_prod_t_prev
lowerCAmelCase__ = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowerCAmelCase__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowerCAmelCase__ = model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
""" for the UnCLIPScheduler.""" )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowerCAmelCase__ = torch.clamp(
snake_case__ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowerCAmelCase__ = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
lowerCAmelCase__ = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowerCAmelCase__ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
lowerCAmelCase__ = 0
if t > 0:
lowerCAmelCase__ = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=snake_case__ , device=model_output.device )
lowerCAmelCase__ = self._get_variance(
snake_case__ , predicted_variance=snake_case__ , prev_timestep=snake_case__ , )
if self.variance_type == "fixed_small_log":
lowerCAmelCase__ = variance
elif self.variance_type == "learned_range":
lowerCAmelCase__ = (0.5 * variance).exp()
else:
raise ValueError(
F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
""" for the UnCLIPScheduler.""" )
lowerCAmelCase__ = variance * variance_noise
lowerCAmelCase__ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=snake_case__ , pred_original_sample=snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : torch.FloatTensor , snake_case__ : torch.FloatTensor , snake_case__ : torch.IntTensor , ):
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
lowerCAmelCase__ = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
lowerCAmelCase__ = timesteps.to(original_samples.device )
lowerCAmelCase__ = alphas_cumprod[timesteps] ** 0.5
lowerCAmelCase__ = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
lowerCAmelCase__ = sqrt_alpha_prod.unsqueeze(-1 )
lowerCAmelCase__ = (1 - alphas_cumprod[timesteps]) ** 0.5
lowerCAmelCase__ = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
lowerCAmelCase__ = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
lowerCAmelCase__ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 710 | """simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a_ ( __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : str = LayoutLMTokenizer
UpperCamelCase_ : List[Any] = LayoutLMTokenizerFast
UpperCamelCase_ : Dict = True
UpperCamelCase_ : Any = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setUp()
lowerCAmelCase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCAmelCase__ = 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 _SCREAMING_SNAKE_CASE ( self : int , **snake_case__ : Union[str, Any] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : Tuple ):
lowerCAmelCase__ = """UNwant\u00E9d,running"""
lowerCAmelCase__ = """unwanted, running"""
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.tokenizer_class(self.vocab_file )
lowerCAmelCase__ = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(snake_case__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [7, 4, 5, 10, 8, 9] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
pass
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCAmelCase : Union[str, Any] = TypeVar("KT")
__lowerCAmelCase : Optional[int] = TypeVar("VT")
class a_ ( Generic[KT, VT] ):
def __init__( self : int , snake_case__ : KT | str = "root" , snake_case__ : VT | None = None ):
lowerCAmelCase__ = key
lowerCAmelCase__ = value
lowerCAmelCase__ = []
def __repr__( self : str ):
return F"""Node({self.key}: {self.value})"""
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return len(self.forward )
class a_ ( Generic[KT, VT] ):
def __init__( self : int , snake_case__ : float = 0.5 , snake_case__ : int = 16 ):
lowerCAmelCase__ = Node[KT, VT]()
lowerCAmelCase__ = 0
lowerCAmelCase__ = p
lowerCAmelCase__ = max_level
def __str__( self : str ):
lowerCAmelCase__ = list(self )
if len(snake_case__ ) == 0:
return F"""SkipList(level={self.level})"""
lowerCAmelCase__ = max((len(str(snake_case__ ) ) for item in items) , default=4 )
lowerCAmelCase__ = max(snake_case__ , 4 ) + 4
lowerCAmelCase__ = self.head
lowerCAmelCase__ = []
lowerCAmelCase__ = node.forward.copy()
lines.append(F"""[{node.key}]""".ljust(snake_case__ , """-""" ) + """* """ * len(snake_case__ ) )
lines.append(""" """ * label_size + """| """ * len(snake_case__ ) )
while len(node.forward ) != 0:
lowerCAmelCase__ = node.forward[0]
lines.append(
F"""[{node.key}]""".ljust(snake_case__ , """-""" )
+ """ """.join(str(n.key ) if n.key == node.key else """|""" for n in forwards ) )
lines.append(""" """ * label_size + """| """ * len(snake_case__ ) )
lowerCAmelCase__ = node.forward
lines.append("""None""".ljust(snake_case__ ) + """* """ * len(snake_case__ ) )
return F"""SkipList(level={self.level})\n""" + "\n".join(snake_case__ )
def __iter__( self : Dict ):
lowerCAmelCase__ = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
lowerCAmelCase__ = node.forward[0]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , snake_case__ : List[str] ):
lowerCAmelCase__ = []
lowerCAmelCase__ = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
lowerCAmelCase__ = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(snake_case__ )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : KT ):
lowerCAmelCase__ , lowerCAmelCase__ = self._locate_node(snake_case__ )
if node is not None:
for i, update_node in enumerate(snake_case__ ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
lowerCAmelCase__ = node.forward[i]
else:
lowerCAmelCase__ = update_node.forward[:i]
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : KT , snake_case__ : VT ):
lowerCAmelCase__ , lowerCAmelCase__ = self._locate_node(snake_case__ )
if node is not None:
lowerCAmelCase__ = value
else:
lowerCAmelCase__ = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , snake_case__ ):
update_vector.append(self.head )
lowerCAmelCase__ = level
lowerCAmelCase__ = Node(snake_case__ , snake_case__ )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(snake_case__ )
else:
lowerCAmelCase__ = new_node
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : VT ):
lowerCAmelCase__ , lowerCAmelCase__ = self._locate_node(snake_case__ )
if node is not None:
return node.value
return None
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
skip_list.insert("""Key1""" , 3 )
skip_list.insert("""Key2""" , 12 )
skip_list.insert("""Key3""" , 41 )
skip_list.insert("""Key4""" , -19 )
lowerCAmelCase__ = skip_list.head
lowerCAmelCase__ = {}
while node.level != 0:
lowerCAmelCase__ = node.forward[0]
lowerCAmelCase__ = node.value
assert len(lowerCamelCase__ ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
skip_list.insert("""Key1""" , 10 )
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""Key5""" , 7 )
skip_list.insert("""Key7""" , 10 )
skip_list.insert("""Key10""" , 5 )
skip_list.insert("""Key7""" , 7 )
skip_list.insert("""Key5""" , 5 )
skip_list.insert("""Key10""" , 10 )
lowerCAmelCase__ = skip_list.head
lowerCAmelCase__ = {}
while node.level != 0:
lowerCAmelCase__ = node.forward[0]
lowerCAmelCase__ = node.value
if len(lowerCamelCase__ ) != 4:
print()
assert len(lowerCamelCase__ ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
assert skip_list.find("""Some key""" ) is None
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
skip_list.insert("""Key2""" , 20 )
assert skip_list.find("""Key2""" ) == 20
skip_list.insert("""Some Key""" , 10 )
skip_list.insert("""Key2""" , 8 )
skip_list.insert("""V""" , 13 )
assert skip_list.find("""Y""" ) is None
assert skip_list.find("""Key2""" ) == 8
assert skip_list.find("""Some Key""" ) == 10
assert skip_list.find("""V""" ) == 13
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
skip_list.delete("""Some key""" )
assert len(skip_list.head.forward ) == 0
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""Key2""" ) is None
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) == 14
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""X""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key1""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) is None
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 142 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""X""" )
def traverse_keys(lowerCamelCase__ ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(lowerCamelCase__ )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def _UpperCAmelCase ( ):
"""simple docstring"""
def is_sorted(lowerCamelCase__ ):
return all(next_item >= item for item, next_item in zip(lowerCamelCase__ , lst[1:] ) )
lowerCAmelCase__ = SkipList()
for i in range(10 ):
skip_list.insert(lowerCamelCase__ , lowerCamelCase__ )
assert is_sorted(list(lowerCamelCase__ ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(lowerCamelCase__ ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(lowerCamelCase__ ) )
def _UpperCAmelCase ( ):
"""simple docstring"""
for _ in range(100 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = SkipList()
skip_list.insert(2 , """2""" )
skip_list.insert(4 , """4""" )
skip_list.insert(6 , """4""" )
skip_list.insert(4 , """5""" )
skip_list.insert(8 , """4""" )
skip_list.insert(9 , """4""" )
skip_list.delete(4 )
print(lowerCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 711 | """simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase : Any = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
+ "FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
+ "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
+ "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
+ "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
+ "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
+ "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
+ "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
+ "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
+ "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
+ "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
+ "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
+ "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
+ "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
+ "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
+ "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
+ "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
+ "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
+ "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
+ "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
+ "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
+ "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
+ "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
+ "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
+ "6DCC4024FFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
+ "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
+ "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
+ "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
+ "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
+ "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
+ "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
+ "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
+ "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
+ "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
+ "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
+ "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
+ "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
+ "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
+ "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
+ "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
+ "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
+ "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
+ "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
+ "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
+ "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
+ "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
+ "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
+ "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
+ "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
+ "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
+ "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
+ "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
+ "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
+ "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
+ "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
+ "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
+ "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
+ "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
+ "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
+ "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
+ "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
+ "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
+ "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
+ "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
+ "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
+ "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
+ "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
base=16,
),
"generator": 2,
},
}
class a_ :
def __init__( self : List[str] , snake_case__ : int = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
lowerCAmelCase__ = primes[group]["""prime"""]
lowerCAmelCase__ = primes[group]["""generator"""]
lowerCAmelCase__ = int(hexlify(urandom(32 ) ) , base=16 )
def _SCREAMING_SNAKE_CASE ( self : Any ):
return hex(self.__private_key )[2:]
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = pow(self.generator , self.__private_key , self.prime )
return hex(snake_case__ )[2:]
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= key <= self.prime - 2
and pow(snake_case__ , (self.prime - 1) // 2 , self.prime ) == 1
)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : str ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
if not self.is_valid_public_key(snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , self.__private_key , self.prime )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : int , snake_case__ : int ):
# check if the other public key is valid based on NIST SP800-56
return (
2 <= remote_public_key_str <= prime - 2
and pow(snake_case__ , (prime - 1) // 2 , snake_case__ ) == 1
)
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : str , snake_case__ : str , snake_case__ : int = 14 ):
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = int(snake_case__ , base=16 )
lowerCAmelCase__ = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(snake_case__ , snake_case__ ):
raise ValueError("""Invalid public key""" )
lowerCAmelCase__ = pow(snake_case__ , snake_case__ , snake_case__ )
return shaaaa(str(snake_case__ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
__lowerCAmelCase : Tuple = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class a_ ( unittest.TestCase ):
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Optional[int] ):
lowerCAmelCase__ = TOKEN
HfFolder.save_token(snake_case__ )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Optional[Any] ):
try:
delete_repo(token=cls._token , repo_id="""test-model-flax""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-model-flax-org""" )
except HTTPError:
pass
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
lowerCAmelCase__ = FlaxBertModel(snake_case__ )
model.push_to_hub("""test-model-flax""" , use_auth_token=self._token )
lowerCAmelCase__ = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
lowerCAmelCase__ = flatten_dict(unfreeze(model.params ) )
lowerCAmelCase__ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
lowerCAmelCase__ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(snake_case__ , 1E-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id="""test-model-flax""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(snake_case__ , repo_id="""test-model-flax""" , push_to_hub=snake_case__ , use_auth_token=self._token )
lowerCAmelCase__ = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
lowerCAmelCase__ = flatten_dict(unfreeze(model.params ) )
lowerCAmelCase__ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
lowerCAmelCase__ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(snake_case__ , 1E-3 , msg=F"""{key} not identical""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
lowerCAmelCase__ = FlaxBertModel(snake_case__ )
model.push_to_hub("""valid_org/test-model-flax-org""" , use_auth_token=self._token )
lowerCAmelCase__ = FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org""" )
lowerCAmelCase__ = flatten_dict(unfreeze(model.params ) )
lowerCAmelCase__ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
lowerCAmelCase__ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(snake_case__ , 1E-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-model-flax-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
snake_case__ , repo_id="""valid_org/test-model-flax-org""" , push_to_hub=snake_case__ , use_auth_token=self._token )
lowerCAmelCase__ = FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org""" )
lowerCAmelCase__ = flatten_dict(unfreeze(model.params ) )
lowerCAmelCase__ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
lowerCAmelCase__ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(snake_case__ , 1E-3 , msg=F"""{key} not identical""" )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = True
lowerCAmelCase__ = flatten_dict(modela.params )
lowerCAmelCase__ = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
lowerCAmelCase__ = False
return models_are_equal
@require_flax
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Dict ):
lowerCAmelCase__ = BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
lowerCAmelCase__ = FlaxBertModel(snake_case__ )
lowerCAmelCase__ = """bert"""
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(snake_case__ , snake_case__ ) )
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = FlaxBertModel.from_pretrained(snake_case__ )
lowerCAmelCase__ = FlaxBertModel.from_pretrained(snake_case__ , subfolder=snake_case__ )
self.assertTrue(check_models_equal(snake_case__ , snake_case__ ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
lowerCAmelCase__ = FlaxBertModel(snake_case__ )
lowerCAmelCase__ = """bert"""
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(snake_case__ , snake_case__ ) , max_shard_size="""10KB""" )
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = FlaxBertModel.from_pretrained(snake_case__ )
lowerCAmelCase__ = FlaxBertModel.from_pretrained(snake_case__ , subfolder=snake_case__ )
self.assertTrue(check_models_equal(snake_case__ , snake_case__ ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = """bert"""
lowerCAmelCase__ = """hf-internal-testing/tiny-random-bert-subfolder"""
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = FlaxBertModel.from_pretrained(snake_case__ )
lowerCAmelCase__ = FlaxBertModel.from_pretrained(snake_case__ , subfolder=snake_case__ )
self.assertIsNotNone(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = """bert"""
lowerCAmelCase__ = """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = FlaxBertModel.from_pretrained(snake_case__ )
lowerCAmelCase__ = FlaxBertModel.from_pretrained(snake_case__ , subfolder=snake_case__ )
self.assertIsNotNone(snake_case__ )
| 712 | """simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
lowerCAmelCase__ = nn.Parameter(lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = np.asarray(weights[0] )
lowerCAmelCase__ = np.asarray(weights[1] )
lowerCAmelCase__ = np.asarray(weights[2] )
lowerCAmelCase__ = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowerCamelCase__ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCamelCase__ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowerCamelCase__ ).view(-1 , lowerCamelCase__ ).contiguous().transpose(0 , 1 ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = weights[0][0][0]
lowerCAmelCase__ = np.asarray(layer_norm_a[0] )
lowerCAmelCase__ = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# lsh weights + output
lowerCAmelCase__ = weights[0][1]
if len(lowerCamelCase__ ) < 4:
set_layer_weights_in_torch_lsh(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
else:
set_layer_weights_in_torch_local(lowerCamelCase__ , torch_block.attention , lowerCamelCase__ )
# intermediate weighs
lowerCAmelCase__ = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowerCamelCase__ ) == 4:
lowerCAmelCase__ = intermediate_weights[2]
# layernorm 2
lowerCAmelCase__ = np.asarray(intermediate_weights[0][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# intermediate dense
lowerCAmelCase__ = np.asarray(intermediate_weights[1][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
# intermediate out
lowerCAmelCase__ = np.asarray(intermediate_weights[4][0] )
lowerCAmelCase__ = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = torch_model.reformer
# word embeds
lowerCAmelCase__ = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCamelCase__ ) , )
if isinstance(weights[3] , lowerCamelCase__ ):
lowerCAmelCase__ = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowerCAmelCase__ = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f"""{position_embeddings[emb_idx]} emb does not match"""
lowerCAmelCase__ = nn.Parameter(torch.tensor(lowerCamelCase__ ) )
lowerCAmelCase__ = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowerCamelCase__ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowerCAmelCase__ = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# output layer norm
lowerCAmelCase__ = np.asarray(weights[7][0] )
lowerCAmelCase__ = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) , )
# output embeddings
lowerCAmelCase__ = np.asarray(weights[9][0] )
lowerCAmelCase__ = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowerCamelCase__ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCamelCase__ ) , )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = ReformerConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = ReformerModelWithLMHead(lowerCamelCase__ )
with open(lowerCamelCase__ , """rb""" ) as f:
lowerCAmelCase__ = pickle.load(lowerCamelCase__ )["""weights"""]
set_model_weights_in_torch(lowerCamelCase__ , lowerCamelCase__ , config.hidden_size )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
__lowerCAmelCase : Dict = True
except ImportError:
__lowerCAmelCase : Dict = False
try:
from torch.hub import _get_torch_home
__lowerCAmelCase : Optional[Any] = _get_torch_home()
except ImportError:
__lowerCAmelCase : List[Any] = os.path.expanduser(
os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
)
__lowerCAmelCase : Optional[Any] = os.path.join(torch_cache_home, "transformers")
__lowerCAmelCase : Any = "https://cdn.huggingface.co"
__lowerCAmelCase : Tuple = "https://s3.amazonaws.com/models.huggingface.co/bert"
__lowerCAmelCase : List[Any] = "/".join(str(Path(__file__).resolve()).split("/")[:-1])
__lowerCAmelCase : Any = os.path.join(PATH, "config.yaml")
__lowerCAmelCase : Union[str, Any] = os.path.join(PATH, "attributes.txt")
__lowerCAmelCase : List[str] = os.path.join(PATH, "objects.txt")
__lowerCAmelCase : int = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
__lowerCAmelCase : Union[str, Any] = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
__lowerCAmelCase : Tuple = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
__lowerCAmelCase : Optional[Any] = "pytorch_model.bin"
__lowerCAmelCase : Any = "config.yaml"
def _UpperCAmelCase ( lowerCamelCase__=OBJECTS , lowerCamelCase__=ATTRIBUTES ):
"""simple docstring"""
lowerCAmelCase__ = []
with open(lowerCamelCase__ ) as f:
for object in f.readlines():
vg_classes.append(object.split(""",""" )[0].lower().strip() )
lowerCAmelCase__ = []
with open(lowerCamelCase__ ) as f:
for object in f.readlines():
vg_attrs.append(object.split(""",""" )[0].lower().strip() )
return vg_classes, vg_attrs
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = OrderedDict()
with open(lowerCamelCase__ , """rb""" ) as f:
lowerCAmelCase__ = pkl.load(lowerCamelCase__ )["""model"""]
for k in copy.deepcopy(list(ckp.keys() ) ):
lowerCAmelCase__ = ckp.pop(lowerCamelCase__ )
if isinstance(lowerCamelCase__ , np.ndarray ):
lowerCAmelCase__ = torch.tensor(lowerCamelCase__ )
else:
assert isinstance(lowerCamelCase__ , torch.tensor ), type(lowerCamelCase__ )
lowerCAmelCase__ = v
return r
class a_ :
UpperCamelCase_ : List[str] = {}
def __init__( self : Tuple , snake_case__ : dict , snake_case__ : str = "root" , snake_case__ : int=0 ):
lowerCAmelCase__ = name
lowerCAmelCase__ = level
lowerCAmelCase__ = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
lowerCAmelCase__ = copy.deepcopy(snake_case__ )
lowerCAmelCase__ = copy.deepcopy(snake_case__ )
if isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase__ = Config(snake_case__ , name=snake_case__ , level=level + 1 )
lowerCAmelCase__ = v
setattr(self , snake_case__ , snake_case__ )
lowerCAmelCase__ = d
def __repr__( self : Optional[Any] ):
return str(list((self._pointer.keys()) ) )
def __setattr__( self : Optional[Any] , snake_case__ : Optional[int] , snake_case__ : Dict ):
lowerCAmelCase__ = val
lowerCAmelCase__ = val
lowerCAmelCase__ = key.split(""".""" )
lowerCAmelCase__ = len(snake_case__ ) - 1
lowerCAmelCase__ = self._pointer
if len(snake_case__ ) > 1:
for i, l in enumerate(snake_case__ ):
if hasattr(self , snake_case__ ) and isinstance(getattr(self , snake_case__ ) , snake_case__ ):
setattr(getattr(self , snake_case__ ) , """.""".join(levels[i:] ) , snake_case__ )
if l == last_level:
lowerCAmelCase__ = val
else:
lowerCAmelCase__ = pointer[l]
def _SCREAMING_SNAKE_CASE ( self : int ):
return self._pointer
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Dict , snake_case__ : Tuple ):
with open(F"""{file_name}""" , """w""" ) as stream:
dump(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : List[Any] , snake_case__ : List[str] ):
with open(F"""{file_name}""" , """w""" ) as stream:
json.dump(snake_case__ , snake_case__ )
@staticmethod
def _SCREAMING_SNAKE_CASE ( snake_case__ : List[Any] ):
with open(snake_case__ ) as stream:
lowerCAmelCase__ = load(snake_case__ , Loader=snake_case__ )
return data
def __str__( self : Any ):
lowerCAmelCase__ = """ """
if self._name != "root":
lowerCAmelCase__ = F"""{t * (self._level-1)}{self._name}:\n"""
else:
lowerCAmelCase__ = """"""
lowerCAmelCase__ = self._level
for i, (k, v) in enumerate(self._pointer.items() ):
if isinstance(snake_case__ , snake_case__ ):
r += F"""{t * (self._level)}{v}\n"""
self._level += 1
else:
r += F"""{t * (self._level)}{k}: {v} ({type(snake_case__ ).__name__})\n"""
lowerCAmelCase__ = level
return r[:-1]
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Any , snake_case__ : str , **snake_case__ : Optional[int] ):
lowerCAmelCase__ , lowerCAmelCase__ = cls.get_config_dict(snake_case__ , **snake_case__ )
return cls(snake_case__ )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Dict , snake_case__ : str , **snake_case__ : Optional[Any] ):
lowerCAmelCase__ = kwargs.pop("""cache_dir""" , snake_case__ )
lowerCAmelCase__ = kwargs.pop("""force_download""" , snake_case__ )
lowerCAmelCase__ = kwargs.pop("""resume_download""" , snake_case__ )
lowerCAmelCase__ = kwargs.pop("""proxies""" , snake_case__ )
lowerCAmelCase__ = kwargs.pop("""local_files_only""" , snake_case__ )
if os.path.isdir(snake_case__ ):
lowerCAmelCase__ = os.path.join(snake_case__ , snake_case__ )
elif os.path.isfile(snake_case__ ) or is_remote_url(snake_case__ ):
lowerCAmelCase__ = pretrained_model_name_or_path
else:
lowerCAmelCase__ = hf_bucket_url(snake_case__ , filename=snake_case__ , use_cdn=snake_case__ )
try:
# Load from URL or cache if already cached
lowerCAmelCase__ = cached_path(
snake_case__ , cache_dir=snake_case__ , force_download=snake_case__ , proxies=snake_case__ , resume_download=snake_case__ , local_files_only=snake_case__ , )
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
lowerCAmelCase__ = Config.load_yaml(snake_case__ )
except EnvironmentError:
lowerCAmelCase__ = """Can't load config for"""
raise EnvironmentError(snake_case__ )
if resolved_config_file == config_file:
print("""loading configuration file from path""" )
else:
print("""loading configuration file cache""" )
return Config.load_yaml(snake_case__ ), kwargs
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = torch.load("""dump.pt""" , map_location=in_tensor.device )
lowerCAmelCase__ = in_tensor.numpy()
lowerCAmelCase__ = out_tensor.numpy()[0]
print(na.shape , na[0, 0, :5] )
print(na.shape , na[0, 0, :5] )
assert np.allclose(lowerCamelCase__ , lowerCamelCase__ , rtol=0.01 , atol=0.1 ), (
f"""{sum([1 for x in np.isclose(lowerCamelCase__ , lowerCamelCase__ , rtol=0.01 , atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %"""
" element-wise mismatch"
)
raise Exception("""tensors are all good""" )
# Hugging face functions below
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = urlparse(lowerCamelCase__ )
return parsed.scheme in ("http", "https")
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=True ):
"""simple docstring"""
lowerCAmelCase__ = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
lowerCAmelCase__ = """/""" not in model_id
if legacy_format:
return f"""{endpoint}/{model_id}-{filename}"""
else:
return f"""{endpoint}/{model_id}/{filename}"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=0 , lowerCamelCase__=None , ):
"""simple docstring"""
lowerCAmelCase__ = """python/{}""".format(sys.version.split()[0] )
if _torch_available:
ua += "; torch/{}".format(torch.__version__ )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
ua += "; " + "; ".join("""{}/{}""".format(lowerCamelCase__ , lowerCamelCase__ ) for k, v in user_agent.items() )
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
ua += "; " + user_agent
lowerCAmelCase__ = {"""user-agent""": ua}
if resume_size > 0:
lowerCAmelCase__ = """bytes=%d-""" % (resume_size,)
lowerCAmelCase__ = requests.get(lowerCamelCase__ , stream=lowerCamelCase__ , proxies=lowerCamelCase__ , headers=lowerCamelCase__ )
if response.status_code == 416: # Range not satisfiable
return
lowerCAmelCase__ = response.headers.get("""Content-Length""" )
lowerCAmelCase__ = resume_size + int(lowerCamelCase__ ) if content_length is not None else None
lowerCAmelCase__ = tqdm(
unit="""B""" , unit_scale=lowerCamelCase__ , total=lowerCamelCase__ , initial=lowerCamelCase__ , desc="""Downloading""" , )
for chunk in response.iter_content(chunk_size=1024 ):
if chunk: # filter out keep-alive new chunks
progress.update(len(lowerCamelCase__ ) )
temp_file.write(lowerCamelCase__ )
progress.close()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=False , lowerCamelCase__=None , lowerCamelCase__=10 , lowerCamelCase__=False , lowerCamelCase__=None , lowerCamelCase__=False , ):
"""simple docstring"""
if cache_dir is None:
lowerCAmelCase__ = TRANSFORMERS_CACHE
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = str(lowerCamelCase__ )
os.makedirs(lowerCamelCase__ , exist_ok=lowerCamelCase__ )
lowerCAmelCase__ = None
if not local_files_only:
try:
lowerCAmelCase__ = requests.head(lowerCamelCase__ , allow_redirects=lowerCamelCase__ , proxies=lowerCamelCase__ , timeout=lowerCamelCase__ )
if response.status_code == 200:
lowerCAmelCase__ = response.headers.get("""ETag""" )
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
lowerCAmelCase__ = url_to_filename(lowerCamelCase__ , lowerCamelCase__ )
# get cache path to put the file
lowerCAmelCase__ = os.path.join(lowerCamelCase__ , lowerCamelCase__ )
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(lowerCamelCase__ ):
return cache_path
else:
lowerCAmelCase__ = [
file
for file in fnmatch.filter(os.listdir(lowerCamelCase__ ) , filename + """.*""" )
if not file.endswith(""".json""" ) and not file.endswith(""".lock""" )
]
if len(lowerCamelCase__ ) > 0:
return os.path.join(lowerCamelCase__ , matching_files[-1] )
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
"""Cannot find the requested files in the cached path and outgoing traffic has been"""
""" disabled. To enable model look-ups and downloads online, set 'local_files_only'"""
""" to False.""" )
return None
# From now on, etag is not None.
if os.path.exists(lowerCamelCase__ ) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
lowerCAmelCase__ = cache_path + """.lock"""
with FileLock(lowerCamelCase__ ):
# If the download just completed while the lock was activated.
if os.path.exists(lowerCamelCase__ ) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
lowerCAmelCase__ = cache_path + """.incomplete"""
@contextmanager
def _resumable_file_manager():
with open(lowerCamelCase__ , """a+b""" ) as f:
yield f
lowerCAmelCase__ = _resumable_file_manager
if os.path.exists(lowerCamelCase__ ):
lowerCAmelCase__ = os.stat(lowerCamelCase__ ).st_size
else:
lowerCAmelCase__ = 0
else:
lowerCAmelCase__ = partial(tempfile.NamedTemporaryFile , dir=lowerCamelCase__ , delete=lowerCamelCase__ )
lowerCAmelCase__ = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
"""%s not found in cache or force_download set to True, downloading to %s""" , lowerCamelCase__ , temp_file.name , )
http_get(
lowerCamelCase__ , lowerCamelCase__ , proxies=lowerCamelCase__ , resume_size=lowerCamelCase__ , user_agent=lowerCamelCase__ , )
os.replace(temp_file.name , lowerCamelCase__ )
lowerCAmelCase__ = {"""url""": url, """etag""": etag}
lowerCAmelCase__ = cache_path + """.json"""
with open(lowerCamelCase__ , """w""" ) as meta_file:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
return cache_path
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
lowerCAmelCase__ = url.encode("""utf-8""" )
lowerCAmelCase__ = shaaaa(lowerCamelCase__ )
lowerCAmelCase__ = url_hash.hexdigest()
if etag:
lowerCAmelCase__ = etag.encode("""utf-8""" )
lowerCAmelCase__ = shaaaa(lowerCamelCase__ )
filename += "." + etag_hash.hexdigest()
if url.endswith(""".h5""" ):
filename += ".h5"
return filename
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=False , lowerCamelCase__=None , lowerCamelCase__=False , lowerCamelCase__=None , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=False , ):
"""simple docstring"""
if cache_dir is None:
lowerCAmelCase__ = TRANSFORMERS_CACHE
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = str(lowerCamelCase__ )
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCAmelCase__ = str(lowerCamelCase__ )
if is_remote_url(lowerCamelCase__ ):
# URL, so get it from the cache (downloading if necessary)
lowerCAmelCase__ = get_from_cache(
lowerCamelCase__ , cache_dir=lowerCamelCase__ , force_download=lowerCamelCase__ , proxies=lowerCamelCase__ , resume_download=lowerCamelCase__ , user_agent=lowerCamelCase__ , local_files_only=lowerCamelCase__ , )
elif os.path.exists(lowerCamelCase__ ):
# File, and it exists.
lowerCAmelCase__ = url_or_filename
elif urlparse(lowerCamelCase__ ).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("""file {} not found""".format(lowerCamelCase__ ) )
else:
# Something unknown
raise ValueError("""unable to parse {} as a URL or as a local path""".format(lowerCamelCase__ ) )
if extract_compressed_file:
if not is_zipfile(lowerCamelCase__ ) and not tarfile.is_tarfile(lowerCamelCase__ ):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
lowerCAmelCase__ , lowerCAmelCase__ = os.path.split(lowerCamelCase__ )
lowerCAmelCase__ = output_file.replace(""".""" , """-""" ) + """-extracted"""
lowerCAmelCase__ = os.path.join(lowerCamelCase__ , lowerCamelCase__ )
if os.path.isdir(lowerCamelCase__ ) and os.listdir(lowerCamelCase__ ) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
lowerCAmelCase__ = output_path + """.lock"""
with FileLock(lowerCamelCase__ ):
shutil.rmtree(lowerCamelCase__ , ignore_errors=lowerCamelCase__ )
os.makedirs(lowerCamelCase__ )
if is_zipfile(lowerCamelCase__ ):
with ZipFile(lowerCamelCase__ , """r""" ) as zip_file:
zip_file.extractall(lowerCamelCase__ )
zip_file.close()
elif tarfile.is_tarfile(lowerCamelCase__ ):
lowerCAmelCase__ = tarfile.open(lowerCamelCase__ )
tar_file.extractall(lowerCamelCase__ )
tar_file.close()
else:
raise EnvironmentError("""Archive format of {} could not be identified""".format(lowerCamelCase__ ) )
return output_path_extracted
return output_path
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__="," ):
"""simple docstring"""
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if os.path.isfile(lowerCamelCase__ ):
with open(lowerCamelCase__ ) as f:
lowerCAmelCase__ = eval(f.read() )
else:
lowerCAmelCase__ = requests.get(lowerCamelCase__ )
try:
lowerCAmelCase__ = requests.json()
except Exception:
lowerCAmelCase__ = req.content.decode()
assert data is not None, "could not connect"
try:
lowerCAmelCase__ = eval(lowerCamelCase__ )
except Exception:
lowerCAmelCase__ = data.split("""\n""" )
req.close()
return data
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = requests.get(lowerCamelCase__ )
lowerCAmelCase__ = np.array(Image.open(BytesIO(response.content ) ) )
return img
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = url.split("""/""" )[-1]
if fn not in os.listdir(os.getcwd() ):
wget.download(lowerCamelCase__ )
with open(lowerCamelCase__ , """rb""" ) as stream:
lowerCAmelCase__ = pkl.load(lowerCamelCase__ )
lowerCAmelCase__ = weights.pop("""model""" )
lowerCAmelCase__ = {}
for k, v in model.items():
lowerCAmelCase__ = torch.from_numpy(lowerCamelCase__ )
if "running_var" in k:
lowerCAmelCase__ = torch.tensor([0] )
lowerCAmelCase__ = k.replace("""running_var""" , """num_batches_tracked""" )
lowerCAmelCase__ = zero
return new
def _UpperCAmelCase ( ):
"""simple docstring"""
print(f"""{os.path.abspath(os.path.join(lowerCamelCase__ , os.pardir ) )}/demo.ipynb""" )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__="RGB" ):
"""simple docstring"""
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if os.path.isfile(lowerCamelCase__ ):
lowerCAmelCase__ = cva.imread(lowerCamelCase__ )
else:
lowerCAmelCase__ = get_image_from_url(lowerCamelCase__ )
assert img is not None, f"""could not connect to: {im}"""
lowerCAmelCase__ = cva.cvtColor(lowerCamelCase__ , cva.COLOR_BGR2RGB )
if input_format == "RGB":
lowerCAmelCase__ = img[:, :, ::-1]
return img
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=1 ):
"""simple docstring"""
return (images[i : i + batch] for i in range(0 , len(lowerCamelCase__ ) , lowerCamelCase__ ))
| 713 | """simple docstring"""
import os
from math import logaa
def _UpperCAmelCase ( lowerCamelCase__ = "base_exp.txt" ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) ):
lowerCAmelCase__ , lowerCAmelCase__ = list(map(lowerCamelCase__ , line.split(""",""" ) ) )
if x * logaa(lowerCamelCase__ ) > largest:
lowerCAmelCase__ = x * logaa(lowerCamelCase__ )
lowerCAmelCase__ = i + 1
return result
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
lowerCAmelCase__ = key.replace("""heads.cmd.mim_head.cls.predictions""" , """mmm_image_head""" )
lowerCAmelCase__ = key.replace("""heads.cmd.mlm_head.cls.predictions""" , """mmm_text_head""" )
lowerCAmelCase__ = key.replace("""heads.cmd.itm_head.cls""" , """itm_head""" )
lowerCAmelCase__ = key.replace("""heads.cmd.itm_head.pooler""" , """itm_head.pooler""" )
lowerCAmelCase__ = key.replace("""heads.cmd.clip_head.logit_scale""" , """flava.logit_scale""" )
lowerCAmelCase__ = key.replace("""heads.fairseq_mlm.cls.predictions""" , """mlm_head""" )
lowerCAmelCase__ = key.replace("""heads.imagenet.mim_head.cls.predictions""" , """mim_head""" )
lowerCAmelCase__ = key.replace("""mm_text_projection""" , """flava.text_to_mm_projection""" )
lowerCAmelCase__ = key.replace("""mm_image_projection""" , """flava.image_to_mm_projection""" )
lowerCAmelCase__ = key.replace("""image_encoder.module""" , """flava.image_model""" )
lowerCAmelCase__ = key.replace("""text_encoder.module""" , """flava.text_model""" )
lowerCAmelCase__ = key.replace("""mm_encoder.module.encoder.cls_token""" , """flava.multimodal_model.cls_token""" )
lowerCAmelCase__ = key.replace("""mm_encoder.module""" , """flava.multimodal_model""" )
lowerCAmelCase__ = key.replace("""text_projection""" , """flava.text_projection""" )
lowerCAmelCase__ = key.replace("""image_projection""" , """flava.image_projection""" )
lowerCAmelCase__ = value.float()
for key, value in codebook_state_dict.items():
lowerCAmelCase__ = value
return upgrade
@torch.no_grad()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
if config_path is not None:
lowerCAmelCase__ = FlavaConfig.from_pretrained(lowerCamelCase__ )
else:
lowerCAmelCase__ = FlavaConfig()
lowerCAmelCase__ = FlavaForPreTraining(lowerCamelCase__ ).eval()
lowerCAmelCase__ = convert_dalle_checkpoint(lowerCamelCase__ , lowerCamelCase__ , save_checkpoint=lowerCamelCase__ )
if os.path.exists(lowerCamelCase__ ):
lowerCAmelCase__ = torch.load(lowerCamelCase__ , map_location="""cpu""" )
else:
lowerCAmelCase__ = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location="""cpu""" )
lowerCAmelCase__ = upgrade_state_dict(lowerCamelCase__ , lowerCamelCase__ )
hf_model.load_state_dict(lowerCamelCase__ )
lowerCAmelCase__ = hf_model.state_dict()
lowerCAmelCase__ = count_parameters(lowerCamelCase__ )
lowerCAmelCase__ = count_parameters(lowerCamelCase__ ) + count_parameters(lowerCamelCase__ )
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-3 )
hf_model.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 714 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
while b:
lowerCAmelCase__ , lowerCAmelCase__ = b, a % b
return a
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(lowerCamelCase__ , a % b )
def _UpperCAmelCase ( ):
"""simple docstring"""
print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 674 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = BertConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = BertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Dict = 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(
"--bert_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."
)
__lowerCAmelCase : List[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 715 | """simple docstring"""
import os
def _UpperCAmelCase ( ):
"""simple docstring"""
lowerCAmelCase__ = os.path.dirname(os.path.realpath(lowerCamelCase__ ) )
lowerCAmelCase__ = os.path.join(lowerCamelCase__ , """triangle.txt""" )
with open(lowerCamelCase__ ) as f:
lowerCAmelCase__ = f.readlines()
lowerCAmelCase__ = []
for line in triangle:
lowerCAmelCase__ = []
for number in line.strip().split(""" """ ):
numbers_from_line.append(int(lowerCamelCase__ ) )
a.append(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
for j in range(len(a[i] ) ):
lowerCAmelCase__ = a[i - 1][j] if j != len(a[i - 1] ) else 0
lowerCAmelCase__ = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowerCamelCase__ , lowerCamelCase__ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 674 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ = 50 ):
"""simple docstring"""
lowerCAmelCase__ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F"{solution() = }")
| 716 | """simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase : Union[str, Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
__lowerCAmelCase : Optional[int] = json.load(f)
@require_torch
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Dict ):
return FSMTTokenizer.from_pretrained(snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : Any ):
lowerCAmelCase__ = FSMTForConditionalGeneration.from_pretrained(snake_case__ ).to(snake_case__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : Any , snake_case__ : int ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
lowerCAmelCase__ = F"""facebook/wmt19-{pair}"""
lowerCAmelCase__ = self.get_tokenizer(snake_case__ )
lowerCAmelCase__ = self.get_model(snake_case__ )
lowerCAmelCase__ = bleu_data[pair]["""src"""]
lowerCAmelCase__ = bleu_data[pair]["""tgt"""]
lowerCAmelCase__ = tokenizer(snake_case__ , return_tensors="""pt""" , truncation=snake_case__ , padding="""longest""" ).to(snake_case__ )
lowerCAmelCase__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
lowerCAmelCase__ = tokenizer.batch_decode(
snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
lowerCAmelCase__ = calculate_bleu(snake_case__ , snake_case__ )
print(snake_case__ )
self.assertGreaterEqual(scores["""bleu"""] , snake_case__ )
| 674 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class a_ :
def __init__( self : int , snake_case__ : List[Any] , snake_case__ : Tuple=13 , snake_case__ : int=7 , snake_case__ : Union[str, Any]=True , snake_case__ : Union[str, Any]=True , snake_case__ : Tuple=True , snake_case__ : int=True , snake_case__ : List[Any]=99 , snake_case__ : Dict=32 , snake_case__ : Any=2 , snake_case__ : List[str]=4 , snake_case__ : str=37 , snake_case__ : Optional[Any]="gelu" , snake_case__ : str=0.1 , snake_case__ : Dict=0.1 , snake_case__ : List[Any]=512 , snake_case__ : Any=16 , snake_case__ : List[Any]=2 , snake_case__ : Union[str, Any]=0.02 , snake_case__ : Union[str, Any]=3 , snake_case__ : List[str]=4 , snake_case__ : Optional[int]=None , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = 13
lowerCAmelCase__ = 7
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = 99
lowerCAmelCase__ = 32
lowerCAmelCase__ = 2
lowerCAmelCase__ = 4
lowerCAmelCase__ = 37
lowerCAmelCase__ = """gelu"""
lowerCAmelCase__ = 0.1
lowerCAmelCase__ = 0.1
lowerCAmelCase__ = 512
lowerCAmelCase__ = 16
lowerCAmelCase__ = 2
lowerCAmelCase__ = 0.02
lowerCAmelCase__ = 3
lowerCAmelCase__ = 4
lowerCAmelCase__ = None
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ = None
if self.use_input_mask:
lowerCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ = None
if self.use_token_type_ids:
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase__ = RoFormerConfig(
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 , return_dict=snake_case__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : List[Any] , snake_case__ : Dict , snake_case__ : int , snake_case__ : Dict , snake_case__ : Union[str, Any] , snake_case__ : int , snake_case__ : str ):
lowerCAmelCase__ = TFRoFormerModel(config=snake_case__ )
lowerCAmelCase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
lowerCAmelCase__ = [input_ids, input_mask]
lowerCAmelCase__ = model(snake_case__ )
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : List[Any] , snake_case__ : Tuple , snake_case__ : int , snake_case__ : Dict , snake_case__ : Tuple , snake_case__ : int , snake_case__ : int ):
lowerCAmelCase__ = True
lowerCAmelCase__ = TFRoFormerForCausalLM(config=snake_case__ )
lowerCAmelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase__ = model(snake_case__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , snake_case__ : str , snake_case__ : Optional[Any] , snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : int , snake_case__ : List[Any] , snake_case__ : Tuple ):
lowerCAmelCase__ = TFRoFormerForMaskedLM(config=snake_case__ )
lowerCAmelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _SCREAMING_SNAKE_CASE ( self : Any , snake_case__ : Dict , snake_case__ : List[Any] , snake_case__ : Optional[Any] , snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : Any , snake_case__ : Tuple ):
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = TFRoFormerForSequenceClassification(config=snake_case__ )
lowerCAmelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Any , snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : List[str] , snake_case__ : List[Any] , snake_case__ : Tuple , snake_case__ : Dict ):
lowerCAmelCase__ = self.num_choices
lowerCAmelCase__ = TFRoFormerForMultipleChoice(config=snake_case__ )
lowerCAmelCase__ = tf.tile(tf.expand_dims(snake_case__ , 1 ) , (1, self.num_choices, 1) )
lowerCAmelCase__ = tf.tile(tf.expand_dims(snake_case__ , 1 ) , (1, self.num_choices, 1) )
lowerCAmelCase__ = tf.tile(tf.expand_dims(snake_case__ , 1 ) , (1, self.num_choices, 1) )
lowerCAmelCase__ = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _SCREAMING_SNAKE_CASE ( self : str , snake_case__ : Tuple , snake_case__ : int , snake_case__ : List[str] , snake_case__ : List[Any] , snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : Tuple ):
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = TFRoFormerForTokenClassification(config=snake_case__ )
lowerCAmelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase__ = model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : List[str] , snake_case__ : Union[str, Any] , snake_case__ : str , snake_case__ : Union[str, Any] , snake_case__ : Union[str, Any] , snake_case__ : str , snake_case__ : int , snake_case__ : Tuple ):
lowerCAmelCase__ = TFRoFormerForQuestionAnswering(config=snake_case__ )
lowerCAmelCase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
lowerCAmelCase__ = model(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 : int ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
(
(
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) , (
lowerCAmelCase__
) ,
) = config_and_inputs
lowerCAmelCase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class a_ ( __UpperCamelCase , __UpperCamelCase , unittest.TestCase ):
UpperCamelCase_ : Any = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : List[str] = (
{
"feature-extraction": TFRoFormerModel,
"fill-mask": TFRoFormerForMaskedLM,
"question-answering": TFRoFormerForQuestionAnswering,
"text-classification": TFRoFormerForSequenceClassification,
"text-generation": TFRoFormerForCausalLM,
"token-classification": TFRoFormerForTokenClassification,
"zero-shot": TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCamelCase_ : Tuple = False
UpperCamelCase_ : Tuple = False
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : List[Any] , snake_case__ : Tuple , snake_case__ : Optional[Any] , snake_case__ : List[Any] , snake_case__ : Any ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = TFRoFormerModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case__ )
@slow
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(snake_case__ )
@require_tf
class a_ ( unittest.TestCase ):
@slow
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
lowerCAmelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowerCAmelCase__ = model(snake_case__ )[0]
# TODO Replace vocab size
lowerCAmelCase__ = 50000
lowerCAmelCase__ = [1, 6, vocab_size]
self.assertEqual(output.shape , snake_case__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
lowerCAmelCase__ = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , snake_case__ , atol=1E-4 )
@require_tf
class a_ ( unittest.TestCase ):
UpperCamelCase_ : str = 1e-4
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ = tf.constant([[4, 10]] )
lowerCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
lowerCAmelCase__ = emba(input_ids.shape )
lowerCAmelCase__ = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(snake_case__ , snake_case__ , atol=self.tolerance )
def _SCREAMING_SNAKE_CASE ( self : Any ):
lowerCAmelCase__ = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
lowerCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 , embedding_dim=512 )
emba([2, 16, 512] )
lowerCAmelCase__ = emba.weight[:3, :5]
tf.debugging.assert_near(snake_case__ , snake_case__ , atol=self.tolerance )
@require_tf
class a_ ( unittest.TestCase ):
UpperCamelCase_ : Union[str, Any] = 1e-4
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
# 2,12,16,64
lowerCAmelCase__ = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
lowerCAmelCase__ = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 100
lowerCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
lowerCAmelCase__ = embed_positions([2, 16, 768] )[None, None, :, :]
lowerCAmelCase__ , lowerCAmelCase__ = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase__ = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
lowerCAmelCase__ = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , snake_case__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , snake_case__ , atol=self.tolerance )
| 717 | """simple docstring"""
import pprint
import requests
__lowerCAmelCase : Union[str, Any] = "https://zenquotes.io/api"
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _UpperCAmelCase ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = random_quotes()
pprint.pprint(response)
| 674 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__lowerCAmelCase : Dict = logging.get_logger("transformers.models.speecht5")
__lowerCAmelCase : List[Any] = {
"speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
"speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
"speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
"speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
__lowerCAmelCase : Optional[int] = {
"text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
"text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
__lowerCAmelCase : List[Any] = {
"speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
"speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
"speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
"speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
"speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
__lowerCAmelCase : Optional[int] = {
"speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
"speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
"speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
"speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
"speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
"speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
"speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
"speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
"speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
"speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
"speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
"speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
__lowerCAmelCase : int = {
"text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
__lowerCAmelCase : Any = {
"text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
__lowerCAmelCase : Dict = {
"encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
"encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
"encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
"encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
"encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
"encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
"encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
"encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
"encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
__lowerCAmelCase : List[Any] = {
"decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
"decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
"decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
"decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
"decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
"decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
"decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
"decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
"decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
"decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
"decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
"decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
"decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
__lowerCAmelCase : List[str] = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCAmelCase : int = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCAmelCase : Optional[Any] = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCAmelCase : List[Any] = []
__lowerCAmelCase : Dict = [
"encoder.version",
"encoder.layers.*.norm_k.weight",
"encoder.layers.*.norm_k.bias",
"decoder.version",
"decoder.layers.*.norm_k.weight",
"decoder.layers.*.norm_k.bias",
"decoder.pos_emb.pe_k",
"speech_encoder_prenet.embed_positions._float_tensor",
"text_decoder_prenet.embed_positions._float_tensor",
]
__lowerCAmelCase : Tuple = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"speech_decoder_prenet.*",
"speech_decoder_postnet.*",
]
__lowerCAmelCase : Union[str, Any] = IGNORE_KEYS + [
"encoder.proj",
"speech_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
__lowerCAmelCase : Dict = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
for attribute in key.split(""".""" ):
lowerCAmelCase__ = getattr(lowerCamelCase__ , lowerCamelCase__ )
if weight_type is not None:
lowerCAmelCase__ = getattr(lowerCamelCase__ , lowerCamelCase__ ).shape
else:
lowerCAmelCase__ = 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":
lowerCAmelCase__ = value
elif weight_type == "weight_g":
lowerCAmelCase__ = value
elif weight_type == "weight_v":
lowerCAmelCase__ = value
elif weight_type == "bias":
lowerCAmelCase__ = value
elif weight_type == "running_mean":
lowerCAmelCase__ = value
elif weight_type == "running_var":
lowerCAmelCase__ = value
elif weight_type == "num_batches_tracked":
lowerCAmelCase__ = value
else:
lowerCAmelCase__ = value
logger.info(f"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.""" )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
for key in ignore_keys:
if key.endswith(""".*""" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowerCAmelCase__ , lowerCAmelCase__ = key.split(""".*.""" )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = []
if task == "s2t":
lowerCAmelCase__ = hf_model.speechta.encoder.prenet.feature_encoder
lowerCAmelCase__ = MAPPING_S2T
lowerCAmelCase__ = IGNORE_KEYS_S2T
elif task == "t2s":
lowerCAmelCase__ = None
lowerCAmelCase__ = MAPPING_T2S
lowerCAmelCase__ = IGNORE_KEYS_T2S
elif task == "s2s":
lowerCAmelCase__ = hf_model.speechta.encoder.prenet.feature_encoder
lowerCAmelCase__ = MAPPING_S2S
lowerCAmelCase__ = IGNORE_KEYS_S2S
else:
raise ValueError(f"""Unsupported task: {task}""" )
for name, value in fairseq_dict.items():
if should_ignore(lowerCamelCase__ , lowerCamelCase__ ):
logger.info(f"""{name} was ignored""" )
continue
lowerCAmelCase__ = False
if "conv_layers" in name:
load_conv_layer(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , hf_model.config.feat_extract_norm == """group""" , )
lowerCAmelCase__ = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
lowerCAmelCase__ , lowerCAmelCase__ = key.split(""".*.""" )
if prefix in name and suffix in name:
lowerCAmelCase__ = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
lowerCAmelCase__ = True
if "*" in mapped_key:
lowerCAmelCase__ = name.split(lowerCamelCase__ )[0].split(""".""" )[-2]
lowerCAmelCase__ = mapped_key.replace("""*""" , lowerCamelCase__ )
if "weight_g" in name:
lowerCAmelCase__ = """weight_g"""
elif "weight_v" in name:
lowerCAmelCase__ = """weight_v"""
elif "bias" in name:
lowerCAmelCase__ = """bias"""
elif "weight" in name:
lowerCAmelCase__ = """weight"""
elif "running_mean" in name:
lowerCAmelCase__ = """running_mean"""
elif "running_var" in name:
lowerCAmelCase__ = """running_var"""
elif "num_batches_tracked" in name:
lowerCAmelCase__ = """num_batches_tracked"""
else:
lowerCAmelCase__ = None
set_recursively(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
continue
if not is_used:
unused_weights.append(lowerCamelCase__ )
logger.warning(f"""Unused weights: {unused_weights}""" )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = full_name.split("""conv_layers.""" )[-1]
lowerCAmelCase__ = name.split(""".""" )
lowerCAmelCase__ = int(items[0] )
lowerCAmelCase__ = 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.""" )
lowerCAmelCase__ = 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.""" )
lowerCAmelCase__ = 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.""" )
lowerCAmelCase__ = 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.""" )
lowerCAmelCase__ = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowerCamelCase__ )
@torch.no_grad()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , ):
"""simple docstring"""
if config_path is not None:
lowerCAmelCase__ = SpeechTaConfig.from_pretrained(lowerCamelCase__ )
else:
lowerCAmelCase__ = SpeechTaConfig()
if task == "s2t":
lowerCAmelCase__ = config.max_text_positions
lowerCAmelCase__ = SpeechTaForSpeechToText(lowerCamelCase__ )
elif task == "t2s":
lowerCAmelCase__ = 1876
lowerCAmelCase__ = 600
lowerCAmelCase__ = config.max_speech_positions
lowerCAmelCase__ = SpeechTaForTextToSpeech(lowerCamelCase__ )
elif task == "s2s":
lowerCAmelCase__ = 1876
lowerCAmelCase__ = config.max_speech_positions
lowerCAmelCase__ = SpeechTaForSpeechToSpeech(lowerCamelCase__ )
else:
raise ValueError(f"""Unknown task name: {task}""" )
if vocab_path:
lowerCAmelCase__ = SpeechTaTokenizer(lowerCamelCase__ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
lowerCAmelCase__ = AddedToken("""<mask>""" , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ )
lowerCAmelCase__ = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
lowerCAmelCase__ = SpeechTaFeatureExtractor()
lowerCAmelCase__ = SpeechTaProcessor(tokenizer=lowerCamelCase__ , feature_extractor=lowerCamelCase__ )
processor.save_pretrained(lowerCamelCase__ )
lowerCAmelCase__ = torch.load(lowerCamelCase__ )
recursively_load_weights(fairseq_checkpoint["""model"""] , lowerCamelCase__ , lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
if repo_id:
print("""Pushing to the hub...""" )
processor.push_to_hub(lowerCamelCase__ )
model.push_to_hub(lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"--task",
default="s2t",
type=str,
help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
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."
)
__lowerCAmelCase : Any = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 718 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ ).to_dict()
config_dict.pop("""image_processor_type""" )
lowerCAmelCase__ = CLIPImageProcessor(**snake_case__ )
# save in new folder
model_config.save_pretrained(snake_case__ )
config.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
# make sure private variable is not incorrectly saved
lowerCAmelCase__ = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with self.assertRaisesRegex(
snake_case__ , """clip-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""clip-base""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
with self.assertRaisesRegex(
snake_case__ , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , revision="""aaaaaa""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
with self.assertRaisesRegex(
snake_case__ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , trust_remote_code=snake_case__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case__ ):
AutoImageProcessor.register(snake_case__ , snake_case__ )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = CustomImageProcessor.from_pretrained(snake_case__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Tuple = True
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# If remote code is not set, the default is to use local
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(snake_case__ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
__lowerCAmelCase : List[str] = logging.getLogger(__name__)
__lowerCAmelCase : Optional[Any] = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Tuple = "bertabs"
def __init__( self : Union[str, Any] , snake_case__ : Dict=30522 , snake_case__ : List[str]=512 , snake_case__ : Any=6 , snake_case__ : Optional[Any]=512 , snake_case__ : Optional[Any]=8 , snake_case__ : Union[str, Any]=512 , snake_case__ : int=0.2 , snake_case__ : Any=6 , snake_case__ : str=768 , snake_case__ : Optional[Any]=8 , snake_case__ : Dict=2048 , snake_case__ : Union[str, Any]=0.2 , **snake_case__ : Optional[int] , ):
super().__init__(**snake_case__ )
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = max_pos
lowerCAmelCase__ = enc_layers
lowerCAmelCase__ = enc_hidden_size
lowerCAmelCase__ = enc_heads
lowerCAmelCase__ = enc_ff_size
lowerCAmelCase__ = enc_dropout
lowerCAmelCase__ = dec_layers
lowerCAmelCase__ = dec_hidden_size
lowerCAmelCase__ = dec_heads
lowerCAmelCase__ = dec_ff_size
lowerCAmelCase__ = dec_dropout
| 719 | """simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a_ :
def __init__( self : Optional[int] , snake_case__ : List[Any]=2 , snake_case__ : Any=3 , snake_case__ : Union[str, Any]=64 , snake_case__ : Any=None ):
lowerCAmelCase__ = np.random.default_rng(snake_case__ )
lowerCAmelCase__ = length
lowerCAmelCase__ = rng.normal(size=(length,) ).astype(np.floataa )
lowerCAmelCase__ = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self : Optional[Any] ):
return self.length
def __getitem__( self : List[str] , snake_case__ : Optional[int] ):
return {"x": self.x[i], "y": self.y[i]}
class a_ ( torch.nn.Module ):
def __init__( self : List[str] , snake_case__ : str=0 , snake_case__ : Dict=0 , snake_case__ : Any=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : int , snake_case__ : Any=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a[0] + self.b[0]
class a_ ( torch.nn.Module ):
def __init__( self : Any , snake_case__ : Union[str, Any]=0 , snake_case__ : Union[str, Any]=0 , snake_case__ : List[Any]=False ):
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(snake_case__ ).float() )
lowerCAmelCase__ = True
def _SCREAMING_SNAKE_CASE ( self : Tuple , snake_case__ : Optional[Any]=None ):
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a + self.b
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ = 16 ):
"""simple docstring"""
from datasets import load_dataset
from transformers import AutoTokenizer
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase__ = {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
lowerCAmelCase__ = load_dataset("""csv""" , data_files=lowerCamelCase__ )
lowerCAmelCase__ = datasets["""train"""].unique("""label""" )
lowerCAmelCase__ = {v: i for i, v in enumerate(lowerCamelCase__ )}
def tokenize_function(lowerCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase__ = tokenizer(
examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ , padding="""max_length""" )
if "label" in examples:
lowerCAmelCase__ = [label_to_id[l] for l in examples["""label"""]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase__ = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""sentence1""", """sentence2""", """label"""] , )
def collate_fn(lowerCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
lowerCAmelCase__ = DataLoader(tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=2 )
lowerCAmelCase__ = DataLoader(tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 674 | 0 |
"""simple docstring"""
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def _UpperCAmelCase ( *lowerCamelCase__ ):
"""simple docstring"""
with open(lowerCamelCase__ , """r""" ) as fh:
fcntl.flock(lowerCamelCase__ , fcntl.LOCK_EX )
try:
print(*lowerCamelCase__ )
finally:
fcntl.flock(lowerCamelCase__ , fcntl.LOCK_UN )
__lowerCAmelCase : Optional[Any] = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__lowerCAmelCase : Optional[Any] = torch.device("cuda", local_rank)
__lowerCAmelCase : List[str] = socket.gethostname()
__lowerCAmelCase : Dict = F"[{hostname}-{local_rank}]"
try:
# test distributed
dist.init_process_group("nccl")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
__lowerCAmelCase : str = dist.get_rank()
__lowerCAmelCase : List[Any] = dist.get_world_size()
printflock(F"{gpu} is OK (global rank: {rank}/{world_size})")
dist.barrier()
if rank == 0:
printflock(F"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")
except Exception:
printflock(F"{gpu} is broken")
raise
| 720 | """simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = MobileBertConfig.from_json_file(lowerCamelCase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
lowerCAmelCase__ = MobileBertForPreTraining(lowerCamelCase__ )
# Load weights from tf checkpoint
lowerCAmelCase__ = load_tf_weights_in_mobilebert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , lowerCamelCase__ )
if __name__ == "__main__":
__lowerCAmelCase : List[str] = 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(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT 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."
)
__lowerCAmelCase : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__lowerCAmelCase : Dict = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__lowerCAmelCase : Optional[Any] = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
__lowerCAmelCase : Dict = {
"distilbert-base-uncased": 5_12,
"distilbert-base-uncased-distilled-squad": 5_12,
"distilbert-base-cased": 5_12,
"distilbert-base-cased-distilled-squad": 5_12,
"distilbert-base-german-cased": 5_12,
"distilbert-base-multilingual-cased": 5_12,
}
__lowerCAmelCase : Union[str, Any] = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Union[str, Any] = VOCAB_FILES_NAMES
UpperCamelCase_ : str = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : List[Any] = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase_ : Tuple = ["input_ids", "attention_mask"]
UpperCamelCase_ : int = DistilBertTokenizer
def __init__( self : Optional[int] , snake_case__ : Dict=None , snake_case__ : int=None , snake_case__ : Tuple=True , snake_case__ : Union[str, Any]="[UNK]" , snake_case__ : Tuple="[SEP]" , snake_case__ : List[str]="[PAD]" , snake_case__ : int="[CLS]" , snake_case__ : Any="[MASK]" , snake_case__ : str=True , snake_case__ : str=None , **snake_case__ : Tuple , ):
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__ , )
lowerCAmelCase__ = 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
):
lowerCAmelCase__ = getattr(snake_case__ , normalizer_state.pop("""type""" ) )
lowerCAmelCase__ = do_lower_case
lowerCAmelCase__ = strip_accents
lowerCAmelCase__ = tokenize_chinese_chars
lowerCAmelCase__ = normalizer_class(**snake_case__ )
lowerCAmelCase__ = do_lower_case
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : Optional[Any] , snake_case__ : int=None ):
lowerCAmelCase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _SCREAMING_SNAKE_CASE ( self : Dict , snake_case__ : str , snake_case__ : Optional[str] = None ):
lowerCAmelCase__ = self._tokenizer.model.save(snake_case__ , name=snake_case__ )
return tuple(snake_case__ )
| 721 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
assert isinstance(lowerCamelCase__ , lowerCamelCase__ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
lowerCAmelCase__ = f"""The input value of [n={number}] has to be > 0"""
raise ValueError(lowerCamelCase__ )
else:
lowerCAmelCase__ = sylvester(number - 1 )
lowerCAmelCase__ = num - 1
lowerCAmelCase__ = num
return lower * upper + 1
if __name__ == "__main__":
print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")
| 674 | 0 |
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