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'''simple docstring'''
import requests
lowercase_ = "YOUR API KEY"
def lowerCAmelCase (__A , __A = giphy_api_key):
"""simple docstring"""
_a = '''+'''.join(query.split())
_a = F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
_a = requests.get(__A).json()['''data''']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("\n".join(get_gifs("space ship")))
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_choices
def a__ (self ) -> str:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxAlbertModelTester(self )
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''albert-base-v2''' )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Dict:
"""simple docstring"""
_a = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
_a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_a = model(A , attention_mask=A )[0]
_a = (1, 11, 768)
self.assertEqual(output.shape , A )
_a = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 11 | 1 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
lowercase_ = get_logger(__name__)
class __A :
'''simple docstring'''
def __init__(self , A = None ) -> int:
"""simple docstring"""
_a = (
os.path.join(A , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
_a = Extractor
def a__ (self , A ) -> str:
"""simple docstring"""
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
_a = os.path.abspath(A )
return os.path.join(self.extract_dir , hash_url_to_filename(A ) )
def a__ (self , A , A ) -> bool:
"""simple docstring"""
return force_extract or (
not os.path.isfile(A ) and not (os.path.isdir(A ) and os.listdir(A ))
)
def a__ (self , A , A = False ) -> str:
"""simple docstring"""
_a = self.extractor.infer_extractor_format(A )
if not extractor_format:
return input_path
_a = self._get_output_path(A )
if self._do_extract(A , A ):
self.extractor.extract(A , A , A )
return output_path
class __A ( A ):
'''simple docstring'''
@classmethod
@abstractmethod
def a__ (cls , A , **A ) -> bool:
"""simple docstring"""
...
@staticmethod
@abstractmethod
def a__ (A , A ) -> None:
"""simple docstring"""
...
class __A ( A , A ):
'''simple docstring'''
__lowerCamelCase : List[bytes] = []
@staticmethod
def a__ (A , A ) -> int:
"""simple docstring"""
with open(A , '''rb''' ) as f:
return f.read(A )
@classmethod
def a__ (cls , A , A = b"" ) -> bool:
"""simple docstring"""
if not magic_number:
_a = max(len(A ) for cls_magic_number in cls.magic_numbers )
try:
_a = cls.read_magic_number(A , A )
except OSError:
return False
return any(magic_number.startswith(A ) for cls_magic_number in cls.magic_numbers )
class __A ( A ):
'''simple docstring'''
@classmethod
def a__ (cls , A , **A ) -> bool:
"""simple docstring"""
return tarfile.is_tarfile(A )
@staticmethod
def a__ (A , A ) -> List[Any]:
"""simple docstring"""
def resolved(A ) -> str:
return os.path.realpath(os.path.abspath(A ) )
def badpath(A , A ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(A , A ) ).startswith(A )
def badlink(A , A ) -> bool:
# Links are interpreted relative to the directory containing the link
_a = resolved(os.path.join(A , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=A )
_a = resolved(A )
for finfo in members:
if badpath(finfo.name , A ):
logger.error(f'''Extraction of {finfo.name} is blocked (illegal path)''' )
elif finfo.issym() and badlink(A , A ):
logger.error(f'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}''' )
elif finfo.islnk() and badlink(A , A ):
logger.error(f'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}''' )
else:
yield finfo
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
os.makedirs(A , exist_ok=A )
_a = tarfile.open(A )
tar_file.extractall(A , members=TarExtractor.safemembers(A , A ) )
tar_file.close()
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : int = [b'\x1F\x8B']
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
with gzip.open(A , '''rb''' ) as gzip_file:
with open(A , '''wb''' ) as extracted_file:
shutil.copyfileobj(A , A )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = [
b'PK\x03\x04',
b'PK\x05\x06', # empty archive
b'PK\x07\x08', # spanned archive
]
@classmethod
def a__ (cls , A , A = b"" ) -> bool:
"""simple docstring"""
if super().is_extractable(A , magic_number=A ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(A , '''rb''' ) as fp:
_a = _EndRecData(A )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
_a = fp.read(A ) # CD is where we expect it to be
if len(A ) == sizeCentralDir:
_a = struct.unpack(A , A ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
os.makedirs(A , exist_ok=A )
with zipfile.ZipFile(A , '''r''' ) as zip_file:
zip_file.extractall(A )
zip_file.close()
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str = [b'\xFD\x37\x7A\x58\x5A\x00']
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
with lzma.open(A ) as compressed_file:
with open(A , '''wb''' ) as extracted_file:
shutil.copyfileobj(A , A )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any = [b'Rar!\x1a\x07\x00', b'Rar!\x1a\x07\x01\x00'] # RAR_ID # RAR5_ID
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
if not config.RARFILE_AVAILABLE:
raise ImportError('''Please pip install rarfile''' )
import rarfile
os.makedirs(A , exist_ok=A )
_a = rarfile.RarFile(A )
rf.extractall(A )
rf.close()
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any = [b'\x28\xb5\x2F\xFD']
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
if not config.ZSTANDARD_AVAILABLE:
raise ImportError('''Please pip install zstandard''' )
import zstandard as zstd
_a = zstd.ZstdDecompressor()
with open(A , '''rb''' ) as ifh, open(A , '''wb''' ) as ofh:
dctx.copy_stream(A , A )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : List[str] = [b'\x42\x5A\x68']
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
with bza.open(A , '''rb''' ) as compressed_file:
with open(A , '''wb''' ) as extracted_file:
shutil.copyfileobj(A , A )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Tuple = [b'\x37\x7A\xBC\xAF\x27\x1C']
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
if not config.PY7ZR_AVAILABLE:
raise ImportError('''Please pip install py7zr''' )
import pyazr
os.makedirs(A , exist_ok=A )
with pyazr.SevenZipFile(A , '''r''' ) as archive:
archive.extractall(A )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = [b'\x04\x22\x4D\x18']
@staticmethod
def a__ (A , A ) -> None:
"""simple docstring"""
if not config.LZ4_AVAILABLE:
raise ImportError('''Please pip install lz4''' )
import lza.frame
with lza.frame.open(A , '''rb''' ) as compressed_file:
with open(A , '''wb''' ) as extracted_file:
shutil.copyfileobj(A , A )
class __A :
'''simple docstring'''
__lowerCamelCase : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def a__ (cls ) -> Tuple:
"""simple docstring"""
return max(
len(A )
for extractor in cls.extractors.values()
if issubclass(A , A )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def a__ (A , A ) -> Tuple:
"""simple docstring"""
try:
return MagicNumberBaseExtractor.read_magic_number(A , magic_number_length=A )
except OSError:
return b""
@classmethod
def a__ (cls , A , A = False ) -> bool:
"""simple docstring"""
warnings.warn(
'''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'infer_extractor_format\' instead.''' , category=A , )
_a = cls.infer_extractor_format(A )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def a__ (cls , A ) -> str: # <Added version="2.4.0"/>
"""simple docstring"""
_a = cls._get_magic_number_max_length()
_a = cls._read_magic_number(A , A )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(A , magic_number=A ):
return extractor_format
@classmethod
def a__ (cls , A , A , A = None , A = "deprecated" , ) -> None:
"""simple docstring"""
os.makedirs(os.path.dirname(A ) , exist_ok=A )
# Prevent parallel extractions
_a = str(Path(A ).with_suffix('''.lock''' ) )
with FileLock(A ):
shutil.rmtree(A , ignore_errors=A )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(A , A ): # passed as positional arg
warnings.warn(
'''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'extractor_format\' instead.''' , category=A , )
_a = extractor if extractor != '''deprecated''' else extractor_format
else:
_a = cls.extractors[extractor_format]
return extractor.extract(A , A )
else:
warnings.warn(
'''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
'''exception in 3.0.0.''' , category=A , )
for extractor in cls.extractors.values():
if extractor.is_extractable(A ):
return extractor.extract(A , A )
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a = 0
_a = len(__A) - 2
for i in range(__A , -1 , -2):
# double the value of every second digit
_a = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_a = cc_number[:i] + str(__A) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCAmelCase (__A):
"""simple docstring"""
_a = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''')
return False
if not 13 <= len(__A) <= 16:
print(F'''{error_message} of its length.''')
return False
if not validate_initial_digits(__A):
print(F'''{error_message} of its first two digits.''')
return False
if not luhn_validation(__A):
print(F'''{error_message} it fails the Luhn check.''')
return False
print(F'''{credit_card_number} is a valid credit card number.''')
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 11 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = 'perceiver'
def __init__(self , A=256 , A=1_280 , A=768 , A=1 , A=26 , A=8 , A=8 , A=None , A=None , A="kv" , A=1 , A=1 , A="gelu" , A=0.1 , A=0.02 , A=1E-12 , A=True , A=262 , A=2_048 , A=56 , A=[368, 496] , A=16 , A=1_920 , A=16 , A=[1, 16, 224, 224] , **A , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**A )
_a = num_latents
_a = d_latents
_a = d_model
_a = num_blocks
_a = num_self_attends_per_block
_a = num_self_attention_heads
_a = num_cross_attention_heads
_a = qk_channels
_a = v_channels
_a = cross_attention_shape_for_attention
_a = self_attention_widening_factor
_a = cross_attention_widening_factor
_a = hidden_act
_a = attention_probs_dropout_prob
_a = initializer_range
_a = layer_norm_eps
_a = use_query_residual
# masked language modeling attributes
_a = vocab_size
_a = max_position_embeddings
# image classification attributes
_a = image_size
# flow attributes
_a = train_size
# multimodal autoencoding attributes
_a = num_frames
_a = audio_samples_per_frame
_a = samples_per_patch
_a = output_shape
class __A ( A ):
'''simple docstring'''
@property
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
_a = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_a = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''inputs''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
@property
def a__ (self ) -> float:
"""simple docstring"""
return 1E-4
def a__ (self , A , A = -1 , A = -1 , A = -1 , A = False , A = None , A = 3 , A = 40 , A = 40 , ) -> Mapping[str, Any]:
"""simple docstring"""
if isinstance(A , A ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_a = compute_effective_axis_dimension(
A , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_a = preprocessor.num_special_tokens_to_add(A )
_a = compute_effective_axis_dimension(
A , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=A )
# Generate dummy inputs according to compute batch and sequence
_a = [''' '''.join(['''a'''] ) * seq_length] * batch_size
_a = dict(preprocessor(A , return_tensors=A ) )
_a = inputs.pop('''input_ids''' )
return inputs
elif isinstance(A , A ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_a = compute_effective_axis_dimension(A , fixed_dimension=OnnxConfig.default_fixed_batch )
_a = self._generate_dummy_images(A , A , A , A )
_a = dict(preprocessor(images=A , return_tensors=A ) )
_a = inputs.pop('''pixel_values''' )
return inputs
else:
raise ValueError(
'''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''' )
| 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) == 0:
return False
_a = len(__A) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __A)
else:
return binary_search(a_list[midpoint + 1 :] , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
lowercase_ = int(input("Enter the number to be found in the list:\n").strip())
lowercase_ = "" if binary_search(sequence, target) else "not "
print(F"""{target} was {not_str}found in {sequence}""")
| 11 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase (__A = "laptop"):
"""simple docstring"""
_a = F'''https://www.amazon.in/laptop/s?k={product}'''
_a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
_a = BeautifulSoup(requests.get(__A , headers=__A).text)
# Initialize a Pandas dataframe with the column titles
_a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
_a = item.ha.text
_a = '''https://www.amazon.in/''' + item.ha.a['''href''']
_a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
_a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
_a = '''Not available'''
try:
_a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
_a = ''''''
try:
_a = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
_a = float('''nan''')
except AttributeError:
pass
_a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_a = ''' '''
_a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 11 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCAmelCase (__A , __A = True , __A = math.inf , __A = -math.inf , __A = math.inf , __A = -math.inf , __A = False , __A = 100 , __A = 0.01 , __A = 1 , ):
"""simple docstring"""
_a = False
_a = search_prob
_a = start_temperate
_a = []
_a = 0
_a = None
while not search_end:
_a = current_state.score()
if best_state is None or current_score > best_state.score():
_a = current_state
scores.append(__A)
iterations += 1
_a = None
_a = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
_a = random.randint(0 , len(__A) - 1) # picking a random neighbor
_a = neighbors.pop(__A)
_a = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
_a = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
_a = picked_neighbor
else:
_a = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
_a = picked_neighbor
_a = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
_a = True
else:
_a = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(__A) , __A)
plt.xlabel('''Iterations''')
plt.ylabel('''Function values''')
plt.show()
return best_state
if __name__ == "__main__":
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
lowercase_ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
lowercase_ = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
lowercase_ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
lowercase_ = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return (3 * x**2) - (6 * y)
lowercase_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowercase_ = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
F"""{local_min.score()}"""
)
lowercase_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowercase_ = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
F"""{local_min.score()}"""
)
| 11 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if isinstance(__A , torch.Tensor):
return image
elif isinstance(__A , PIL.Image.Image):
_a = [image]
if isinstance(image[0] , PIL.Image.Image):
_a = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
_a = np.concatenate(__A , axis=0)
_a = np.array(__A).astype(np.floataa) / 2_55.0
_a = image.transpose(0 , 3 , 1 , 2)
_a = 2.0 * image - 1.0
_a = torch.from_numpy(__A)
elif isinstance(image[0] , torch.Tensor):
_a = torch.cat(__A , dim=0)
return image
def lowerCAmelCase (__A , __A , __A , __A=0.99_95):
"""simple docstring"""
if not isinstance(__A , np.ndarray):
_a = True
_a = va.device
_a = va.cpu().numpy()
_a = va.cpu().numpy()
_a = np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
if np.abs(__A) > DOT_THRESHOLD:
_a = (1 - t) * va + t * va
else:
_a = np.arccos(__A)
_a = np.sin(__A)
_a = theta_a * t
_a = np.sin(__A)
_a = np.sin(theta_a - theta_t) / sin_theta_a
_a = sin_theta_t / sin_theta_a
_a = sa * va + sa * va
if inputs_are_torch:
_a = torch.from_numpy(__A).to(__A)
return va
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = F.normalize(__A , dim=-1)
_a = F.normalize(__A , dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for param in model.parameters():
_a = value
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
_a = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size['''shortest_edge''']
)
_a = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def a__ (self , A = "auto" ) -> Union[str, Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(A )
def a__ (self ) -> int:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self ) -> str:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = min(int(num_inference_steps * strength ) , A )
_a = max(num_inference_steps - init_timestep , 0 )
_a = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a__ (self , A , A , A , A , A , A=None ) -> List[str]:
"""simple docstring"""
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
_a = image.to(device=A , dtype=A )
if isinstance(A , A ):
_a = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
_a = torch.cat(A , dim=0 )
else:
_a = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 0.18215 * init_latents
_a = init_latents.repeat_interleave(A , dim=0 )
_a = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
_a = self.scheduler.add_noise(A , A , A )
_a = init_latents
return latents
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_a = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_a = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.feature_extractor.preprocess(A )
_a = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a__ (self , A , A , A , A , A , A , A , ) -> Union[str, Any]:
"""simple docstring"""
_a = latents.detach().requires_grad_()
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_a = self.scheduler.alphas_cumprod[timestep]
_a = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_a = torch.sqrt(A )
_a = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
_a = self.scheduler.sigmas[index]
_a = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * sample
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = transforms.Resize(self.feature_extractor_size )(A )
_a = self.normalize(A ).to(latents.dtype )
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
_a = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
_a = latents.detach() + grads * (sigma**2)
_a = noise_pred_original
else:
_a = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> str:
"""simple docstring"""
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
_a = [generator] + [None] * (batch_size - 1)
_a = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_a = [x[0] for x in coca_is_none if x[1]]
_a = ''', '''.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
# get prompt text embeddings for content and style
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_a = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
_a = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
_a = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_a = {}
if accepts_offset:
_a = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_a , _a = self.get_timesteps(A , A , self.device )
_a = timesteps[:1].repeat(A )
# Preprocess image
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = slerp(A , A , A )
if clip_guidance_scale > 0:
_a = self.get_clip_image_embeddings(A , A )
_a = self.get_clip_image_embeddings(A , A )
_a = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_a = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_a = content_text_input.input_ids.shape[-1]
_a = self.tokenizer([''''''] , padding='''max_length''' , max_length=A , return_tensors='''pt''' )
_a = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_a = uncond_embeddings.repeat_interleave(A , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_a = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_a = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_a = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_a = torch.randn(A , generator=A , device='''cpu''' , dtype=A ).to(
self.device )
else:
_a = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
# check if the scheduler accepts generator
_a = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_a = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_a = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_a , _a = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * latents
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 11 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase_ = "\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n >>> repo = \"openai/shap-e-img2img\"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n >>> image = load_image(image_url).convert(\"RGB\")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n ```\n"
@dataclass
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Union[PIL.Image.Image, np.ndarray]
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , ) -> Any:
"""simple docstring"""
super().__init__()
self.register_modules(
prior=A , image_encoder=A , image_processor=A , scheduler=A , renderer=A , )
def a__ (self , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
if latents is None:
_a = randn_tensor(A , generator=A , device=A , dtype=A )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
_a = latents.to(A )
_a = latents * scheduler.init_noise_sigma
return latents
def a__ (self , A=0 ) -> List[str]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
_a = torch.device(f'''cuda:{gpu_id}''' )
_a = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(A , A )
@property
def a__ (self ) -> str:
"""simple docstring"""
if self.device != torch.device('''meta''' ) or not hasattr(self.image_encoder , '''_hf_hook''' ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(A , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def a__ (self , A , A , A , A , ) -> Tuple:
"""simple docstring"""
if isinstance(A , A ) and isinstance(image[0] , torch.Tensor ):
_a = torch.cat(A , axis=0 ) if image[0].ndim == 4 else torch.stack(A , axis=0 )
if not isinstance(A , torch.Tensor ):
_a = self.image_processor(A , return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )
_a = image.to(dtype=self.image_encoder.dtype , device=A )
_a = self.image_encoder(A )['''last_hidden_state''']
_a = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
_a = image_embeds.repeat_interleave(A , dim=0 )
if do_classifier_free_guidance:
_a = torch.zeros_like(A )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_a = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(A )
def __call__(self , A , A = 1 , A = 25 , A = None , A = None , A = 4.0 , A = 64 , A = "pil" , A = True , ) -> List[Any]:
"""simple docstring"""
if isinstance(A , PIL.Image.Image ):
_a = 1
elif isinstance(A , torch.Tensor ):
_a = image.shape[0]
elif isinstance(A , A ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
_a = len(A )
else:
raise ValueError(
f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(A )}''' )
_a = self._execution_device
_a = batch_size * num_images_per_prompt
_a = guidance_scale > 1.0
_a = self._encode_image(A , A , A , A )
# prior
self.scheduler.set_timesteps(A , device=A )
_a = self.scheduler.timesteps
_a = self.prior.config.num_embeddings
_a = self.prior.config.embedding_dim
_a = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , A , A , A , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
_a = latents.reshape(latents.shape[0] , A , A )
for i, t in enumerate(self.progress_bar(A ) ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
_a = self.prior(
A , timestep=A , proj_embedding=A , ).predicted_image_embedding
# remove the variance
_a , _a = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
_a = self.scheduler.step(
A , timestep=A , sample=A , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=A )
_a = []
for i, latent in enumerate(A ):
print()
_a = self.renderer.decode(
latent[None, :] , A , size=A , ray_batch_size=4_096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(A )
_a = torch.stack(A )
if output_type not in ["np", "pil"]:
raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )
_a = images.cpu().numpy()
if output_type == "pil":
_a = [self.numpy_to_pil(A ) for image in images]
# Offload last model to CPU
if hasattr(self , '''final_offload_hook''' ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=A )
| 11 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = CTRLTokenizer
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Any = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = 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(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = '''adapt react readapt apt'''
_a = '''adapt react readapt apt'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt react readapt apt'''
_a = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return [ord(__A) - 96 for elem in plain]
def lowerCAmelCase (__A):
"""simple docstring"""
return "".join(chr(elem + 96) for elem in encoded)
def lowerCAmelCase ():
"""simple docstring"""
_a = encode(input('''-> ''').strip().lower())
print('''Encoded: ''' , __A)
print('''Decoded:''' , decode(__A))
if __name__ == "__main__":
main()
| 11 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) < k or k < 0:
raise ValueError('''Invalid Input''')
_a = _a = sum(array[:k])
for i in range(len(__A) - k):
_a = current_sum - array[i] + array[i + k]
_a = max(__A , __A)
return max_sum
if __name__ == "__main__":
from doctest import testmod
from random import randint
testmod()
lowercase_ = [randint(-1_000, 1_000) for i in range(100)]
lowercase_ = randint(0, 110)
print(F"""The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}""")
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
def __init__(self , *A , **A ) -> None:
"""simple docstring"""
warnings.warn(
'''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use CLIPImageProcessor instead.''' , A , )
super().__init__(*A , **A )
| 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowercase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_ = 10
lowercase_ = 256
def lowerCAmelCase (__A):
"""simple docstring"""
if len(__A) < MIN_NUM_TOKENS:
return None
_a = MinHash(num_perm=__A)
for token in set(__A):
min_hash.update(token.encode())
return min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}
class __A :
'''simple docstring'''
def __init__(self , *,
A = 0.85 , ) -> Optional[int]:
"""simple docstring"""
_a = duplication_jaccard_threshold
_a = NUM_PERM
_a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_a = defaultdict(A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
_a = self._index.query(A )
if code_key in self._index.keys:
print(f'''Duplicate key {code_key}''' )
return
self._index.insert(A , A )
if len(A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A )
def a__ (self ) -> List[List[Dict]]:
"""simple docstring"""
_a = []
for base, duplicates in self._duplicate_clusters.items():
_a = [base] + list(A )
# reformat the cluster to be a list of dict
_a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(A )
return duplicate_clusters
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_duplicate_clusters()
with open(A , '''w''' ) as f:
json.dump(A , A )
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = element
_a = get_min_hash([t for t in NON_ALPHA.split(data['''content''']) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__A , max_queue_size=10_000) , chunksize=100 , ):
if data is not None:
yield data
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = DuplicationIndex(duplication_jaccard_threshold=__A)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A)) , max_queue_size=100)):
di.add(__A , __A)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = get_tokens(__A)
_a = get_tokens(__A)
return len(tokensa & tokensa) / len(tokensa | tokensa)
lowercase_ = None
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for elementa in cluster:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__A , __A) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_a = 1
extremes.append(__A)
return extremes
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
global _shared_dataset
_a = dataset
_a = []
_a = partial(_find_cluster_extremes_shared , jaccard_threshold=__A)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__A , __A , ) , total=len(__A) , ):
extremes_list.append(__A)
return extremes_list
def lowerCAmelCase (__A , __A = 0.85):
"""simple docstring"""
_a = make_duplicate_clusters(__A , __A)
_a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_a = {}
_a = find_extremes(__A , __A , __A)
for extremes in extremes_clusters:
for element in extremes:
_a = element
_a = duplicate_indices - set(extreme_dict.keys())
_a = dataset.filter(lambda __A , __A: idx not in remove_indices , with_indices=__A)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_a = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(__A)}''')
print(F'''Number of duplicate clusters: {len(__A)}''')
print(F'''Files in duplicate cluster: {len(__A)}''')
print(F'''Unique files in duplicate cluster: {len(__A)}''')
print(F'''Filtered dataset size: {len(__A)}''')
return ds_filter, duplicate_clusters
| 11 | 1 |
'''simple docstring'''
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCAmelCase (__A):
"""simple docstring"""
_a = {}
_a = tokenizer(example['''content'''] , truncation=__A)['''input_ids''']
_a = len(example['''content''']) / len(output['''input_ids'''])
return output
lowercase_ = HfArgumentParser(PretokenizationArguments)
lowercase_ = parser.parse_args()
if args.num_workers is None:
lowercase_ = multiprocessing.cpu_count()
lowercase_ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
lowercase_ = time.time()
lowercase_ = load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")
lowercase_ = time.time()
lowercase_ = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
"repo_name",
"path",
"copies",
"size",
"content",
"license",
"hash",
"line_mean",
"line_max",
"alpha_frac",
"autogenerated",
],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")
lowercase_ = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")
| 11 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
assert column_title.isupper()
_a = 0
_a = len(__A) - 1
_a = 0
while index >= 0:
_a = (ord(column_title[index]) - 64) * pow(26 , __A)
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 11 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = IFInpaintingSuperResolutionPipeline
__lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
__lowerCamelCase : str = PipelineTesterMixin.required_optional_params - {'latents'}
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def a__ (self , A , A=0 ) -> List[Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = floats_tensor((1, 3, 16, 16) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ (self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def a__ (self ) -> str:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._test_save_load_local()
def a__ (self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 11 | 1 |
'''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 ( A ):
'''simple docstring'''
__lowerCamelCase : torch.FloatTensor
__lowerCamelCase : Optional[torch.FloatTensor] = None
def lowerCAmelCase (__A , __A=0.9_99 , __A="cosine" , ):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(__A):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__A):
return math.exp(t * -12.0)
else:
raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''')
_a = []
for i in range(__A):
_a = i / num_diffusion_timesteps
_a = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__A) / alpha_bar_fn(__A) , __A))
return torch.tensor(__A , dtype=torch.floataa)
class __A ( A , A ):
'''simple docstring'''
@register_to_config
def __init__(self , A = 1_000 , A = "fixed_small_log" , A = True , A = 1.0 , A = "epsilon" , A = "squaredcos_cap_v2" , ) -> Optional[int]:
"""simple docstring"""
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
_a = betas_for_alpha_bar(A )
_a = 1.0 - self.betas
_a = torch.cumprod(self.alphas , dim=0 )
_a = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
_a = 1.0
# setable values
_a = None
_a = torch.from_numpy(np.arange(0 , A )[::-1].copy() )
_a = variance_type
def a__ (self , A , A = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def a__ (self , A , A = None ) -> int:
"""simple docstring"""
_a = num_inference_steps
_a = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
_a = (np.arange(0 , A ) * step_ratio).round()[::-1].copy().astype(np.intaa )
_a = torch.from_numpy(A ).to(A )
def a__ (self , A , A=None , A=None , A=None ) -> Optional[Any]:
"""simple docstring"""
if prev_timestep is None:
_a = t - 1
_a = self.alphas_cumprod[t]
_a = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
_a = 1 - alpha_prod_t
_a = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
_a = self.betas[t]
else:
_a = 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
_a = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
_a = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
_a = torch.log(torch.clamp(A , min=1E-20 ) )
_a = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
_a = variance.log()
_a = beta.log()
_a = (predicted_variance + 1) / 2
_a = frac * max_log + (1 - frac) * min_log
return variance
def a__ (self , A , A , A , A = None , A=None , A = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
"""simple docstring"""
_a = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
_a , _a = torch.split(A , sample.shape[1] , dim=1 )
else:
_a = None
# 1. compute alphas, betas
if prev_timestep is None:
_a = t - 1
_a = self.alphas_cumprod[t]
_a = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
_a = 1 - alpha_prod_t
_a = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
_a = self.betas[t]
_a = self.alphas[t]
else:
_a = 1 - alpha_prod_t / alpha_prod_t_prev
_a = 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":
_a = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
_a = 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:
_a = torch.clamp(
A , -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
_a = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
_a = 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
_a = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
_a = 0
if t > 0:
_a = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=A , device=model_output.device )
_a = self._get_variance(
A , predicted_variance=A , prev_timestep=A , )
if self.variance_type == "fixed_small_log":
_a = variance
elif self.variance_type == "learned_range":
_a = (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.''' )
_a = variance * variance_noise
_a = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=A , pred_original_sample=A )
def a__ (self , A , A , A , ) -> torch.FloatTensor:
"""simple docstring"""
_a = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
_a = timesteps.to(original_samples.device )
_a = alphas_cumprod[timesteps] ** 0.5
_a = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
_a = sqrt_alpha_prod.unsqueeze(-1 )
_a = (1 - alphas_cumprod[timesteps]) ** 0.5
_a = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
_a = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
_a = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 11 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
"tokenization_m2m_100": ["M2M100Tokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
"M2M100ForConditionalGeneration",
"M2M100Model",
"M2M100PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return len(set(__A)) == len(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return math.sqrt(sum(pow(a - b , 2) for a, b in zip(__A , __A)))
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if dataset.ndim != value_array.ndim:
_a = (
'''Wrong input data\'s dimensions... '''
F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
)
raise ValueError(__A)
try:
if dataset.shape[1] != value_array.shape[1]:
_a = (
'''Wrong input data\'s shape... '''
F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
)
raise ValueError(__A)
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('''Wrong shape''')
if dataset.dtype != value_array.dtype:
_a = (
'''Input data have different datatype... '''
F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
)
raise TypeError(__A)
_a = []
for value in value_array:
_a = euclidean(__A , dataset[0])
_a = dataset[0].tolist()
for dataset_value in dataset[1:]:
_a = euclidean(__A , __A)
if dist > temp_dist:
_a = temp_dist
_a = dataset_value.tolist()
answer.append([vector, dist])
return answer
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return np.dot(__A , __A) / (norm(__A) * norm(__A))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) == 0:
return False
_a = len(__A) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __A)
else:
return binary_search(a_list[midpoint + 1 :] , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
lowercase_ = int(input("Enter the number to be found in the list:\n").strip())
lowercase_ = "" if binary_search(sequence, target) else "not "
print(F"""{target} was {not_str}found in {sequence}""")
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1))
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 11 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A ):
_a = self.prefix_sum[i - 1] + array[i]
def a__ (self , A , A ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __A ( A ):
'''simple docstring'''
def __init__(self , A=0.01 , A=1_000 ) -> int:
"""simple docstring"""
_a = p_stop
_a = max_length
def __iter__(self ) -> str:
"""simple docstring"""
_a = 0
_a = False
while not stop and count < self.max_length:
yield count
count += 1
_a = random.random() < self.p_stop
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self , A , A , A=False , A=True ) -> List[Any]:
"""simple docstring"""
_a = [
BatchSamplerShard(A , 2 , A , split_batches=A , even_batches=A )
for i in range(2 )
]
_a = [list(A ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(A ) for shard in batch_sampler_shards] , [len(A ) for e in expected] )
self.assertListEqual(A , A )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A )
_a = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
_a = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(A , A )
_a = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
_a = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(A , A )
_a = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
_a = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(A , A )
_a = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A )
# Check the shards when the dataset is very small.
_a = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
_a = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(A , A )
_a = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
_a = [[], []]
self.check_batch_sampler_shards(A , A )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
_a = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
_a = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
_a = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
_a = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
_a = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A )
# Check the shards when the dataset is very small.
_a = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
_a = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(A , A , split_batches=A )
_a = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
_a = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
_a = BatchSampler(range(24 ) , batch_size=3 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
_a = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
_a = BatchSampler(range(21 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
_a = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
_a = BatchSampler(range(22 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
_a = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
_a = BatchSampler(range(20 ) , batch_size=3 , drop_last=A )
_a = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(A , A , even_batches=A )
# Check the shards when the dataset is very small.
_a = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
_a = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
_a = BatchSampler(range(2 ) , batch_size=3 , drop_last=A )
_a = [[], []]
self.check_batch_sampler_shards(A , A , even_batches=A )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
_a = BatchSampler(range(24 ) , batch_size=4 , drop_last=A )
# Expected shouldn't change
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size.
_a = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
_a = BatchSampler(range(22 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
_a = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
_a = BatchSampler(range(21 ) , batch_size=4 , drop_last=A )
_a = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
# Check the shards when the dataset is very small.
_a = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
_a = [[[0, 1]], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
_a = BatchSampler(range(2 ) , batch_size=4 , drop_last=A )
_a = [[], []]
self.check_batch_sampler_shards(A , A , split_batches=A , even_batches=A )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
_a = [BatchSamplerShard(A , 2 , A , even_batches=A ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def a__ (self , A , A , A , A=False , A=2 , A=False ) -> Optional[Any]:
"""simple docstring"""
random.seed(A )
_a = list(A )
_a = [
IterableDatasetShard(
A , batch_size=A , drop_last=A , num_processes=A , process_index=A , split_batches=A , )
for i in range(A )
]
_a = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(A )
iterable_dataset_lists.append(list(A ) )
_a = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
_a = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(A ) , len(A ) )
self.assertTrue(len(A ) % shard_batch_size == 0 )
_a = []
for idx in range(0 , len(A ) , A ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(A ) < len(A ):
reference += reference
self.assertListEqual(A , reference[: len(A )] )
def a__ (self ) -> str:
"""simple docstring"""
_a = 42
_a = RandomIterableDataset()
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
# Edge case with a very small dataset
_a = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
self.check_iterable_dataset_shards(A , A , batch_size=4 , drop_last=A , split_batches=A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = BatchSampler(range(16 ) , batch_size=4 , drop_last=A )
_a = SkipBatchSampler(A , 2 )
self.assertListEqual(list(A ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def a__ (self ) -> str:
"""simple docstring"""
_a = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = DataLoader(list(range(16 ) ) , batch_size=4 )
_a = skip_first_batches(A , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
Accelerator()
_a = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(A ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A)
if n > 1:
factors.append(__A)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : List[Any] = 'decision_transformer'
__lowerCamelCase : Dict = ['past_key_values']
__lowerCamelCase : str = {
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , A=17 , A=4 , A=128 , A=4_096 , A=True , A=1 , A=1_024 , A=3 , A=1 , A=None , A="relu" , A=0.1 , A=0.1 , A=0.1 , A=1E-5 , A=0.02 , A=True , A=True , A=50_256 , A=50_256 , A=False , A=False , **A , ) -> Union[str, Any]:
"""simple docstring"""
_a = state_dim
_a = act_dim
_a = hidden_size
_a = max_ep_len
_a = action_tanh
_a = vocab_size
_a = n_positions
_a = n_layer
_a = n_head
_a = n_inner
_a = activation_function
_a = resid_pdrop
_a = embd_pdrop
_a = attn_pdrop
_a = layer_norm_epsilon
_a = initializer_range
_a = scale_attn_weights
_a = use_cache
_a = scale_attn_by_inverse_layer_idx
_a = reorder_and_upcast_attn
_a = bos_token_id
_a = eos_token_id
super().__init__(bos_token_id=A , eos_token_id=A , **A )
| 11 |
'''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
lowercase_ = {
# 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 :
'''simple docstring'''
def __init__(self , A = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError('''Unsupported Group''' )
_a = primes[group]['''prime''']
_a = primes[group]['''generator''']
_a = int(hexlify(urandom(32 ) ) , base=16 )
def a__ (self ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def a__ (self ) -> str:
"""simple docstring"""
_a = pow(self.generator , self.__private_key , self.prime )
return hex(A )[2:]
def a__ (self , A ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(A , (self.prime - 1) // 2 , self.prime ) == 1
)
def a__ (self , A ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
if not self.is_valid_public_key(A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , self.__private_key , self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def a__ (A , A ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(A , (prime - 1) // 2 , A ) == 1
)
@staticmethod
def a__ (A , A , A = 14 ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
_a = int(A , base=16 )
_a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(A , A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , A , A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
lowercase_ = threading.Lock()
lowercase_ = None
lowercase_ = {
"debug": logging.DEBUG,
"info": logging.INFO,
"warning": logging.WARNING,
"error": logging.ERROR,
"critical": logging.CRITICAL,
}
lowercase_ = logging.WARNING
lowercase_ = True
def lowerCAmelCase ():
"""simple docstring"""
_a = os.getenv('''TRANSFORMERS_VERBOSITY''' , __A)
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F'''Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '''
F'''has to be one of: { ', '.join(log_levels.keys()) }''')
return _default_log_level
def lowerCAmelCase ():
"""simple docstring"""
return __name__.split('''.''')[0]
def lowerCAmelCase ():
"""simple docstring"""
return logging.getLogger(_get_library_name())
def lowerCAmelCase ():
"""simple docstring"""
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
_a = logging.StreamHandler() # Set sys.stderr as stream.
_a = sys.stderr.flush
# Apply our default configuration to the library root logger.
_a = _get_library_root_logger()
library_root_logger.addHandler(_default_handler)
library_root_logger.setLevel(_get_default_logging_level())
_a = False
def lowerCAmelCase ():
"""simple docstring"""
global _default_handler
with _lock:
if not _default_handler:
return
_a = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler)
library_root_logger.setLevel(logging.NOTSET)
_a = None
def lowerCAmelCase ():
"""simple docstring"""
return log_levels
def lowerCAmelCase (__A = None):
"""simple docstring"""
if name is None:
_a = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(__A)
def lowerCAmelCase ():
"""simple docstring"""
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def lowerCAmelCase (__A):
"""simple docstring"""
_configure_library_root_logger()
_get_library_root_logger().setLevel(__A)
def lowerCAmelCase ():
"""simple docstring"""
return set_verbosity(__A)
def lowerCAmelCase ():
"""simple docstring"""
return set_verbosity(__A)
def lowerCAmelCase ():
"""simple docstring"""
return set_verbosity(__A)
def lowerCAmelCase ():
"""simple docstring"""
return set_verbosity(__A)
def lowerCAmelCase ():
"""simple docstring"""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler)
def lowerCAmelCase ():
"""simple docstring"""
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler)
def lowerCAmelCase (__A):
"""simple docstring"""
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(__A)
def lowerCAmelCase (__A):
"""simple docstring"""
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(__A)
def lowerCAmelCase ():
"""simple docstring"""
_configure_library_root_logger()
_a = False
def lowerCAmelCase ():
"""simple docstring"""
_configure_library_root_logger()
_a = True
def lowerCAmelCase ():
"""simple docstring"""
_a = _get_library_root_logger().handlers
for handler in handlers:
_a = logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s''')
handler.setFormatter(__A)
def lowerCAmelCase ():
"""simple docstring"""
_a = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(__A)
def lowerCAmelCase (self , *__A , **__A):
"""simple docstring"""
_a = os.getenv('''TRANSFORMERS_NO_ADVISORY_WARNINGS''' , __A)
if no_advisory_warnings:
return
self.warning(*__A , **__A)
lowercase_ = warning_advice
@functools.lru_cache(__A)
def lowerCAmelCase (self , *__A , **__A):
"""simple docstring"""
self.warning(*__A , **__A)
lowercase_ = warning_once
class __A :
'''simple docstring'''
def __init__(self , *A , **A ) -> str: # pylint: disable=unused-argument
"""simple docstring"""
_a = args[0] if args else None
def __iter__(self ) -> Optional[int]:
"""simple docstring"""
return iter(self._iterator )
def __getattr__(self , A ) -> Optional[int]:
"""simple docstring"""
def empty_fn(*A , **A ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__(self ) -> List[Any]:
"""simple docstring"""
return self
def __exit__(self , A , A , A ) -> Union[str, Any]:
"""simple docstring"""
return
class __A :
'''simple docstring'''
def __call__(self , *A , **A ) -> Optional[Any]:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm(*A , **A )
else:
return EmptyTqdm(*A , **A )
def a__ (self , *A , **A ) -> Optional[int]:
"""simple docstring"""
_a = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*A , **A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
lowercase_ = _tqdm_cls()
def lowerCAmelCase ():
"""simple docstring"""
global _tqdm_active
return bool(_tqdm_active)
def lowerCAmelCase ():
"""simple docstring"""
global _tqdm_active
_a = True
hf_hub_utils.enable_progress_bars()
def lowerCAmelCase ():
"""simple docstring"""
global _tqdm_active
_a = False
hf_hub_utils.disable_progress_bars()
| 11 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ = logging.getLogger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if os.path.exists(__A):
if os.path.exists(os.path.join(__A , '''config.json''')) and os.path.isfile(
os.path.join(__A , '''config.json''')):
os.remove(os.path.join(__A , '''config.json'''))
if os.path.exists(os.path.join(__A , '''pytorch_model.bin''')) and os.path.isfile(
os.path.join(__A , '''pytorch_model.bin''')):
os.remove(os.path.join(__A , '''pytorch_model.bin'''))
else:
os.makedirs(__A)
model.save_pretrained(__A)
def lowerCAmelCase (__A , __A=False):
"""simple docstring"""
_a = 2
if unlogit:
_a = torch.pow(__A , __A)
_a = p * torch.log(__A)
_a = 0
return -plogp.sum(dim=-1)
def lowerCAmelCase (__A):
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
for row in range(len(__A)):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
else:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))
def lowerCAmelCase (__A , __A , __A , __A=True , __A=True , __A=None , __A=False):
"""simple docstring"""
_a , _a = model.config.num_hidden_layers, model.config.num_attention_heads
_a = torch.zeros(__A , __A).to(args.device)
_a = torch.zeros(__A , __A).to(args.device)
if head_mask is None:
_a = torch.ones(__A , __A).to(args.device)
head_mask.requires_grad_(requires_grad=__A)
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_a = None
_a = 0.0
_a = 0.0
for step, inputs in enumerate(tqdm(__A , desc='''Iteration''' , disable=args.local_rank not in [-1, 0])):
_a = tuple(t.to(args.device) for t in inputs)
((_a) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_a = model(__A , labels=__A , head_mask=__A)
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_a , _a , _a = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__A):
_a = entropy(attn.detach() , __A)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__A).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_a = 2
_a = torch.pow(torch.pow(__A , __A).sum(-1) , 1 / exponent)
head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
if not args.dont_normalize_global_importance:
_a = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''')
print_ad_tensor(__A)
if compute_importance:
logger.info('''Head importance scores''')
print_ad_tensor(__A)
logger.info('''Head ranked by importance scores''')
_a = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device)
_a = torch.arange(
head_importance.numel() , device=args.device)
_a = head_ranks.view_as(__A)
print_ad_tensor(__A)
return attn_entropy, head_importance, total_loss
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a , _a , _a = compute_heads_importance(__A , __A , __A , compute_entropy=__A)
_a = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , __A , original_score * args.masking_threshold)
_a = torch.ones_like(__A)
_a = max(1 , int(new_head_mask.numel() * args.masking_amount))
_a = original_score
while current_score >= original_score * args.masking_threshold:
_a = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_a = float('''Inf''')
_a = head_importance.view(-1).sort()[1]
if len(__A) <= num_to_mask:
print('''BREAK BY num_to_mask''')
break
# mask heads
_a = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist()))
_a = new_head_mask.view(-1)
_a = 0.0
_a = new_head_mask.view_as(__A)
_a = new_head_mask.clone().detach()
print_ad_tensor(__A)
# Compute metric and head importance again
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , head_mask=__A)
_a = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''')
print_ad_tensor(__A)
np.save(os.path.join(args.output_dir , '''head_mask.npy''') , head_mask.detach().cpu().numpy())
return head_mask
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A)
_a = 1 / loss
_a = datetime.now() - before_time
_a = sum(p.numel() for p in model.parameters())
_a = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
}
for k, v in heads_to_prune.items():
if isinstance(__A , __A):
_a = [
v,
]
assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(__A)
_a = sum(p.numel() for p in model.parameters())
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A , actually_pruned=__A , )
_a = 1 / loss
_a = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , __A , __A , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , __A , __A)
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100)
save_model(__A , args.output_dir)
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=__A , type=__A , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=__A , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''')
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''')
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''')
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''')
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=__A , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=__A , help='''Amount to heads to masking at each masking step.''')
parser.add_argument('''--metric_name''' , default='''acc''' , type=__A , help='''Metric to use for head masking.''')
parser.add_argument(
'''--max_seq_length''' , default=128 , type=__A , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=__A , help='''Batch size.''')
parser.add_argument('''--seed''' , type=__A , default=42)
parser.add_argument('''--local_rank''' , type=__A , default=-1 , help='''local_rank for distributed training on gpus''')
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''')
parser.add_argument('''--server_ip''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
parser.add_argument('''--server_port''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
_a = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A)
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_a = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
_a = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
_a = torch.device('''cuda''' , args.local_rank)
_a = 1
torch.distributed.init_process_group(backend='''nccl''') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1)))
_a = GPTaLMHeadModel.from_pretrained(args.model_name_or_path)
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
_a = nn.parallel.DistributedDataParallel(
__A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__A)
elif args.n_gpu > 1:
_a = nn.DataParallel(__A)
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__A)
torch.save(__A , os.path.join(args.output_dir , '''run_args.bin'''))
logger.info('''Training/evaluation parameters %s''' , __A)
# Prepare dataset
_a = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa),
])
_a = (torch.from_numpy(__A),)
_a = TensorDataset(*__A)
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A , batch_size=args.batch_size)
# Compute head entropy and importance score
compute_heads_importance(__A , __A , __A)
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_a = mask_heads(__A , __A , __A)
prune_heads(__A , __A , __A , __A)
if __name__ == "__main__":
main()
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A)
if n > 1:
factors.append(__A)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 1
for i in range(0 , len(__A)):
total *= numbers[i]
_a = str(__A)
steps += 1
return steps
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''additive_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 0
for i in range(0 , len(__A)):
total += numbers[i]
_a = str(__A)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Tuple = BlenderbotSmallTokenizer
__lowerCamelCase : List[str] = False
def a__ (self ) -> Tuple:
"""simple docstring"""
super().setUp()
_a = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_a = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = 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(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> Optional[int]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Union[str, Any]:
"""simple docstring"""
_a = '''adapt act apte'''
_a = '''adapt act apte'''
return input_text, output_text
def a__ (self ) -> Any:
"""simple docstring"""
_a = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt act apte'''
_a = ['''adapt''', '''act''', '''ap@@''', '''te''']
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_a = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [1_384]
_a = '''I am a small frog.'''
_a = tok([src_text] , padding=A , truncation=A )['''input_ids''']
_a = tok.batch_decode(A , skip_special_tokens=A , clean_up_tokenization_spaces=A )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def a__ (self ) -> int:
"""simple docstring"""
_a = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_a = '''I am a small frog .'''
_a = '''.'''
_a = tok(A )['''input_ids''']
_a = tok(A )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_a = size if size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = DPTImageProcessor if is_vision_available() else None
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPTImageProcessingTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 11 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Any = KandinskyVaaPriorPipeline
__lowerCamelCase : Optional[Any] = ['prompt']
__lowerCamelCase : Tuple = ['prompt', 'negative_prompt']
__lowerCamelCase : Union[str, Any] = [
'num_images_per_prompt',
'generator',
'num_inference_steps',
'latents',
'negative_prompt',
'guidance_scale',
'output_type',
'return_dict',
]
__lowerCamelCase : int = False
@property
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return 32
@property
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
return 32
@property
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return self.time_input_dim
@property
def a__ (self ) -> Tuple:
"""simple docstring"""
return self.time_input_dim * 4
@property
def a__ (self ) -> Any:
"""simple docstring"""
return 100
@property
def a__ (self ) -> str:
"""simple docstring"""
_a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def a__ (self ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
_a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModelWithProjection(A )
@property
def a__ (self ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
_a = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 12,
'''embedding_dim''': self.text_embedder_hidden_size,
'''num_layers''': 1,
}
_a = PriorTransformer(**A )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
_a = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def a__ (self ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
_a = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
_a = CLIPVisionModelWithProjection(A )
return model
@property
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = CLIPImageProcessor(
crop_size=224 , do_center_crop=A , do_normalize=A , do_resize=A , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=224 , )
return image_processor
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.dummy_prior
_a = self.dummy_image_encoder
_a = self.dummy_text_encoder
_a = self.dummy_tokenizer
_a = self.dummy_image_processor
_a = UnCLIPScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1_000 , clip_sample=A , clip_sample_range=10.0 , )
_a = {
'''prior''': prior,
'''image_encoder''': image_encoder,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''scheduler''': scheduler,
'''image_processor''': image_processor,
}
return components
def a__ (self , A , A=0 ) -> Union[str, Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = {
'''prompt''': '''horse''',
'''generator''': generator,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def a__ (self ) -> int:
"""simple docstring"""
_a = '''cpu'''
_a = self.get_dummy_components()
_a = self.pipeline_class(**A )
_a = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_a = pipe(**self.get_dummy_inputs(A ) )
_a = output.image_embeds
_a = pipe(
**self.get_dummy_inputs(A ) , return_dict=A , )[0]
_a = image[0, -10:]
_a = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
_a = np.array(
[-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = torch_device == '''cpu'''
_a = True
_a = False
self._test_inference_batch_single_identical(
test_max_difference=A , relax_max_difference=A , test_mean_pixel_difference=A , )
@skip_mps
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = torch_device == '''cpu'''
_a = False
self._test_attention_slicing_forward_pass(
test_max_difference=A , test_mean_pixel_difference=A , )
| 11 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, 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
lowercase_ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __A :
'''simple docstring'''
def __init__(self , A , A=16 , A=13 , A=7 , A=14 , A=10 , A=19 , A=5 , A=4 , A=True , A=16 , A=2 , A=4 , A=4 , A="gelu" , A=0.1 , A=0.1 , A=[1, 2, 3, 4, 5] , A=25 , A=5 , ) -> List[str]:
"""simple docstring"""
_a = d_model
_a = parent
_a = batch_size
_a = prediction_length
_a = context_length
_a = cardinality
_a = num_time_features
_a = lags_sequence
_a = embedding_dimension
_a = is_training
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = context_length
_a = prediction_length + label_length
_a = label_length
_a = moving_average
_a = autocorrelation_factor
def a__ (self ) -> Any:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
_a = config.context_length + max(config.lags_sequence )
_a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, _past_length] )
_a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, config.prediction_length] )
_a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_config()
_a = self.prepare_autoformer_inputs_dict(A )
return config, inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModel(config=A ).to(A ).eval()
_a = model(**A )
_a = outputs.encoder_last_hidden_state
_a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_encoder()
encoder.save_pretrained(A )
_a = AutoformerEncoder.from_pretrained(A ).to(A )
_a , _a , _a , _a , _a = model.create_network_inputs(**A )
_a , _a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_a = encoder(inputs_embeds=A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
_a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_decoder()
decoder.save_pretrained(A )
_a = AutoformerDecoder.from_pretrained(A ).to(A )
_a = decoder(
trend=A , inputs_embeds=A , encoder_hidden_states=A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__lowerCamelCase : Tuple = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_a = model_class(A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a , _a = model_class.from_pretrained(A , output_loading_info=A )
self.assertEqual(info['''missing_keys'''] , [] )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*A )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = inspect.signature(getattr(A , '''forward''' ) )
# The main input is the name of the argument after `self`
_a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(A )] , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
_a = getattr(self.model_tester , '''seq_length''' , A )
_a = getattr(self.model_tester , '''decoder_seq_length''' , A )
_a = getattr(self.model_tester , '''encoder_seq_length''' , A )
_a = getattr(self.model_tester , '''d_model''' , A )
_a = getattr(self.model_tester , '''num_attention_heads''' , A )
_a = d_model // num_attention_heads
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_a = len(A )
_a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(A , A )
# decoder attentions
_a = outputs.decoder_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_a = outputs.cross_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
self.assertEqual(out_len + 2 , len(A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCAmelCase (__A="train-batch.pt"):
"""simple docstring"""
_a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__A , repo_type='''dataset''')
_a = torch.load(__A , map_location=__A)
return batch
@require_torch
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch()
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
_a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
_a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
_a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , A )
_a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=A )
_a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , A , rtol=1E-1 ) )
| 11 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowercase_ = 4
lowercase_ = 3
class __A ( A ):
'''simple docstring'''
pass
def lowerCAmelCase (__A):
"""simple docstring"""
for shard in shards:
for i in range(__A):
yield {"i": i, "shard": shard}
def lowerCAmelCase ():
"""simple docstring"""
_a = int(os.environ['''RANK'''])
_a = int(os.environ['''WORLD_SIZE'''])
_a = ArgumentParser()
parser.add_argument('''--streaming''' , type=__A)
parser.add_argument('''--local_rank''' , type=__A)
parser.add_argument('''--num_workers''' , type=__A , default=0)
_a = parser.parse_args()
_a = args.streaming
_a = args.num_workers
_a = {'''shards''': [F'''shard_{shard_idx}''' for shard_idx in range(__A)]}
_a = IterableDataset.from_generator(__A , gen_kwargs=__A)
if not streaming:
_a = Dataset.from_list(list(__A))
_a = split_dataset_by_node(__A , rank=__A , world_size=__A)
_a = torch.utils.data.DataLoader(__A , num_workers=__A)
_a = NUM_SHARDS * NUM_ITEMS_PER_SHARD
_a = full_size // world_size
expected_local_size += int(rank < (full_size % world_size))
_a = sum(1 for _ in dataloader)
if local_size != expected_local_size:
raise FailedTestError(F'''local_size {local_size} != expected_local_size {expected_local_size}''')
if __name__ == "__main__":
main()
| 11 |
'''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 :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=False , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> str:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , use_stable_embedding=A , )
def a__ (self , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = OpenLlamaModel(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A )
_a = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Any:
"""simple docstring"""
_a = True
_a = OpenLlamaModel(A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , )
_a = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Dict:
"""simple docstring"""
_a = True
_a = True
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_a = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_a = ids_tensor((self.batch_size, 3) , config.vocab_size )
_a = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_a = torch.cat([input_ids, next_tokens] , dim=-1 )
_a = torch.cat([input_mask, next_mask] , dim=-1 )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )['''hidden_states'''][0]
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )['''hidden_states'''][0]
# select random slice
_a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a = output_from_no_past[:, -3:, random_slice_idx].detach()
_a = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__lowerCamelCase : Any = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__lowerCamelCase : List[Any] = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''single_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''multi_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = ids_tensor([1, 10] , config.vocab_size )
_a = 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
_a = OpenLlamaModel(A )
original_model.to(A )
original_model.eval()
_a = original_model(A ).last_hidden_state
_a = original_model(A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = {'''type''': scaling_type, '''factor''': 10.0}
_a = OpenLlamaModel(A )
scaled_model.to(A )
scaled_model.eval()
_a = scaled_model(A ).last_hidden_state
_a = scaled_model(A ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
| 11 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : List[str] = 'mgp-str'
def __init__(self , A=[32, 128] , A=4 , A=3 , A=27 , A=38 , A=50_257 , A=30_522 , A=768 , A=12 , A=12 , A=4.0 , A=True , A=False , A=1E-5 , A=0.0 , A=0.0 , A=0.0 , A=False , A=0.02 , **A , ) -> Dict:
"""simple docstring"""
super().__init__(**A )
_a = image_size
_a = patch_size
_a = num_channels
_a = max_token_length
_a = num_character_labels
_a = num_bpe_labels
_a = num_wordpiece_labels
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = mlp_ratio
_a = distilled
_a = layer_norm_eps
_a = drop_rate
_a = qkv_bias
_a = attn_drop_rate
_a = drop_path_rate
_a = output_aa_attentions
_a = initializer_range
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_choices
def a__ (self ) -> str:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxAlbertModelTester(self )
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''albert-base-v2''' )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Dict:
"""simple docstring"""
_a = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
_a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_a = model(A , attention_mask=A )[0]
_a = (1, 11, 768)
self.assertEqual(output.shape , A )
_a = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
while b:
_a , _a = b, a % b
return a
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(__A , a % b)
def lowerCAmelCase ():
"""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()
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a = 0
_a = len(__A) - 2
for i in range(__A , -1 , -2):
# double the value of every second digit
_a = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_a = cc_number[:i] + str(__A) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCAmelCase (__A):
"""simple docstring"""
_a = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''')
return False
if not 13 <= len(__A) <= 16:
print(F'''{error_message} of its length.''')
return False
if not validate_initial_digits(__A):
print(F'''{error_message} of its first two digits.''')
return False
if not luhn_validation(__A):
print(F'''{error_message} it fails the Luhn check.''')
return False
print(F'''{credit_card_number} is a valid credit card number.''')
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 11 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : int = 'codegen'
__lowerCamelCase : Dict = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , A=50_400 , A=2_048 , A=2_048 , A=4_096 , A=28 , A=16 , A=64 , A=None , A="gelu_new" , A=0.0 , A=0.0 , A=0.0 , A=1E-5 , A=0.02 , A=True , A=50_256 , A=50_256 , A=False , **A , ) -> Optional[Any]:
"""simple docstring"""
_a = vocab_size
_a = n_ctx
_a = n_positions
_a = n_embd
_a = n_layer
_a = n_head
_a = n_inner
_a = rotary_dim
_a = activation_function
_a = resid_pdrop
_a = embd_pdrop
_a = attn_pdrop
_a = layer_norm_epsilon
_a = initializer_range
_a = use_cache
_a = bos_token_id
_a = eos_token_id
super().__init__(
bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A )
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A = "default" , A = None , A = False , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(A , task=A , patching_specs=A , use_past=A )
if not getattr(self._config , '''pad_token_id''' , A ):
# TODO: how to do that better?
_a = 0
@property
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
_a = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(A , direction='''inputs''' )
_a = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
_a = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def a__ (self ) -> int:
"""simple docstring"""
return self._config.n_layer
@property
def a__ (self ) -> int:
"""simple docstring"""
return self._config.n_head
def a__ (self , A , A = -1 , A = -1 , A = False , A = None , ) -> Mapping[str, Any]:
"""simple docstring"""
_a = super(A , self ).generate_dummy_inputs(
A , batch_size=A , seq_length=A , is_pair=A , framework=A )
# We need to order the input in the way they appears in the forward()
_a = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_a , _a = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_a = seqlen + 2
_a = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_a = [
(torch.zeros(A ), torch.zeros(A )) for _ in range(self.num_layers )
]
_a = common_inputs['''attention_mask''']
if self.use_past:
_a = ordered_inputs['''attention_mask'''].dtype
_a = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(A , A , dtype=A )] , dim=1 )
return ordered_inputs
@property
def a__ (self ) -> int:
"""simple docstring"""
return 13
| 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 | 1 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str = None
__lowerCamelCase : Optional[Any] = None
@property
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(A , '''feature_size''' ) )
self.assertTrue(hasattr(A , '''sampling_rate''' ) )
self.assertTrue(hasattr(A , '''padding_value''' ) )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(A ) == len(A ) for x, y in zip(A , processed_features[input_name] ) ) )
_a = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
_a = BatchFeature({input_name: speech_inputs} , tensor_type='''np''' )
_a = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_a = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} , tensor_type='''pt''' )
_a = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_a = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def a__ (self ) -> str:
"""simple docstring"""
_a = self.feat_extract_tester.prepare_inputs_for_common(equal_length=A )
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} , tensor_type='''tf''' )
_a = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_a = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def a__ (self , A=False ) -> Any:
"""simple docstring"""
def _inputs_have_equal_length(A ):
_a = len(input[0] )
for input_slice in input[1:]:
if len(A ) != length:
return False
return True
def _inputs_are_equal(A , A ):
if len(A ) != len(A ):
return False
for input_slice_a, input_slice_a in zip(A , A ):
if not np.allclose(np.asarray(A ) , np.asarray(A ) , atol=1E-3 ):
return False
return True
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = self.feat_extract_tester.prepare_inputs_for_common(numpify=A )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = self.feat_extract_tester.seq_length_diff
_a = self.feat_extract_tester.max_seq_length + pad_diff
_a = self.feat_extract_tester.min_seq_length
_a = self.feat_extract_tester.batch_size
_a = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_a = feat_extract.pad(A , padding=A )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''longest''' )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''max_length''' , max_length=len(speech_inputs[-1] ) )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''np''' )
_a = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(A ):
feat_extract.pad(A , padding='''max_length''' )[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=A , return_tensors='''np''' )
_a = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_are_equal(A , A ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_a = feat_extract.pad(A , pad_to_multiple_of=10 )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''longest''' , pad_to_multiple_of=10 )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , pad_to_multiple_of=10 , max_length=A )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , pad_to_multiple_of=10 , max_length=A , return_tensors='''np''' , )
_a = input_a[input_name]
self.assertTrue(all(len(A ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(A , A ) )
_a = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(A ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_a = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1E-3 )
def a__ (self , A=False ) -> List[Any]:
"""simple docstring"""
def _inputs_have_equal_length(A ):
_a = len(input[0] )
for input_slice in input[1:]:
if len(A ) != length:
return False
return True
def _inputs_are_equal(A , A ):
if len(A ) != len(A ):
return False
for input_slice_a, input_slice_a in zip(A , A ):
if not np.allclose(np.asarray(A ) , np.asarray(A ) , atol=1E-3 ):
return False
return True
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = self.feat_extract_tester.prepare_inputs_for_common(numpify=A )
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , truncation=A )
_a = input_a[input_name]
_a = feat_extract.pad(A , padding='''max_length''' , max_length=len(speech_inputs[0] ) )
_a = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertFalse(_inputs_have_equal_length(A ) )
# truncate to smallest with np
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , return_tensors='''np''' , truncation=A , )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , return_tensors='''np''' )
_a = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A ) )
# truncate to middle
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[1] ) , truncation=A , return_tensors='''np''' , )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[1] ) , truncation=A )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[1] ) , return_tensors='''np''' )
_a = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertTrue(_inputs_are_equal(A , A ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(A ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , truncation=A )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , padding='''longest''' , truncation=A )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(A ):
feat_extract.pad(A , padding='''longest''' , truncation=A )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(A ):
feat_extract.pad(A , padding='''max_length''' , truncation=A )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_a = 12
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A , truncation=A , )
_a = input_a[input_name]
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=A , )
_a = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_a = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_a = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(A ) )
self.assertFalse(_inputs_have_equal_length(A ) )
def a__ (self ) -> List[Any]:
"""simple docstring"""
self._check_padding(numpify=A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self._check_padding(numpify=A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self._check_truncation(numpify=A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._check_truncation(numpify=A )
@require_torch
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''np''' )[input_name]
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''pt''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
@require_tf
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.feature_extraction_class(**self.feat_extract_dict )
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''np''' )[input_name]
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''tf''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.feat_extract_dict
_a = True
_a = self.feature_extraction_class(**A )
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = [len(A ) for x in speech_inputs]
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = feat_extract.pad(A , padding='''longest''' , return_tensors='''np''' )
self.assertIn('''attention_mask''' , A )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , A )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.feat_extract_dict
_a = True
_a = self.feature_extraction_class(**A )
_a = self.feat_extract_tester.prepare_inputs_for_common()
_a = [len(A ) for x in speech_inputs]
_a = feat_extract.model_input_names[0]
_a = BatchFeature({input_name: speech_inputs} )
_a = min(A )
_a = feat_extract.pad(
A , padding='''max_length''' , max_length=A , truncation=A , return_tensors='''np''' )
self.assertIn('''attention_mask''' , A )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 11 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase (__A = "laptop"):
"""simple docstring"""
_a = F'''https://www.amazon.in/laptop/s?k={product}'''
_a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
_a = BeautifulSoup(requests.get(__A , headers=__A).text)
# Initialize a Pandas dataframe with the column titles
_a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
_a = item.ha.text
_a = '''https://www.amazon.in/''' + item.ha.a['''href''']
_a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
_a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
_a = '''Not available'''
try:
_a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
_a = ''''''
try:
_a = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
_a = float('''nan''')
except AttributeError:
pass
_a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_a = ''' '''
_a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A = False):
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
raise ValueError(
'''Warning: upper bound of deterministic test is exceeded. '''
'''Pass allow_probable=True to allow probabilistic test. '''
'''A return value of True indicates a probable prime.''')
# array bounds provided by analysis
_a = [
2_047,
1_373_653,
25_326_001,
3_215_031_751,
2_152_302_898_747,
3_474_749_660_383,
341_550_071_728_321,
1,
3_825_123_056_546_413_051,
1,
1,
318_665_857_834_031_151_167_461,
3_317_044_064_679_887_385_961_981,
]
_a = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(__A , 1):
if n < _p:
# then we have our last prime to check
_a = primes[:idx]
break
_a , _a = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
_a = False
for r in range(__A):
_a = pow(__A , d * 2**r , __A)
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
_a = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def lowerCAmelCase ():
"""simple docstring"""
assert not miller_rabin(561)
assert miller_rabin(563)
# 2047
assert not miller_rabin(838_201)
assert miller_rabin(838_207)
# 1_373_653
assert not miller_rabin(17_316_001)
assert miller_rabin(17_316_017)
# 25_326_001
assert not miller_rabin(3_078_386_641)
assert miller_rabin(3_078_386_653)
# 3_215_031_751
assert not miller_rabin(1_713_045_574_801)
assert miller_rabin(1_713_045_574_819)
# 2_152_302_898_747
assert not miller_rabin(2_779_799_728_307)
assert miller_rabin(2_779_799_728_327)
# 3_474_749_660_383
assert not miller_rabin(113_850_023_909_441)
assert miller_rabin(113_850_023_909_527)
# 341_550_071_728_321
assert not miller_rabin(1_275_041_018_848_804_351)
assert miller_rabin(1_275_041_018_848_804_391)
# 3_825_123_056_546_413_051
assert not miller_rabin(79_666_464_458_507_787_791_867)
assert miller_rabin(79_666_464_458_507_787_791_951)
# 318_665_857_834_031_151_167_461
assert not miller_rabin(552_840_677_446_647_897_660_333)
assert miller_rabin(552_840_677_446_647_897_660_359)
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 11 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if isinstance(__A , torch.Tensor):
return image
elif isinstance(__A , PIL.Image.Image):
_a = [image]
if isinstance(image[0] , PIL.Image.Image):
_a = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
_a = np.concatenate(__A , axis=0)
_a = np.array(__A).astype(np.floataa) / 2_55.0
_a = image.transpose(0 , 3 , 1 , 2)
_a = 2.0 * image - 1.0
_a = torch.from_numpy(__A)
elif isinstance(image[0] , torch.Tensor):
_a = torch.cat(__A , dim=0)
return image
def lowerCAmelCase (__A , __A , __A , __A=0.99_95):
"""simple docstring"""
if not isinstance(__A , np.ndarray):
_a = True
_a = va.device
_a = va.cpu().numpy()
_a = va.cpu().numpy()
_a = np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
if np.abs(__A) > DOT_THRESHOLD:
_a = (1 - t) * va + t * va
else:
_a = np.arccos(__A)
_a = np.sin(__A)
_a = theta_a * t
_a = np.sin(__A)
_a = np.sin(theta_a - theta_t) / sin_theta_a
_a = sin_theta_t / sin_theta_a
_a = sa * va + sa * va
if inputs_are_torch:
_a = torch.from_numpy(__A).to(__A)
return va
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = F.normalize(__A , dim=-1)
_a = F.normalize(__A , dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for param in model.parameters():
_a = value
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
_a = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size['''shortest_edge''']
)
_a = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def a__ (self , A = "auto" ) -> Union[str, Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(A )
def a__ (self ) -> int:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self ) -> str:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = min(int(num_inference_steps * strength ) , A )
_a = max(num_inference_steps - init_timestep , 0 )
_a = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a__ (self , A , A , A , A , A , A=None ) -> List[str]:
"""simple docstring"""
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
_a = image.to(device=A , dtype=A )
if isinstance(A , A ):
_a = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
_a = torch.cat(A , dim=0 )
else:
_a = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 0.18215 * init_latents
_a = init_latents.repeat_interleave(A , dim=0 )
_a = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
_a = self.scheduler.add_noise(A , A , A )
_a = init_latents
return latents
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_a = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_a = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.feature_extractor.preprocess(A )
_a = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a__ (self , A , A , A , A , A , A , A , ) -> Union[str, Any]:
"""simple docstring"""
_a = latents.detach().requires_grad_()
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_a = self.scheduler.alphas_cumprod[timestep]
_a = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_a = torch.sqrt(A )
_a = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
_a = self.scheduler.sigmas[index]
_a = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * sample
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = transforms.Resize(self.feature_extractor_size )(A )
_a = self.normalize(A ).to(latents.dtype )
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
_a = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
_a = latents.detach() + grads * (sigma**2)
_a = noise_pred_original
else:
_a = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> str:
"""simple docstring"""
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
_a = [generator] + [None] * (batch_size - 1)
_a = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_a = [x[0] for x in coca_is_none if x[1]]
_a = ''', '''.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
# get prompt text embeddings for content and style
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_a = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
_a = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
_a = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_a = {}
if accepts_offset:
_a = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_a , _a = self.get_timesteps(A , A , self.device )
_a = timesteps[:1].repeat(A )
# Preprocess image
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = slerp(A , A , A )
if clip_guidance_scale > 0:
_a = self.get_clip_image_embeddings(A , A )
_a = self.get_clip_image_embeddings(A , A )
_a = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_a = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_a = content_text_input.input_ids.shape[-1]
_a = self.tokenizer([''''''] , padding='''max_length''' , max_length=A , return_tensors='''pt''' )
_a = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_a = uncond_embeddings.repeat_interleave(A , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_a = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_a = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_a = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_a = torch.randn(A , generator=A , device='''cpu''' , dtype=A ).to(
self.device )
else:
_a = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
# check if the scheduler accepts generator
_a = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_a = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_a = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_a , _a = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * latents
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 11 | 1 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
def __init__(self , A=None , **A ) -> str:
"""simple docstring"""
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , A , )
super().__init__(args=A , **A )
| 11 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = CTRLTokenizer
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Any = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = 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(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = '''adapt react readapt apt'''
_a = '''adapt react readapt apt'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt react readapt apt'''
_a = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
| 11 | 1 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowercase_ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = ['''layers''', '''blocks''']
for k in ignore_keys:
state_dict.pop(__A , __A)
lowercase_ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
return s_dict
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = emb.weight.shape
_a = nn.Linear(__A , __A , bias=__A)
_a = emb.weight.data
return lin_layer
def lowerCAmelCase (__A , __A):
"""simple docstring"""
os.makedirs(__A , exist_ok=__A)
_a = os.path.basename(__A)
_a = url.split('''/''')[-2]
_a = os.path.join(__A , __A)
if os.path.exists(__A) and not os.path.isfile(__A):
raise RuntimeError(F'''{download_target} exists and is not a regular file''')
if os.path.isfile(__A):
_a = open(__A , '''rb''').read()
if hashlib.shaaaa(__A).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file''')
with urllib.request.urlopen(__A) as source, open(__A , '''wb''') as output:
with tqdm(
total=int(source.info().get('''Content-Length''')) , ncols=80 , unit='''iB''' , unit_scale=__A , unit_divisor=1_024) as loop:
while True:
_a = source.read(8_192)
if not buffer:
break
output.write(__A)
loop.update(len(__A))
_a = open(__A , '''rb''').read()
if hashlib.shaaaa(__A).hexdigest() != expected_shaaaa:
raise RuntimeError(
'''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.''')
return model_bytes
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if ".pt" not in checkpoint_path:
_a = _download(_MODELS[checkpoint_path])
else:
_a = torch.load(__A , map_location='''cpu''')
_a = original_checkpoint['''dims''']
_a = original_checkpoint['''model_state_dict''']
_a = state_dict['''decoder.token_embedding.weight''']
remove_ignore_keys_(__A)
rename_keys(__A)
_a = True
_a = state_dict['''decoder.layers.0.fc1.weight'''].shape[0]
_a = WhisperConfig(
vocab_size=dimensions['''n_vocab'''] , encoder_ffn_dim=__A , decoder_ffn_dim=__A , num_mel_bins=dimensions['''n_mels'''] , d_model=dimensions['''n_audio_state'''] , max_target_positions=dimensions['''n_text_ctx'''] , encoder_layers=dimensions['''n_audio_layer'''] , encoder_attention_heads=dimensions['''n_audio_head'''] , decoder_layers=dimensions['''n_text_layer'''] , decoder_attention_heads=dimensions['''n_text_state'''] , max_source_positions=dimensions['''n_audio_ctx'''] , )
_a = WhisperForConditionalGeneration(__A)
_a , _a = model.model.load_state_dict(__A , strict=__A)
if len(__A) > 0 and not set(__A) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
F''' but all the following weights are missing {missing}''')
if tie_embeds:
_a = make_linear_from_emb(model.model.decoder.embed_tokens)
else:
_a = proj_out_weights
model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
lowercase_ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 11 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 | 1 |
'''simple docstring'''
import sys
import turtle
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCAmelCase (__A , __A , __A , __A , ):
"""simple docstring"""
my_pen.up()
my_pen.goto(vertexa[0] , vertexa[1])
my_pen.down()
my_pen.goto(vertexa[0] , vertexa[1])
my_pen.goto(vertexa[0] , vertexa[1])
my_pen.goto(vertexa[0] , vertexa[1])
if depth == 0:
return
triangle(__A , get_mid(__A , __A) , get_mid(__A , __A) , depth - 1)
triangle(__A , get_mid(__A , __A) , get_mid(__A , __A) , depth - 1)
triangle(__A , get_mid(__A , __A) , get_mid(__A , __A) , depth - 1)
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
"Correct format for using this script: "
"python fractals.py <int:depth_for_fractal>"
)
lowercase_ = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor("red")
lowercase_ = [(-175, -125), (0, 175), (175, -125)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__A , int(b / 2)) * actual_power(__A , int(b / 2))
else:
return a * actual_power(__A , int(b / 2)) * actual_power(__A , int(b / 2))
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if b < 0:
return 1 / actual_power(__A , __A)
return actual_power(__A , __A)
if __name__ == "__main__":
print(power(-2, -3))
| 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowercase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_ = 10
lowercase_ = 256
def lowerCAmelCase (__A):
"""simple docstring"""
if len(__A) < MIN_NUM_TOKENS:
return None
_a = MinHash(num_perm=__A)
for token in set(__A):
min_hash.update(token.encode())
return min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}
class __A :
'''simple docstring'''
def __init__(self , *,
A = 0.85 , ) -> Optional[int]:
"""simple docstring"""
_a = duplication_jaccard_threshold
_a = NUM_PERM
_a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_a = defaultdict(A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
_a = self._index.query(A )
if code_key in self._index.keys:
print(f'''Duplicate key {code_key}''' )
return
self._index.insert(A , A )
if len(A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A )
def a__ (self ) -> List[List[Dict]]:
"""simple docstring"""
_a = []
for base, duplicates in self._duplicate_clusters.items():
_a = [base] + list(A )
# reformat the cluster to be a list of dict
_a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(A )
return duplicate_clusters
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_duplicate_clusters()
with open(A , '''w''' ) as f:
json.dump(A , A )
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = element
_a = get_min_hash([t for t in NON_ALPHA.split(data['''content''']) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__A , max_queue_size=10_000) , chunksize=100 , ):
if data is not None:
yield data
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = DuplicationIndex(duplication_jaccard_threshold=__A)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A)) , max_queue_size=100)):
di.add(__A , __A)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = get_tokens(__A)
_a = get_tokens(__A)
return len(tokensa & tokensa) / len(tokensa | tokensa)
lowercase_ = None
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for elementa in cluster:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__A , __A) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_a = 1
extremes.append(__A)
return extremes
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
global _shared_dataset
_a = dataset
_a = []
_a = partial(_find_cluster_extremes_shared , jaccard_threshold=__A)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__A , __A , ) , total=len(__A) , ):
extremes_list.append(__A)
return extremes_list
def lowerCAmelCase (__A , __A = 0.85):
"""simple docstring"""
_a = make_duplicate_clusters(__A , __A)
_a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_a = {}
_a = find_extremes(__A , __A , __A)
for extremes in extremes_clusters:
for element in extremes:
_a = element
_a = duplicate_indices - set(extreme_dict.keys())
_a = dataset.filter(lambda __A , __A: idx not in remove_indices , with_indices=__A)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_a = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(__A)}''')
print(F'''Number of duplicate clusters: {len(__A)}''')
print(F'''Files in duplicate cluster: {len(__A)}''')
print(F'''Unique files in duplicate cluster: {len(__A)}''')
print(F'''Filtered dataset size: {len(__A)}''')
return ds_filter, duplicate_clusters
| 11 | 1 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : int = 'detr'
__lowerCamelCase : Tuple = ['past_key_values']
__lowerCamelCase : Union[str, Any] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__(self , A=True , A=None , A=3 , A=100 , A=6 , A=2_048 , A=8 , A=6 , A=2_048 , A=8 , A=0.0 , A=0.0 , A=True , A="relu" , A=256 , A=0.1 , A=0.0 , A=0.0 , A=0.02 , A=1.0 , A=False , A="sine" , A="resnet50" , A=True , A=False , A=1 , A=5 , A=2 , A=1 , A=1 , A=5 , A=2 , A=0.1 , **A , ) -> int:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
_a = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(A , A ):
_a = backbone_config.get('''model_type''' )
_a = CONFIG_MAPPING[backbone_model_type]
_a = config_class.from_dict(A )
# set timm attributes to None
_a , _a , _a = None, None, None
_a = use_timm_backbone
_a = backbone_config
_a = num_channels
_a = num_queries
_a = d_model
_a = encoder_ffn_dim
_a = encoder_layers
_a = encoder_attention_heads
_a = decoder_ffn_dim
_a = decoder_layers
_a = decoder_attention_heads
_a = dropout
_a = attention_dropout
_a = activation_dropout
_a = activation_function
_a = init_std
_a = init_xavier_std
_a = encoder_layerdrop
_a = decoder_layerdrop
_a = encoder_layers
_a = auxiliary_loss
_a = position_embedding_type
_a = backbone
_a = use_pretrained_backbone
_a = dilation
# Hungarian matcher
_a = class_cost
_a = bbox_cost
_a = giou_cost
# Loss coefficients
_a = mask_loss_coefficient
_a = dice_loss_coefficient
_a = bbox_loss_coefficient
_a = giou_loss_coefficient
_a = eos_coefficient
super().__init__(is_encoder_decoder=A , **A )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.d_model
@classmethod
def a__ (cls , A , **A ) -> Optional[int]:
"""simple docstring"""
return cls(backbone_config=A , **A )
def a__ (self ) -> Dict[str, any]:
"""simple docstring"""
_a = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
_a = self.backbone_config.to_dict()
_a = self.__class__.model_type
return output
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = version.parse('1.11' )
@property
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def a__ (self ) -> float:
"""simple docstring"""
return 1E-5
@property
def a__ (self ) -> int:
"""simple docstring"""
return 12
| 11 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 | 1 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if isinstance(__A , torch.Tensor):
return image
elif isinstance(__A , PIL.Image.Image):
_a = [image]
if isinstance(image[0] , PIL.Image.Image):
_a = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
_a = np.concatenate(__A , axis=0)
_a = np.array(__A).astype(np.floataa) / 2_55.0
_a = image.transpose(0 , 3 , 1 , 2)
_a = 2.0 * image - 1.0
_a = torch.from_numpy(__A)
elif isinstance(image[0] , torch.Tensor):
_a = torch.cat(__A , dim=0)
return image
def lowerCAmelCase (__A , __A , __A , __A=0.99_95):
"""simple docstring"""
if not isinstance(__A , np.ndarray):
_a = True
_a = va.device
_a = va.cpu().numpy()
_a = va.cpu().numpy()
_a = np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
if np.abs(__A) > DOT_THRESHOLD:
_a = (1 - t) * va + t * va
else:
_a = np.arccos(__A)
_a = np.sin(__A)
_a = theta_a * t
_a = np.sin(__A)
_a = np.sin(theta_a - theta_t) / sin_theta_a
_a = sin_theta_t / sin_theta_a
_a = sa * va + sa * va
if inputs_are_torch:
_a = torch.from_numpy(__A).to(__A)
return va
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = F.normalize(__A , dim=-1)
_a = F.normalize(__A , dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for param in model.parameters():
_a = value
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
_a = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size['''shortest_edge''']
)
_a = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def a__ (self , A = "auto" ) -> Union[str, Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(A )
def a__ (self ) -> int:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self ) -> str:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = min(int(num_inference_steps * strength ) , A )
_a = max(num_inference_steps - init_timestep , 0 )
_a = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a__ (self , A , A , A , A , A , A=None ) -> List[str]:
"""simple docstring"""
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
_a = image.to(device=A , dtype=A )
if isinstance(A , A ):
_a = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
_a = torch.cat(A , dim=0 )
else:
_a = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 0.18215 * init_latents
_a = init_latents.repeat_interleave(A , dim=0 )
_a = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
_a = self.scheduler.add_noise(A , A , A )
_a = init_latents
return latents
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_a = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_a = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.feature_extractor.preprocess(A )
_a = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a__ (self , A , A , A , A , A , A , A , ) -> Union[str, Any]:
"""simple docstring"""
_a = latents.detach().requires_grad_()
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_a = self.scheduler.alphas_cumprod[timestep]
_a = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_a = torch.sqrt(A )
_a = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
_a = self.scheduler.sigmas[index]
_a = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * sample
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = transforms.Resize(self.feature_extractor_size )(A )
_a = self.normalize(A ).to(latents.dtype )
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
_a = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
_a = latents.detach() + grads * (sigma**2)
_a = noise_pred_original
else:
_a = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> str:
"""simple docstring"""
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
_a = [generator] + [None] * (batch_size - 1)
_a = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_a = [x[0] for x in coca_is_none if x[1]]
_a = ''', '''.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
# get prompt text embeddings for content and style
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_a = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
_a = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
_a = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_a = {}
if accepts_offset:
_a = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_a , _a = self.get_timesteps(A , A , self.device )
_a = timesteps[:1].repeat(A )
# Preprocess image
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = slerp(A , A , A )
if clip_guidance_scale > 0:
_a = self.get_clip_image_embeddings(A , A )
_a = self.get_clip_image_embeddings(A , A )
_a = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_a = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_a = content_text_input.input_ids.shape[-1]
_a = self.tokenizer([''''''] , padding='''max_length''' , max_length=A , return_tensors='''pt''' )
_a = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_a = uncond_embeddings.repeat_interleave(A , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_a = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_a = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_a = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_a = torch.randn(A , generator=A , device='''cpu''' , dtype=A ).to(
self.device )
else:
_a = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
# check if the scheduler accepts generator
_a = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_a = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_a = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_a , _a = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * latents
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 11 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = IFInpaintingSuperResolutionPipeline
__lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
__lowerCamelCase : str = PipelineTesterMixin.required_optional_params - {'latents'}
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def a__ (self , A , A=0 ) -> List[Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = floats_tensor((1, 3, 16, 16) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ (self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def a__ (self ) -> str:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._test_save_load_local()
def a__ (self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 11 | 1 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
def __init__(self , *A , **A ) -> None:
"""simple docstring"""
warnings.warn(
'''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use DeformableDetrImageProcessor instead.''' , A , )
super().__init__(*A , **A )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return len(set(__A)) == len(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from typing import Any
class __A :
'''simple docstring'''
def __init__(self , A ) -> int:
"""simple docstring"""
_a = data
_a = None
def __repr__(self ) -> str:
"""simple docstring"""
return f'''Node({self.data})'''
class __A :
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
_a = None
def __iter__(self ) -> Any:
"""simple docstring"""
_a = self.head
while node:
yield node.data
_a = node.next
def __len__(self ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __repr__(self ) -> str:
"""simple docstring"""
return "->".join([str(A ) for item in self] )
def __getitem__(self , A ) -> Any:
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('''list index out of range.''' )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__(self , A , A ) -> None:
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError('''list index out of range.''' )
_a = self.head
for _ in range(A ):
_a = current.next
_a = data
def a__ (self , A ) -> None:
"""simple docstring"""
self.insert_nth(len(self ) , A )
def a__ (self , A ) -> None:
"""simple docstring"""
self.insert_nth(0 , A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
if not 0 <= index <= len(self ):
raise IndexError('''list index out of range''' )
_a = Node(A )
if self.head is None:
_a = new_node
elif index == 0:
_a = self.head # link new_node to head
_a = new_node
else:
_a = self.head
for _ in range(index - 1 ):
_a = temp.next
_a = temp.next
_a = new_node
def a__ (self ) -> None: # print every node data
"""simple docstring"""
print(self )
def a__ (self ) -> Any:
"""simple docstring"""
return self.delete_nth(0 )
def a__ (self ) -> Any: # delete from tail
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def a__ (self , A = 0 ) -> Any:
"""simple docstring"""
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError('''List index out of range.''' )
_a = self.head # default first node
if index == 0:
_a = self.head.next
else:
_a = self.head
for _ in range(index - 1 ):
_a = temp.next
_a = temp.next
_a = temp.next.next
return delete_node.data
def a__ (self ) -> bool:
"""simple docstring"""
return self.head is None
def a__ (self ) -> None:
"""simple docstring"""
_a = None
_a = self.head
while current:
# Store the current node's next node.
_a = current.next
# Make the current node's next point backwards
_a = prev
# Make the previous node be the current node
_a = current
# Make the current node the next node (to progress iteration)
_a = next_node
# Return prev in order to put the head at the end
_a = prev
def lowerCAmelCase ():
"""simple docstring"""
_a = LinkedList()
assert linked_list.is_empty() is True
assert str(__A) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10):
assert len(__A) == i
linked_list.insert_nth(__A , i + 1)
assert str(__A) == "->".join(str(__A) for i in range(1 , 11))
linked_list.insert_head(0)
linked_list.insert_tail(11)
assert str(__A) == "->".join(str(__A) for i in range(0 , 12))
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9) == 10
assert linked_list.delete_tail() == 11
assert len(__A) == 9
assert str(__A) == "->".join(str(__A) for i in range(1 , 10))
assert all(linked_list[i] == i + 1 for i in range(0 , 9)) is True
for i in range(0 , 9):
_a = -i
assert all(linked_list[i] == -i for i in range(0 , 9)) is True
linked_list.reverse()
assert str(__A) == "->".join(str(__A) for i in range(-8 , 1))
def lowerCAmelCase ():
"""simple docstring"""
_a = [
-9,
100,
Node(77_345_112),
'''dlrow olleH''',
7,
5_555,
0,
-1_92.5_55_55,
'''Hello, world!''',
77.9,
Node(10),
None,
None,
12.20,
]
_a = LinkedList()
for i in test_input:
linked_list.insert_tail(__A)
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(__A) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
_a = linked_list.delete_head()
assert result == -9
assert (
str(__A) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
_a = linked_list.delete_tail()
assert result == 12.2
assert (
str(__A) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
_a = linked_list.delete_nth(10)
assert result is None
assert (
str(__A) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node('''Hello again, world!'''))
assert (
str(__A)
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(__A)
assert (
str(__A)
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(__A)
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def lowerCAmelCase ():
"""simple docstring"""
from doctest import testmod
testmod()
_a = LinkedList()
linked_list.insert_head(input('''Inserting 1st at head ''').strip())
linked_list.insert_head(input('''Inserting 2nd at head ''').strip())
print('''\nPrint list:''')
linked_list.print_list()
linked_list.insert_tail(input('''\nInserting 1st at tail ''').strip())
linked_list.insert_tail(input('''Inserting 2nd at tail ''').strip())
print('''\nPrint list:''')
linked_list.print_list()
print('''\nDelete head''')
linked_list.delete_head()
print('''Delete tail''')
linked_list.delete_tail()
print('''\nPrint list:''')
linked_list.print_list()
print('''\nReverse linked list''')
linked_list.reverse()
print('''\nPrint list:''')
linked_list.print_list()
print('''\nString representation of linked list:''')
print(__A)
print('''\nReading/changing Node data using indexing:''')
print(F'''Element at Position 1: {linked_list[1]}''')
_a = input('''Enter New Value: ''').strip()
print('''New list:''')
print(__A)
print(F'''length of linked_list is : {len(__A)}''')
if __name__ == "__main__":
main()
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) == 0:
return False
_a = len(__A) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __A)
else:
return binary_search(a_list[midpoint + 1 :] , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
lowercase_ = int(input("Enter the number to be found in the list:\n").strip())
lowercase_ = "" if binary_search(sequence, target) else "not "
print(F"""{target} was {not_str}found in {sequence}""")
| 11 | 1 |
'''simple docstring'''
import pprint
import requests
lowercase_ = "https://zenquotes.io/api"
def lowerCAmelCase ():
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '''/today''').json()
def lowerCAmelCase ():
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '''/random''').json()
if __name__ == "__main__":
lowercase_ = random_quotes()
pprint.pprint(response)
| 11 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A ):
_a = self.prefix_sum[i - 1] + array[i]
def a__ (self , A , A ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if any(not isinstance(__A , __A) or x < 0 for x in sequence):
raise TypeError('''Sequence must be list of non-negative integers''')
for _ in range(len(__A)):
for i, (rod_upper, rod_lower) in enumerate(zip(__A , sequence[1:])):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A)
if n > 1:
factors.append(__A)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def lowerCAmelCase (__A):
"""simple docstring"""
_a = torch.load(__A , map_location='''cpu''')
if "model" in sd.keys():
_a = torch.load(__A , map_location='''cpu''')['''model''']
# pop unnecessary weights
_a = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(__A)
_a = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
_a = sd.pop(__A)
_a = list(sd.keys())
for key in keys:
if ".qkv_proj." in key:
_a = sd[key]
# We split QKV in separate Q,K,V
_a = key.replace('''.qkv_proj.''' , '''.q_proj.''')
_a = key.replace('''.qkv_proj.''' , '''.k_proj.''')
_a = key.replace('''.qkv_proj.''' , '''.v_proj.''')
_a = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
_a , _a , _a = torch.split(__A , depth // 3 , dim=0)
_a = q
_a = k
_a = v
del sd[key]
return sd
@torch.no_grad()
def lowerCAmelCase (__A , __A , __A=None):
"""simple docstring"""
_a = load_checkpoint(__A)
if config is not None:
_a = OPTConfig.from_pretrained(__A)
else:
_a = OPTConfig()
_a = OPTModel(__A).half().eval()
model.load_state_dict(__A)
# Check results
Path(__A).mkdir(exist_ok=__A)
model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
lowercase_ = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 11 |
'''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
lowercase_ = {
# 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 :
'''simple docstring'''
def __init__(self , A = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError('''Unsupported Group''' )
_a = primes[group]['''prime''']
_a = primes[group]['''generator''']
_a = int(hexlify(urandom(32 ) ) , base=16 )
def a__ (self ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def a__ (self ) -> str:
"""simple docstring"""
_a = pow(self.generator , self.__private_key , self.prime )
return hex(A )[2:]
def a__ (self , A ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(A , (self.prime - 1) // 2 , self.prime ) == 1
)
def a__ (self , A ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
if not self.is_valid_public_key(A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , self.__private_key , self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def a__ (A , A ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(A , (prime - 1) // 2 , A ) == 1
)
@staticmethod
def a__ (A , A , A = 14 ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
_a = int(A , base=16 )
_a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(A , A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , A , A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCAmelCase (__A , __A=0):
"""simple docstring"""
return sorted(__A , key=lambda __A: x[column])
def lowerCAmelCase (__A , __A , __A=float('''inf''')):
"""simple docstring"""
for i in range(points_counts - 1):
for j in range(i + 1 , __A):
_a = euclidean_distance_sqr(points[i] , points[j])
if current_dis < min_dis:
_a = current_dis
return min_dis
def lowerCAmelCase (__A , __A , __A=float('''inf''')):
"""simple docstring"""
for i in range(min(6 , points_counts - 1) , __A):
for j in range(max(0 , i - 6) , __A):
_a = euclidean_distance_sqr(points[i] , points[j])
if current_dis < min_dis:
_a = current_dis
return min_dis
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if points_counts <= 3:
return dis_between_closest_pair(__A , __A)
# recursion
_a = points_counts // 2
_a = closest_pair_of_points_sqr(
__A , points_sorted_on_y[:mid] , __A)
_a = closest_pair_of_points_sqr(
__A , points_sorted_on_y[mid:] , points_counts - mid)
_a = min(__A , __A)
_a = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis:
cross_strip.append(__A)
_a = dis_between_closest_in_strip(
__A , len(__A) , __A)
return min(__A , __A)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = column_based_sort(__A , column=0)
_a = column_based_sort(__A , column=1)
return (
closest_pair_of_points_sqr(
__A , __A , __A)
) ** 0.5
if __name__ == "__main__":
lowercase_ = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("Distance:", closest_pair_of_points(points, len(points)))
| 11 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ = logging.getLogger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if os.path.exists(__A):
if os.path.exists(os.path.join(__A , '''config.json''')) and os.path.isfile(
os.path.join(__A , '''config.json''')):
os.remove(os.path.join(__A , '''config.json'''))
if os.path.exists(os.path.join(__A , '''pytorch_model.bin''')) and os.path.isfile(
os.path.join(__A , '''pytorch_model.bin''')):
os.remove(os.path.join(__A , '''pytorch_model.bin'''))
else:
os.makedirs(__A)
model.save_pretrained(__A)
def lowerCAmelCase (__A , __A=False):
"""simple docstring"""
_a = 2
if unlogit:
_a = torch.pow(__A , __A)
_a = p * torch.log(__A)
_a = 0
return -plogp.sum(dim=-1)
def lowerCAmelCase (__A):
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
for row in range(len(__A)):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
else:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))
def lowerCAmelCase (__A , __A , __A , __A=True , __A=True , __A=None , __A=False):
"""simple docstring"""
_a , _a = model.config.num_hidden_layers, model.config.num_attention_heads
_a = torch.zeros(__A , __A).to(args.device)
_a = torch.zeros(__A , __A).to(args.device)
if head_mask is None:
_a = torch.ones(__A , __A).to(args.device)
head_mask.requires_grad_(requires_grad=__A)
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_a = None
_a = 0.0
_a = 0.0
for step, inputs in enumerate(tqdm(__A , desc='''Iteration''' , disable=args.local_rank not in [-1, 0])):
_a = tuple(t.to(args.device) for t in inputs)
((_a) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_a = model(__A , labels=__A , head_mask=__A)
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_a , _a , _a = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__A):
_a = entropy(attn.detach() , __A)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__A).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_a = 2
_a = torch.pow(torch.pow(__A , __A).sum(-1) , 1 / exponent)
head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
if not args.dont_normalize_global_importance:
_a = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''')
print_ad_tensor(__A)
if compute_importance:
logger.info('''Head importance scores''')
print_ad_tensor(__A)
logger.info('''Head ranked by importance scores''')
_a = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device)
_a = torch.arange(
head_importance.numel() , device=args.device)
_a = head_ranks.view_as(__A)
print_ad_tensor(__A)
return attn_entropy, head_importance, total_loss
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a , _a , _a = compute_heads_importance(__A , __A , __A , compute_entropy=__A)
_a = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , __A , original_score * args.masking_threshold)
_a = torch.ones_like(__A)
_a = max(1 , int(new_head_mask.numel() * args.masking_amount))
_a = original_score
while current_score >= original_score * args.masking_threshold:
_a = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_a = float('''Inf''')
_a = head_importance.view(-1).sort()[1]
if len(__A) <= num_to_mask:
print('''BREAK BY num_to_mask''')
break
# mask heads
_a = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist()))
_a = new_head_mask.view(-1)
_a = 0.0
_a = new_head_mask.view_as(__A)
_a = new_head_mask.clone().detach()
print_ad_tensor(__A)
# Compute metric and head importance again
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , head_mask=__A)
_a = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''')
print_ad_tensor(__A)
np.save(os.path.join(args.output_dir , '''head_mask.npy''') , head_mask.detach().cpu().numpy())
return head_mask
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A)
_a = 1 / loss
_a = datetime.now() - before_time
_a = sum(p.numel() for p in model.parameters())
_a = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
}
for k, v in heads_to_prune.items():
if isinstance(__A , __A):
_a = [
v,
]
assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(__A)
_a = sum(p.numel() for p in model.parameters())
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A , actually_pruned=__A , )
_a = 1 / loss
_a = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , __A , __A , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , __A , __A)
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100)
save_model(__A , args.output_dir)
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=__A , type=__A , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=__A , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''')
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''')
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''')
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''')
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=__A , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=__A , help='''Amount to heads to masking at each masking step.''')
parser.add_argument('''--metric_name''' , default='''acc''' , type=__A , help='''Metric to use for head masking.''')
parser.add_argument(
'''--max_seq_length''' , default=128 , type=__A , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=__A , help='''Batch size.''')
parser.add_argument('''--seed''' , type=__A , default=42)
parser.add_argument('''--local_rank''' , type=__A , default=-1 , help='''local_rank for distributed training on gpus''')
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''')
parser.add_argument('''--server_ip''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
parser.add_argument('''--server_port''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
_a = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A)
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_a = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
_a = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
_a = torch.device('''cuda''' , args.local_rank)
_a = 1
torch.distributed.init_process_group(backend='''nccl''') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1)))
_a = GPTaLMHeadModel.from_pretrained(args.model_name_or_path)
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
_a = nn.parallel.DistributedDataParallel(
__A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__A)
elif args.n_gpu > 1:
_a = nn.DataParallel(__A)
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__A)
torch.save(__A , os.path.join(args.output_dir , '''run_args.bin'''))
logger.info('''Training/evaluation parameters %s''' , __A)
# Prepare dataset
_a = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa),
])
_a = (torch.from_numpy(__A),)
_a = TensorDataset(*__A)
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A , batch_size=args.batch_size)
# Compute head entropy and importance score
compute_heads_importance(__A , __A , __A)
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_a = mask_heads(__A , __A , __A)
prune_heads(__A , __A , __A , __A)
if __name__ == "__main__":
main()
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCAmelCase (__A):
"""simple docstring"""
if num <= 0:
_a = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(__A)
_a = [True] * (num + 1)
_a = []
_a = 2
_a = int(math.sqrt(__A))
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(__A)
# Set multiples of start be False
for i in range(start * start , num + 1 , __A):
if sieve[i] is True:
_a = False
start += 1
for j in range(end + 1 , num + 1):
if sieve[j] is True:
prime.append(__A)
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("Enter a positive integer: ").strip())))
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 1
for i in range(0 , len(__A)):
total *= numbers[i]
_a = str(__A)
steps += 1
return steps
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''additive_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 0
for i in range(0 , len(__A)):
total += numbers[i]
_a = str(__A)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class __A :
'''simple docstring'''
def __init__(self , A , A=2 , A=3 , A=4 , A=2 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=36 , A=3 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=6 , A=6 , A=3 , A=4 , A=None , A=1_000 , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = patch_size
_a = text_seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = coordinate_size
_a = shape_size
_a = num_labels
_a = num_choices
_a = scope
_a = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
_a = text_seq_length
_a = (image_size // patch_size) ** 2 + 1
_a = self.text_seq_length + self.image_seq_length
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
_a = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_a = bbox[i, j, 3]
_a = bbox[i, j, 1]
_a = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_a = bbox[i, j, 2]
_a = bbox[i, j, 0]
_a = t
_a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.text_seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
_a = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def a__ (self , A , A , A , A , A , A , A , A ) -> List[str]:
"""simple docstring"""
_a = LayoutLMvaModel(config=A )
model.to(A )
model.eval()
# text + image
_a = model(A , pixel_values=A )
_a = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A )
_a = model(A , bbox=A , pixel_values=A , token_type_ids=A )
_a = model(A , bbox=A , pixel_values=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
_a = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
_a = model(pixel_values=A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A ) -> Tuple:
"""simple docstring"""
_a = self.num_labels
_a = LayoutLMvaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a__ (self , A , A , A , A , A , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = self.num_labels
_a = LayoutLMvaForTokenClassification(config=A )
model.to(A )
model.eval()
_a = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def a__ (self , A , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = LayoutLMvaForQuestionAnswering(config=A )
model.to(A )
model.eval()
_a = model(
A , bbox=A , pixel_values=A , attention_mask=A , token_type_ids=A , start_positions=A , end_positions=A , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a__ (self ) -> int:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''pixel_values''': pixel_values,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : int = False
__lowerCamelCase : int = False
__lowerCamelCase : List[str] = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
__lowerCamelCase : List[str] = (
{'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
if is_torch_available()
else {}
)
def a__ (self , A , A , A , A , A ) -> Dict:
"""simple docstring"""
return True
def a__ (self ) -> Dict:
"""simple docstring"""
_a = LayoutLMvaModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self , A , A , A=False ) -> List[str]:
"""simple docstring"""
_a = copy.deepcopy(A )
if model_class in get_values(A ):
_a = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(A , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(A ):
_a = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in get_values(A ):
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in [
*get_values(A ),
]:
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A )
elif model_class in [
*get_values(A ),
]:
_a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=A , )
return inputs_dict
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
@slow
def a__ (self ) -> List[Any]:
"""simple docstring"""
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = LayoutLMvaModel.from_pretrained(A )
self.assertIsNotNone(A )
def lowerCAmelCase ():
"""simple docstring"""
_a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
return image
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=A ) if is_vision_available() else None
@slow
def a__ (self ) -> Any:
"""simple docstring"""
_a = LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' ).to(A )
_a = self.default_image_processor
_a = prepare_img()
_a = image_processor(images=A , return_tensors='''pt''' ).pixel_values.to(A )
_a = torch.tensor([[1, 2]] )
_a = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
_a = model(
input_ids=input_ids.to(A ) , bbox=bbox.to(A ) , pixel_values=pixel_values.to(A ) , )
# verify the logits
_a = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , A )
_a = torch.tensor(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] ).to(A )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , A , atol=1E-4 ) )
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_a = size if size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = DPTImageProcessor if is_vision_available() else None
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPTImageProcessingTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 11 | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = StableUnCLIPPipeline
__lowerCamelCase : Dict = TEXT_TO_IMAGE_PARAMS
__lowerCamelCase : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
__lowerCamelCase : List[str] = TEXT_TO_IMAGE_IMAGE_PARAMS
__lowerCamelCase : Optional[int] = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
__lowerCamelCase : List[str] = False
def a__ (self ) -> str:
"""simple docstring"""
_a = 32
_a = embedder_hidden_size
# prior components
torch.manual_seed(0 )
_a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_a = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=A , projection_dim=A , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
_a = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=A , num_layers=1 , )
torch.manual_seed(0 )
_a = DDPMScheduler(
variance_type='''fixed_small_log''' , prediction_type='''sample''' , num_train_timesteps=1_000 , clip_sample=A , clip_sample_range=5.0 , beta_schedule='''squaredcos_cap_v2''' , )
# regular denoising components
torch.manual_seed(0 )
_a = StableUnCLIPImageNormalizer(embedding_dim=A )
_a = DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' )
torch.manual_seed(0 )
_a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
torch.manual_seed(0 )
_a = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=A , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) )
torch.manual_seed(0 )
_a = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type='''projection''' , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=A , layers_per_block=1 , upcast_attention=A , use_linear_projection=A , )
torch.manual_seed(0 )
_a = DDIMScheduler(
beta_schedule='''scaled_linear''' , beta_start=0.00085 , beta_end=0.012 , prediction_type='''v_prediction''' , set_alpha_to_one=A , steps_offset=1 , )
torch.manual_seed(0 )
_a = AutoencoderKL()
_a = {
# prior components
'''prior_tokenizer''': prior_tokenizer,
'''prior_text_encoder''': prior_text_encoder,
'''prior''': prior,
'''prior_scheduler''': prior_scheduler,
# image noising components
'''image_normalizer''': image_normalizer,
'''image_noising_scheduler''': image_noising_scheduler,
# regular denoising components
'''tokenizer''': tokenizer,
'''text_encoder''': text_encoder,
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
}
return components
def a__ (self , A , A=0 ) -> Tuple:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''prior_num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = torch_device == '''cpu'''
self._test_attention_slicing_forward_pass(test_max_difference=A )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = torch_device in ['''cpu''', '''mps''']
self._test_inference_batch_single_identical(test_max_difference=A )
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> int:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''' )
_a = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_a = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a = pipe('''anime turle''' , generator=A , output_type='''np''' )
_a = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(A , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_a = StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' , torch_dtype=torch.floataa )
_a = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_a = pipe(
'''anime turtle''' , prior_num_inference_steps=2 , num_inference_steps=2 , output_type='''np''' , )
_a = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 11 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, 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
lowercase_ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __A :
'''simple docstring'''
def __init__(self , A , A=16 , A=13 , A=7 , A=14 , A=10 , A=19 , A=5 , A=4 , A=True , A=16 , A=2 , A=4 , A=4 , A="gelu" , A=0.1 , A=0.1 , A=[1, 2, 3, 4, 5] , A=25 , A=5 , ) -> List[str]:
"""simple docstring"""
_a = d_model
_a = parent
_a = batch_size
_a = prediction_length
_a = context_length
_a = cardinality
_a = num_time_features
_a = lags_sequence
_a = embedding_dimension
_a = is_training
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = context_length
_a = prediction_length + label_length
_a = label_length
_a = moving_average
_a = autocorrelation_factor
def a__ (self ) -> Any:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
_a = config.context_length + max(config.lags_sequence )
_a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, _past_length] )
_a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, config.prediction_length] )
_a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_config()
_a = self.prepare_autoformer_inputs_dict(A )
return config, inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModel(config=A ).to(A ).eval()
_a = model(**A )
_a = outputs.encoder_last_hidden_state
_a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_encoder()
encoder.save_pretrained(A )
_a = AutoformerEncoder.from_pretrained(A ).to(A )
_a , _a , _a , _a , _a = model.create_network_inputs(**A )
_a , _a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_a = encoder(inputs_embeds=A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
_a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_decoder()
decoder.save_pretrained(A )
_a = AutoformerDecoder.from_pretrained(A ).to(A )
_a = decoder(
trend=A , inputs_embeds=A , encoder_hidden_states=A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__lowerCamelCase : Tuple = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_a = model_class(A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a , _a = model_class.from_pretrained(A , output_loading_info=A )
self.assertEqual(info['''missing_keys'''] , [] )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*A )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = inspect.signature(getattr(A , '''forward''' ) )
# The main input is the name of the argument after `self`
_a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(A )] , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
_a = getattr(self.model_tester , '''seq_length''' , A )
_a = getattr(self.model_tester , '''decoder_seq_length''' , A )
_a = getattr(self.model_tester , '''encoder_seq_length''' , A )
_a = getattr(self.model_tester , '''d_model''' , A )
_a = getattr(self.model_tester , '''num_attention_heads''' , A )
_a = d_model // num_attention_heads
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_a = len(A )
_a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(A , A )
# decoder attentions
_a = outputs.decoder_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_a = outputs.cross_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
self.assertEqual(out_len + 2 , len(A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCAmelCase (__A="train-batch.pt"):
"""simple docstring"""
_a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__A , repo_type='''dataset''')
_a = torch.load(__A , map_location=__A)
return batch
@require_torch
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch()
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
_a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
_a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
_a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , A )
_a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=A )
_a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , A , rtol=1E-1 ) )
| 11 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = 'rwkv'
__lowerCamelCase : Optional[Any] = {'max_position_embeddings': 'context_length'}
def __init__(self , A=50_277 , A=1_024 , A=4_096 , A=32 , A=None , A=None , A=1E-5 , A=0 , A=0 , A=6 , A=False , A=True , **A , ) -> Union[str, Any]:
"""simple docstring"""
_a = vocab_size
_a = context_length
_a = hidden_size
_a = num_hidden_layers
_a = attention_hidden_size if attention_hidden_size is not None else hidden_size
_a = intermediate_size if intermediate_size is not None else 4 * hidden_size
_a = layer_norm_epsilon
_a = rescale_every
_a = use_cache
_a = bos_token_id
_a = eos_token_id
super().__init__(
tie_word_embeddings=A , bos_token_id=A , eos_token_id=A , **A )
| 11 |
'''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 :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=False , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> str:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , use_stable_embedding=A , )
def a__ (self , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = OpenLlamaModel(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A )
_a = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Any:
"""simple docstring"""
_a = True
_a = OpenLlamaModel(A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , )
_a = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Dict:
"""simple docstring"""
_a = True
_a = True
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_a = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_a = ids_tensor((self.batch_size, 3) , config.vocab_size )
_a = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_a = torch.cat([input_ids, next_tokens] , dim=-1 )
_a = torch.cat([input_mask, next_mask] , dim=-1 )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )['''hidden_states'''][0]
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )['''hidden_states'''][0]
# select random slice
_a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a = output_from_no_past[:, -3:, random_slice_idx].detach()
_a = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__lowerCamelCase : Any = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__lowerCamelCase : List[Any] = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''single_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''multi_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = ids_tensor([1, 10] , config.vocab_size )
_a = 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
_a = OpenLlamaModel(A )
original_model.to(A )
original_model.eval()
_a = original_model(A ).last_hidden_state
_a = original_model(A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = {'''type''': scaling_type, '''factor''': 10.0}
_a = OpenLlamaModel(A )
scaled_model.to(A )
scaled_model.eval()
_a = scaled_model(A ).last_hidden_state
_a = scaled_model(A ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
| 11 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowercase_ = logging.getLogger(__name__)
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A=None ) -> str:
"""simple docstring"""
super().__init__(
A , question_encoder_tokenizer=A , generator_tokenizer=A , index=A , init_retrieval=A , )
_a = None
def a__ (self , A ) -> Union[str, Any]:
"""simple docstring"""
logger.info('''initializing retrieval''' )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info('''dist initialized''' )
# needs to be set manually
_a = self._infer_socket_ifname()
# avoid clash with the NCCL port
_a = str(distributed_port + 1 )
_a = dist.new_group(ranks=A , backend='''gloo''' )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info('''dist not initialized / main''' )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def a__ (self , A , A , A=torch.floataa ) -> int:
"""simple docstring"""
_a = torch.empty(A , dtype=A )
dist.scatter(A , src=0 , scatter_list=A , group=self.process_group )
return target_tensor
def a__ (self ) -> Dict:
"""simple docstring"""
_a = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
_a = next((addr for addr in addrs if addr.startswith('''e''' )) , A )
return ifname
def a__ (self , A , A ) -> Tuple[np.ndarray, List[dict]]:
"""simple docstring"""
if not dist.is_initialized():
_a , _a = self._main_retrieve(A , A )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )
# distributed training
_a = dist.get_world_size(group=self.process_group )
# gather logic
_a = None
if self._is_main():
_a = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(A )]
dist.gather(torch.tensor(A ) , dst=0 , gather_list=A , group=self.process_group )
# scatter logic
_a = question_hidden_states.shape[0]
_a = []
_a = []
if self._is_main():
assert len(A ) == world_size
_a , _a = self._main_retrieve(torch.cat(A ).numpy() , A )
_a , _a = torch.tensor(A ), torch.tensor(A )
_a = self._chunk_tensor(A , A )
_a = self._chunk_tensor(A , A )
_a = self._scattered(A , [n_queries, n_docs] , target_type=torch.intaa )
_a = self._scattered(A , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(A )
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_choices
def a__ (self ) -> str:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxAlbertModelTester(self )
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''albert-base-v2''' )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Dict:
"""simple docstring"""
_a = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
_a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_a = model(A , attention_mask=A )[0]
_a = (1, 11, 768)
self.assertEqual(output.shape , A )
_a = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 11 | 1 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
lowercase_ = {
"vocab_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
},
}
lowercase_ = {
"vinai/phobert-base": 256,
"vinai/phobert-large": 256,
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = set()
_a = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
_a = char
_a = set(__A)
return pairs
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self , A , A , A="<s>" , A="</s>" , A="</s>" , A="<s>" , A="<unk>" , A="<pad>" , A="<mask>" , **A , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
bos_token=A , eos_token=A , unk_token=A , sep_token=A , cls_token=A , pad_token=A , mask_token=A , **A , )
_a = vocab_file
_a = merges_file
_a = {}
_a = 0
_a = 1
_a = 2
_a = 3
self.add_from_file(A )
_a = {v: k for k, v in self.encoder.items()}
with open(A , encoding='''utf-8''' ) as merges_handle:
_a = merges_handle.read().split('''\n''' )[:-1]
_a = [tuple(merge.split()[:-1] ) for merge in merges]
_a = dict(zip(A , range(len(A ) ) ) )
_a = {}
def a__ (self , A , A = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_a = [self.cls_token_id]
_a = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def a__ (self , A , A = None , A = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def a__ (self , A , A = None ) -> List[int]:
"""simple docstring"""
_a = [self.sep_token_id]
_a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return len(self.encoder )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
_a = tuple(A )
_a = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
_a = get_pairs(A )
if not pairs:
return token
while True:
_a = min(A , key=lambda A : self.bpe_ranks.get(A , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_a , _a = bigram
_a = []
_a = 0
while i < len(A ):
try:
_a = word.index(A , A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_a = j
if word[i] == first and i < len(A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_a = tuple(A )
_a = new_word
if len(A ) == 1:
break
else:
_a = get_pairs(A )
_a = '''@@ '''.join(A )
_a = word[:-4]
_a = word
return word
def a__ (self , A ) -> str:
"""simple docstring"""
_a = []
_a = re.findall(R'''\S+\n?''' , A )
for token in words:
split_tokens.extend(list(self.bpe(A ).split(''' ''' ) ) )
return split_tokens
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.encoder.get(A , self.encoder.get(self.unk_token ) )
def a__ (self , A ) -> Any:
"""simple docstring"""
return self.decoder.get(A , self.unk_token )
def a__ (self , A ) -> int:
"""simple docstring"""
_a = ''' '''.join(A ).replace('''@@ ''' , '''''' ).strip()
return out_string
def a__ (self , A , A = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_a = os.path.join(
A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_a = os.path.join(
A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
if os.path.abspath(self.merges_file ) != os.path.abspath(A ):
copyfile(self.merges_file , A )
return out_vocab_file, out_merge_file
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
if isinstance(A , A ):
try:
with open(A , '''r''' , encoding='''utf-8''' ) as fd:
self.add_from_file(A )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
return
_a = f.readlines()
for lineTmp in lines:
_a = lineTmp.strip()
_a = line.rfind(''' ''' )
if idx == -1:
raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' )
_a = line[:idx]
_a = len(self.encoder )
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a = 0
_a = len(__A) - 2
for i in range(__A , -1 , -2):
# double the value of every second digit
_a = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_a = cc_number[:i] + str(__A) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCAmelCase (__A):
"""simple docstring"""
_a = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''')
return False
if not 13 <= len(__A) <= 16:
print(F'''{error_message} of its length.''')
return False
if not validate_initial_digits(__A):
print(F'''{error_message} of its first two digits.''')
return False
if not luhn_validation(__A):
print(F'''{error_message} it fails the Luhn check.''')
return False
print(F'''{credit_card_number} is a valid credit card number.''')
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 11 | 1 |
'''simple docstring'''
from math import loga
def lowerCAmelCase (__A):
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''')
elif isinstance(__A , __A):
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()
| 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 | 1 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Tuple = 'linear'
__lowerCamelCase : Optional[Any] = 'cosine'
__lowerCamelCase : Optional[int] = 'cosine_with_restarts'
__lowerCamelCase : Optional[Any] = 'polynomial'
__lowerCamelCase : Optional[int] = 'constant'
__lowerCamelCase : Dict = 'constant_with_warmup'
__lowerCamelCase : Optional[int] = 'piecewise_constant'
def lowerCAmelCase (__A , __A = -1):
"""simple docstring"""
return LambdaLR(__A , lambda __A: 1 , last_epoch=__A)
def lowerCAmelCase (__A , __A , __A = -1):
"""simple docstring"""
def lr_lambda(__A):
if current_step < num_warmup_steps:
return float(__A) / float(max(1.0 , __A))
return 1.0
return LambdaLR(__A , __A , last_epoch=__A)
def lowerCAmelCase (__A , __A , __A = -1):
"""simple docstring"""
_a = {}
_a = step_rules.split(''',''')
for rule_str in rule_list[:-1]:
_a , _a = rule_str.split(''':''')
_a = int(__A)
_a = float(__A)
_a = value
_a = float(rule_list[-1])
def create_rules_function(__A , __A):
def rule_func(__A) -> float:
_a = sorted(rules_dict.keys())
for i, sorted_step in enumerate(__A):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
_a = create_rules_function(__A , __A)
return LambdaLR(__A , __A , last_epoch=__A)
def lowerCAmelCase (__A , __A , __A , __A=-1):
"""simple docstring"""
def lr_lambda(__A):
if current_step < num_warmup_steps:
return float(__A) / float(max(1 , __A))
return max(
0.0 , float(num_training_steps - current_step) / float(max(1 , num_training_steps - num_warmup_steps)))
return LambdaLR(__A , __A , __A)
def lowerCAmelCase (__A , __A , __A , __A = 0.5 , __A = -1):
"""simple docstring"""
def lr_lambda(__A):
if current_step < num_warmup_steps:
return float(__A) / float(max(1 , __A))
_a = float(current_step - num_warmup_steps) / float(max(1 , num_training_steps - num_warmup_steps))
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(__A) * 2.0 * progress)))
return LambdaLR(__A , __A , __A)
def lowerCAmelCase (__A , __A , __A , __A = 1 , __A = -1):
"""simple docstring"""
def lr_lambda(__A):
if current_step < num_warmup_steps:
return float(__A) / float(max(1 , __A))
_a = float(current_step - num_warmup_steps) / float(max(1 , num_training_steps - num_warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(__A) * progress) % 1.0))))
return LambdaLR(__A , __A , __A)
def lowerCAmelCase (__A , __A , __A , __A=1e-7 , __A=1.0 , __A=-1):
"""simple docstring"""
_a = optimizer.defaults['''lr''']
if not (lr_init > lr_end):
raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''')
def lr_lambda(__A):
if current_step < num_warmup_steps:
return float(__A) / float(max(1 , __A))
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
_a = lr_init - lr_end
_a = num_training_steps - num_warmup_steps
_a = 1 - (current_step - num_warmup_steps) / decay_steps
_a = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(__A , __A , __A)
lowercase_ = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCAmelCase (__A , __A , __A = None , __A = None , __A = None , __A = 1 , __A = 1.0 , __A = -1 , ):
"""simple docstring"""
_a = SchedulerType(__A)
_a = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(__A , last_epoch=__A)
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(__A , step_rules=__A , last_epoch=__A)
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.''')
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(__A , num_warmup_steps=__A , last_epoch=__A)
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.''')
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
__A , num_warmup_steps=__A , num_training_steps=__A , num_cycles=__A , last_epoch=__A , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
__A , num_warmup_steps=__A , num_training_steps=__A , power=__A , last_epoch=__A , )
return schedule_func(
__A , num_warmup_steps=__A , num_training_steps=__A , last_epoch=__A)
| 11 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase (__A = "laptop"):
"""simple docstring"""
_a = F'''https://www.amazon.in/laptop/s?k={product}'''
_a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
_a = BeautifulSoup(requests.get(__A , headers=__A).text)
# Initialize a Pandas dataframe with the column titles
_a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
_a = item.ha.text
_a = '''https://www.amazon.in/''' + item.ha.a['''href''']
_a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
_a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
_a = '''Not available'''
try:
_a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
_a = ''''''
try:
_a = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
_a = float('''nan''')
except AttributeError:
pass
_a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_a = ''' '''
_a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 11 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ = logging.getLogger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if os.path.exists(__A):
if os.path.exists(os.path.join(__A , '''config.json''')) and os.path.isfile(
os.path.join(__A , '''config.json''')):
os.remove(os.path.join(__A , '''config.json'''))
if os.path.exists(os.path.join(__A , '''pytorch_model.bin''')) and os.path.isfile(
os.path.join(__A , '''pytorch_model.bin''')):
os.remove(os.path.join(__A , '''pytorch_model.bin'''))
else:
os.makedirs(__A)
model.save_pretrained(__A)
def lowerCAmelCase (__A , __A=False):
"""simple docstring"""
_a = 2
if unlogit:
_a = torch.pow(__A , __A)
_a = p * torch.log(__A)
_a = 0
return -plogp.sum(dim=-1)
def lowerCAmelCase (__A):
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
for row in range(len(__A)):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
else:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))
def lowerCAmelCase (__A , __A , __A , __A=True , __A=True , __A=None , __A=False):
"""simple docstring"""
_a , _a = model.config.num_hidden_layers, model.config.num_attention_heads
_a = torch.zeros(__A , __A).to(args.device)
_a = torch.zeros(__A , __A).to(args.device)
if head_mask is None:
_a = torch.ones(__A , __A).to(args.device)
head_mask.requires_grad_(requires_grad=__A)
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_a = None
_a = 0.0
_a = 0.0
for step, inputs in enumerate(tqdm(__A , desc='''Iteration''' , disable=args.local_rank not in [-1, 0])):
_a = tuple(t.to(args.device) for t in inputs)
((_a) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_a = model(__A , labels=__A , head_mask=__A)
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_a , _a , _a = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__A):
_a = entropy(attn.detach() , __A)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__A).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_a = 2
_a = torch.pow(torch.pow(__A , __A).sum(-1) , 1 / exponent)
head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
if not args.dont_normalize_global_importance:
_a = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''')
print_ad_tensor(__A)
if compute_importance:
logger.info('''Head importance scores''')
print_ad_tensor(__A)
logger.info('''Head ranked by importance scores''')
_a = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device)
_a = torch.arange(
head_importance.numel() , device=args.device)
_a = head_ranks.view_as(__A)
print_ad_tensor(__A)
return attn_entropy, head_importance, total_loss
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a , _a , _a = compute_heads_importance(__A , __A , __A , compute_entropy=__A)
_a = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , __A , original_score * args.masking_threshold)
_a = torch.ones_like(__A)
_a = max(1 , int(new_head_mask.numel() * args.masking_amount))
_a = original_score
while current_score >= original_score * args.masking_threshold:
_a = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_a = float('''Inf''')
_a = head_importance.view(-1).sort()[1]
if len(__A) <= num_to_mask:
print('''BREAK BY num_to_mask''')
break
# mask heads
_a = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist()))
_a = new_head_mask.view(-1)
_a = 0.0
_a = new_head_mask.view_as(__A)
_a = new_head_mask.clone().detach()
print_ad_tensor(__A)
# Compute metric and head importance again
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , head_mask=__A)
_a = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''')
print_ad_tensor(__A)
np.save(os.path.join(args.output_dir , '''head_mask.npy''') , head_mask.detach().cpu().numpy())
return head_mask
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A)
_a = 1 / loss
_a = datetime.now() - before_time
_a = sum(p.numel() for p in model.parameters())
_a = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
}
for k, v in heads_to_prune.items():
if isinstance(__A , __A):
_a = [
v,
]
assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(__A)
_a = sum(p.numel() for p in model.parameters())
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A , actually_pruned=__A , )
_a = 1 / loss
_a = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , __A , __A , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , __A , __A)
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100)
save_model(__A , args.output_dir)
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=__A , type=__A , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=__A , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''')
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''')
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''')
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''')
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=__A , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=__A , help='''Amount to heads to masking at each masking step.''')
parser.add_argument('''--metric_name''' , default='''acc''' , type=__A , help='''Metric to use for head masking.''')
parser.add_argument(
'''--max_seq_length''' , default=128 , type=__A , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=__A , help='''Batch size.''')
parser.add_argument('''--seed''' , type=__A , default=42)
parser.add_argument('''--local_rank''' , type=__A , default=-1 , help='''local_rank for distributed training on gpus''')
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''')
parser.add_argument('''--server_ip''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
parser.add_argument('''--server_port''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
_a = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A)
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_a = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
_a = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
_a = torch.device('''cuda''' , args.local_rank)
_a = 1
torch.distributed.init_process_group(backend='''nccl''') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1)))
_a = GPTaLMHeadModel.from_pretrained(args.model_name_or_path)
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
_a = nn.parallel.DistributedDataParallel(
__A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__A)
elif args.n_gpu > 1:
_a = nn.DataParallel(__A)
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__A)
torch.save(__A , os.path.join(args.output_dir , '''run_args.bin'''))
logger.info('''Training/evaluation parameters %s''' , __A)
# Prepare dataset
_a = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa),
])
_a = (torch.from_numpy(__A),)
_a = TensorDataset(*__A)
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A , batch_size=args.batch_size)
# Compute head entropy and importance score
compute_heads_importance(__A , __A , __A)
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_a = mask_heads(__A , __A , __A)
prune_heads(__A , __A , __A , __A)
if __name__ == "__main__":
main()
| 11 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if isinstance(__A , torch.Tensor):
return image
elif isinstance(__A , PIL.Image.Image):
_a = [image]
if isinstance(image[0] , PIL.Image.Image):
_a = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
_a = np.concatenate(__A , axis=0)
_a = np.array(__A).astype(np.floataa) / 2_55.0
_a = image.transpose(0 , 3 , 1 , 2)
_a = 2.0 * image - 1.0
_a = torch.from_numpy(__A)
elif isinstance(image[0] , torch.Tensor):
_a = torch.cat(__A , dim=0)
return image
def lowerCAmelCase (__A , __A , __A , __A=0.99_95):
"""simple docstring"""
if not isinstance(__A , np.ndarray):
_a = True
_a = va.device
_a = va.cpu().numpy()
_a = va.cpu().numpy()
_a = np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
if np.abs(__A) > DOT_THRESHOLD:
_a = (1 - t) * va + t * va
else:
_a = np.arccos(__A)
_a = np.sin(__A)
_a = theta_a * t
_a = np.sin(__A)
_a = np.sin(theta_a - theta_t) / sin_theta_a
_a = sin_theta_t / sin_theta_a
_a = sa * va + sa * va
if inputs_are_torch:
_a = torch.from_numpy(__A).to(__A)
return va
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = F.normalize(__A , dim=-1)
_a = F.normalize(__A , dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for param in model.parameters():
_a = value
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
_a = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size['''shortest_edge''']
)
_a = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def a__ (self , A = "auto" ) -> Union[str, Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(A )
def a__ (self ) -> int:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self ) -> str:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = min(int(num_inference_steps * strength ) , A )
_a = max(num_inference_steps - init_timestep , 0 )
_a = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a__ (self , A , A , A , A , A , A=None ) -> List[str]:
"""simple docstring"""
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
_a = image.to(device=A , dtype=A )
if isinstance(A , A ):
_a = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
_a = torch.cat(A , dim=0 )
else:
_a = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 0.18215 * init_latents
_a = init_latents.repeat_interleave(A , dim=0 )
_a = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
_a = self.scheduler.add_noise(A , A , A )
_a = init_latents
return latents
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_a = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_a = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.feature_extractor.preprocess(A )
_a = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a__ (self , A , A , A , A , A , A , A , ) -> Union[str, Any]:
"""simple docstring"""
_a = latents.detach().requires_grad_()
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_a = self.scheduler.alphas_cumprod[timestep]
_a = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_a = torch.sqrt(A )
_a = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
_a = self.scheduler.sigmas[index]
_a = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * sample
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = transforms.Resize(self.feature_extractor_size )(A )
_a = self.normalize(A ).to(latents.dtype )
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
_a = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
_a = latents.detach() + grads * (sigma**2)
_a = noise_pred_original
else:
_a = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> str:
"""simple docstring"""
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
_a = [generator] + [None] * (batch_size - 1)
_a = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_a = [x[0] for x in coca_is_none if x[1]]
_a = ''', '''.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
# get prompt text embeddings for content and style
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_a = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
_a = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
_a = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_a = {}
if accepts_offset:
_a = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_a , _a = self.get_timesteps(A , A , self.device )
_a = timesteps[:1].repeat(A )
# Preprocess image
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = slerp(A , A , A )
if clip_guidance_scale > 0:
_a = self.get_clip_image_embeddings(A , A )
_a = self.get_clip_image_embeddings(A , A )
_a = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_a = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_a = content_text_input.input_ids.shape[-1]
_a = self.tokenizer([''''''] , padding='''max_length''' , max_length=A , return_tensors='''pt''' )
_a = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_a = uncond_embeddings.repeat_interleave(A , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_a = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_a = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_a = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_a = torch.randn(A , generator=A , device='''cpu''' , dtype=A ).to(
self.device )
else:
_a = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
# check if the scheduler accepts generator
_a = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_a = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_a = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_a , _a = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * latents
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 11 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> Optional[int]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_choices
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = True
_a = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = True
__lowerCamelCase : int = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxRobertaModelTester(self )
@slow
def a__ (self ) -> Tuple:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''roberta-base''' , from_pt=A )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
| 11 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = CTRLTokenizer
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Any = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = 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(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = '''adapt react readapt apt'''
_a = '''adapt react readapt apt'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt react readapt apt'''
_a = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
| 11 | 1 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class __A :
'''simple docstring'''
def __init__(self , A=None , A=None ) -> Any:
"""simple docstring"""
_a = list(poly_a or [0] )[:]
_a = list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
_a = len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
_a = len(self.polyB )
# Add 0 to make lengths equal a power of 2
_a = int(
2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )
while len(self.polyA ) < self.c_max_length:
self.polyA.append(0 )
while len(self.polyB ) < self.c_max_length:
self.polyB.append(0 )
# A complex root used for the fourier transform
_a = complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
_a = self.__multiply()
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
# Corner case
if len(A ) <= 1:
return dft[0]
#
_a = self.c_max_length // 2
while next_ncol > 0:
_a = [[] for i in range(A )]
_a = self.root**next_ncol
# First half of next step
_a = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(A ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
_a = 1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(A ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
_a = new_dft
_a = next_ncol // 2
return dft[0]
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.__dft('''A''' )
_a = self.__dft('''B''' )
_a = [[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
del dft_a
del dft_b
# Corner Case
if len(inverce_c[0] ) <= 1:
return inverce_c[0]
# Inverse DFT
_a = 2
while next_ncol <= self.c_max_length:
_a = [[] for i in range(A )]
_a = self.root ** (next_ncol // 2)
_a = 1
# First half of next step
for j in range(self.c_max_length // next_ncol ):
for i in range(next_ncol // 2 ):
# Even positions
new_inverse_c[i].append(
(
inverce_c[i][j]
+ inverce_c[i][j + self.c_max_length // next_ncol]
)
/ 2 )
# Odd positions
new_inverse_c[i + next_ncol // 2].append(
(
inverce_c[i][j]
- inverce_c[i][j + self.c_max_length // next_ncol]
)
/ (2 * current_root) )
current_root *= root
# Update
_a = new_inverse_c
next_ncol *= 2
# Unpack
_a = [round(x[0].real , 8 ) + round(x[0].imag , 8 ) * 1J for x in inverce_c]
# Remove leading 0's
while inverce_c[-1] == 0:
inverce_c.pop()
return inverce_c
def __str__(self ) -> Union[str, Any]:
"""simple docstring"""
_a = '''A = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A] ) )
_a = '''B = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B] ) )
_a = '''A*B = ''' + ''' + '''.join(
f'''{coef}*x^{i}''' for coef, i in enumerate(self.product ) )
return f'''{a}\n{b}\n{c}'''
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=32 , A=3 , A=4 , A=[10, 20, 30, 40] , A=[2, 2, 3, 2] , A=True , A=True , A=37 , A="gelu" , A=10 , A=0.02 , A=["stage2", "stage3", "stage4"] , A=3 , A=None , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = image_size
_a = num_channels
_a = num_stages
_a = hidden_sizes
_a = depths
_a = is_training
_a = use_labels
_a = intermediate_size
_a = hidden_act
_a = type_sequence_label_size
_a = initializer_range
_a = out_features
_a = num_labels
_a = scope
_a = num_stages
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = self.get_config()
return config, pixel_values, labels
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=512 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=A , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=256 , auxiliary_num_convs=1 , auxiliary_concat_input=A , loss_ignore_index=255 , num_labels=self.num_labels , )
def a__ (self , A , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = UperNetForSemanticSegmentation(config=A )
model.to(A )
model.eval()
_a = model(A )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def a__ (self ) -> int:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Tuple = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
__lowerCamelCase : Any = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
__lowerCamelCase : List[Any] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : int = False
__lowerCamelCase : str = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : int = False
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = UperNetModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A , hidden_size=37 )
def a__ (self ) -> List[Any]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def a__ (self ) -> List[str]:
"""simple docstring"""
return
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A )
@unittest.skip(reason='''UperNet does not use inputs_embeds''' )
def a__ (self ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''' )
def a__ (self ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason='''UperNet does not have a base model''' )
def a__ (self ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason='''UperNet does not have a base model''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def a__ (self ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def a__ (self ) -> Any:
"""simple docstring"""
pass
def a__ (self ) -> str:
"""simple docstring"""
def check_hidden_states_output(A , A , A ):
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_a = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a = True
check_hidden_states_output(A , A , A )
def a__ (self ) -> str:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = _config_zero_init(A )
_a = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
_a = model_class(config=A )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='''UperNet does not have tied weights''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = UperNetForSemanticSegmentation.from_pretrained(A )
self.assertIsNotNone(A )
def lowerCAmelCase ():
"""simple docstring"""
_a = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''' , repo_type='''dataset''' , filename='''ADE_val_00000001.jpg''')
_a = Image.open(__A).convert('''RGB''')
return image
@require_torch
@require_vision
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''' )
_a = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''' ).to(A )
_a = prepare_img()
_a = processor(images=A , return_tensors='''pt''' ).to(A )
with torch.no_grad():
_a = model(**A )
_a = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , A )
_a = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , A , atol=1E-4 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''' )
_a = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''' ).to(A )
_a = prepare_img()
_a = processor(images=A , return_tensors='''pt''' ).to(A )
with torch.no_grad():
_a = model(**A )
_a = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , A )
_a = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , A , atol=1E-4 ) )
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> str:
"""simple docstring"""
_a = tempfile.mkdtemp()
_a = BlipImageProcessor()
_a = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
_a = BlipaProcessor(A , A )
processor.save_pretrained(self.tmpdirname )
def a__ (self , **A ) -> Union[str, Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **A ).tokenizer
def a__ (self , **A ) -> Union[str, Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **A ).image_processor
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_a = [Image.fromarray(np.moveaxis(A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
_a = self.get_image_processor(do_normalize=A , padding_value=1.0 )
_a = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=A , image_processor=A )
_a = self.prepare_image_inputs()
_a = image_processor(A , return_tensors='''np''' )
_a = processor(images=A , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def a__ (self ) -> int:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=A , image_processor=A )
_a = '''lower newer'''
_a = processor(text=A )
_a = tokenizer(A , return_token_type_ids=A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def a__ (self ) -> int:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=A , image_processor=A )
_a = '''lower newer'''
_a = self.prepare_image_inputs()
_a = processor(text=A , images=A )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
# test if it raises when no input is passed
with pytest.raises(A ):
processor()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=A , image_processor=A )
_a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_a = processor.batch_decode(A )
_a = tokenizer.batch_decode(A )
self.assertListEqual(A , A )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_image_processor()
_a = self.get_tokenizer()
_a = BlipaProcessor(tokenizer=A , image_processor=A )
_a = '''lower newer'''
_a = self.prepare_image_inputs()
_a = processor(text=A , images=A )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''input_ids''', '''attention_mask'''] )
| 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowercase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_ = 10
lowercase_ = 256
def lowerCAmelCase (__A):
"""simple docstring"""
if len(__A) < MIN_NUM_TOKENS:
return None
_a = MinHash(num_perm=__A)
for token in set(__A):
min_hash.update(token.encode())
return min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}
class __A :
'''simple docstring'''
def __init__(self , *,
A = 0.85 , ) -> Optional[int]:
"""simple docstring"""
_a = duplication_jaccard_threshold
_a = NUM_PERM
_a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_a = defaultdict(A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
_a = self._index.query(A )
if code_key in self._index.keys:
print(f'''Duplicate key {code_key}''' )
return
self._index.insert(A , A )
if len(A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A )
def a__ (self ) -> List[List[Dict]]:
"""simple docstring"""
_a = []
for base, duplicates in self._duplicate_clusters.items():
_a = [base] + list(A )
# reformat the cluster to be a list of dict
_a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(A )
return duplicate_clusters
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_duplicate_clusters()
with open(A , '''w''' ) as f:
json.dump(A , A )
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = element
_a = get_min_hash([t for t in NON_ALPHA.split(data['''content''']) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__A , max_queue_size=10_000) , chunksize=100 , ):
if data is not None:
yield data
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = DuplicationIndex(duplication_jaccard_threshold=__A)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A)) , max_queue_size=100)):
di.add(__A , __A)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = get_tokens(__A)
_a = get_tokens(__A)
return len(tokensa & tokensa) / len(tokensa | tokensa)
lowercase_ = None
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for elementa in cluster:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__A , __A) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_a = 1
extremes.append(__A)
return extremes
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
global _shared_dataset
_a = dataset
_a = []
_a = partial(_find_cluster_extremes_shared , jaccard_threshold=__A)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__A , __A , ) , total=len(__A) , ):
extremes_list.append(__A)
return extremes_list
def lowerCAmelCase (__A , __A = 0.85):
"""simple docstring"""
_a = make_duplicate_clusters(__A , __A)
_a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_a = {}
_a = find_extremes(__A , __A , __A)
for extremes in extremes_clusters:
for element in extremes:
_a = element
_a = duplicate_indices - set(extreme_dict.keys())
_a = dataset.filter(lambda __A , __A: idx not in remove_indices , with_indices=__A)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_a = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(__A)}''')
print(F'''Number of duplicate clusters: {len(__A)}''')
print(F'''Files in duplicate cluster: {len(__A)}''')
print(F'''Unique files in duplicate cluster: {len(__A)}''')
print(F'''Filtered dataset size: {len(__A)}''')
return ds_filter, duplicate_clusters
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return 10 - x * x
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if equation(__A) * equation(__A) >= 0:
raise ValueError('''Wrong space!''')
_a = a
while (b - a) >= 0.01:
# Find middle point
_a = (a + b) / 2
# Check if middle point is root
if equation(__A) == 0.0:
break
# Decide the side to repeat the steps
if equation(__A) * equation(__A) < 0:
_a = c
else:
_a = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 11 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
lowercase_ = ["gpt2"]
lowercase_ = "gpt2"
if is_tf_available():
class __A ( tf.Module ):
'''simple docstring'''
def __init__(self , A ) -> List[Any]:
"""simple docstring"""
super().__init__()
_a = tokenizer
_a = AutoConfig.from_pretrained(A )
_a = TFGPTaLMHeadModel.from_config(A )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name='''text''' ),) )
def a__ (self , A ) -> Optional[Any]:
"""simple docstring"""
_a = self.tokenizer(A )
_a = tokenized['''input_ids'''].to_tensor()
_a = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
_a = self.model(input_ids=A , attention_mask=A )['''logits''']
return outputs
@require_tf
@require_keras_nlp
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Any:
"""simple docstring"""
super().setUp()
_a = [GPTaTokenizer.from_pretrained(A ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
_a = [TFGPTaTokenizer.from_pretrained(A ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
_a = [
'''This is a straightforward English test sentence.''',
'''This one has some weird characters\rto\nsee\r\nif those\u00E9break things.''',
'''Now we\'re going to add some Chinese: 一 二 三 一二三''',
'''And some much more rare Chinese: 齉 堃 齉堃''',
'''Je vais aussi écrire en français pour tester les accents''',
'''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
]
_a = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def a__ (self ) -> Any:
"""simple docstring"""
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
_a = tokenizer([test_inputs] , return_tensors='''tf''' )
_a = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
_a = python_outputs[key].numpy()
_a = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(A , tf.intaa ) == tf_outputs_values ) )
@slow
def a__ (self ) -> int:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
_a = tf.function(A )
for test_inputs in self.test_sentences:
_a = tf.constant(A )
_a = compiled_tokenizer(A )
_a = tf_tokenizer(A )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def a__ (self ) -> Any:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
_a = ModelToSave(tokenizer=A )
_a = tf.convert_to_tensor([self.test_sentences[0]] )
_a = model.serving(A ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
_a = Path(A ) / '''saved.model'''
tf.saved_model.save(A , A , signatures={'''serving_default''': model.serving} )
_a = tf.saved_model.load(A )
_a = loaded_model.signatures['''serving_default'''](A )['''output_0''']
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def a__ (self ) -> List[Any]:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
_a = tf.convert_to_tensor([self.test_sentences[0]] )
_a = tf_tokenizer(A ) # Build model with some sample inputs
_a = tf_tokenizer.get_config()
_a = TFGPTaTokenizer.from_config(A )
_a = model_from_config(A )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def a__ (self ) -> Any:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
_a = 123_123
for max_length in [3, 5, 1_024]:
_a = tf.convert_to_tensor([self.test_sentences[0]] )
_a = tf_tokenizer(A , max_length=A )
_a = out['''input_ids'''].numpy().shape[1]
assert out_length == max_length
| 11 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = IFInpaintingSuperResolutionPipeline
__lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
__lowerCamelCase : str = PipelineTesterMixin.required_optional_params - {'latents'}
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def a__ (self , A , A=0 ) -> List[Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = floats_tensor((1, 3, 16, 16) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ (self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def a__ (self ) -> str:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._test_save_load_local()
def a__ (self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 11 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __A ( unittest.TestCase ):
'''simple docstring'''
@property
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
_a = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def a__ (self ) -> str:
"""simple docstring"""
_a = self.dummy_uncond_unet
_a = KarrasVeScheduler()
_a = KarrasVePipeline(unet=A , scheduler=A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_a = torch.manual_seed(0 )
_a = pipe(num_inference_steps=2 , generator=A , output_type='''numpy''' ).images
_a = torch.manual_seed(0 )
_a = pipe(num_inference_steps=2 , generator=A , output_type='''numpy''' , return_dict=A )[0]
_a = image[0, -3:, -3:, -1]
_a = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_a = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> str:
"""simple docstring"""
_a = '''google/ncsnpp-celebahq-256'''
_a = UNetaDModel.from_pretrained(A )
_a = KarrasVeScheduler()
_a = KarrasVePipeline(unet=A , scheduler=A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_a = torch.manual_seed(0 )
_a = pipe(num_inference_steps=20 , generator=A , output_type='''numpy''' ).images
_a = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_a = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 11 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str = 'wav2vec2'
def __init__(self , A=32 , A=768 , A=12 , A=12 , A=3_072 , A="gelu" , A=0.1 , A=0.1 , A=0.1 , A=0.0 , A=0.0 , A=0.1 , A=0.1 , A=0.02 , A=1E-5 , A="group" , A="gelu" , A=(512, 512, 512, 512, 512, 512, 512) , A=(5, 2, 2, 2, 2, 2, 2) , A=(10, 3, 3, 3, 3, 2, 2) , A=False , A=128 , A=16 , A=False , A=True , A=0.05 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=320 , A=2 , A=0.1 , A=100 , A=256 , A=256 , A=0.1 , A="sum" , A=False , A=False , A=256 , A=(512, 512, 512, 512, 1_500) , A=(5, 3, 3, 1, 1) , A=(1, 2, 3, 1, 1) , A=512 , A=0 , A=1 , A=2 , A=False , A=3 , A=2 , A=3 , A=None , A=None , **A , ) -> int:
"""simple docstring"""
super().__init__(**A , pad_token_id=A , bos_token_id=A , eos_token_id=A )
_a = hidden_size
_a = feat_extract_norm
_a = feat_extract_activation
_a = list(A )
_a = list(A )
_a = list(A )
_a = conv_bias
_a = num_conv_pos_embeddings
_a = num_conv_pos_embedding_groups
_a = len(self.conv_dim )
_a = num_hidden_layers
_a = intermediate_size
_a = hidden_act
_a = num_attention_heads
_a = hidden_dropout
_a = attention_dropout
_a = activation_dropout
_a = feat_proj_dropout
_a = final_dropout
_a = layerdrop
_a = layer_norm_eps
_a = initializer_range
_a = vocab_size
_a = do_stable_layer_norm
_a = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_a = apply_spec_augment
_a = mask_time_prob
_a = mask_time_length
_a = mask_time_min_masks
_a = mask_feature_prob
_a = mask_feature_length
_a = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_a = num_codevectors_per_group
_a = num_codevector_groups
_a = contrastive_logits_temperature
_a = feat_quantizer_dropout
_a = num_negatives
_a = codevector_dim
_a = proj_codevector_dim
_a = diversity_loss_weight
# ctc loss
_a = ctc_loss_reduction
_a = ctc_zero_infinity
# adapter
_a = add_adapter
_a = adapter_kernel_size
_a = adapter_stride
_a = num_adapter_layers
_a = output_hidden_size or hidden_size
_a = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_a = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_a = list(A )
_a = list(A )
_a = list(A )
_a = xvector_output_dim
@property
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return len(set(__A)) == len(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A = 600_851_475_143):
"""simple docstring"""
try:
_a = int(__A)
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''')
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''')
_a = 2
_a = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
_a = i
while n % i == 0:
_a = n // i
i += 1
return int(__A)
if __name__ == "__main__":
print(F"""{solution() = }""")
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) == 0:
return False
_a = len(__A) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __A)
else:
return binary_search(a_list[midpoint + 1 :] , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
lowercase_ = int(input("Enter the number to be found in the list:\n").strip())
lowercase_ = "" if binary_search(sequence, target) else "not "
print(F"""{target} was {not_str}found in {sequence}""")
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowercase_ = 1.0_54_57_18_17e-34 # unit of ℏ : J * s
lowercase_ = 3e8 # unit of c : m * s^-1
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if (force, area, distance).count(0) != 1:
raise ValueError('''One and only one argument must be 0''')
if force < 0:
raise ValueError('''Magnitude of force can not be negative''')
if distance < 0:
raise ValueError('''Distance can not be negative''')
if area < 0:
raise ValueError('''Area can not be negative''')
if force == 0:
_a = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
_a = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
_a = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError('''One and only one argument must be 0''')
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A ):
_a = self.prefix_sum[i - 1] + array[i]
def a__ (self , A , A ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
"google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
"google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str = 'owlvit_text_model'
def __init__(self , A=49_408 , A=512 , A=2_048 , A=12 , A=8 , A=16 , A="quick_gelu" , A=1E-5 , A=0.0 , A=0.02 , A=1.0 , A=0 , A=49_406 , A=49_407 , **A , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A )
_a = vocab_size
_a = hidden_size
_a = intermediate_size
_a = num_hidden_layers
_a = num_attention_heads
_a = max_position_embeddings
_a = hidden_act
_a = layer_norm_eps
_a = attention_dropout
_a = initializer_range
_a = initializer_factor
@classmethod
def a__ (cls , A , **A ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(A )
_a , _a = cls.get_config_dict(A , **A )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
_a = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Dict = 'owlvit_vision_model'
def __init__(self , A=768 , A=3_072 , A=12 , A=12 , A=3 , A=768 , A=32 , A="quick_gelu" , A=1E-5 , A=0.0 , A=0.02 , A=1.0 , **A , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**A )
_a = hidden_size
_a = intermediate_size
_a = num_hidden_layers
_a = num_attention_heads
_a = num_channels
_a = image_size
_a = patch_size
_a = hidden_act
_a = layer_norm_eps
_a = attention_dropout
_a = initializer_range
_a = initializer_factor
@classmethod
def a__ (cls , A , **A ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(A )
_a , _a = cls.get_config_dict(A , **A )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
_a = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = 'owlvit'
__lowerCamelCase : List[str] = True
def __init__(self , A=None , A=None , A=512 , A=2.6592 , A=True , **A , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**A )
if text_config is None:
_a = {}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
_a = {}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
_a = OwlViTTextConfig(**A )
_a = OwlViTVisionConfig(**A )
_a = projection_dim
_a = logit_scale_init_value
_a = return_dict
_a = 1.0
@classmethod
def a__ (cls , A , **A ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(A )
_a , _a = cls.get_config_dict(A , **A )
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A , **A )
@classmethod
def a__ (cls , A , A , **A ) -> Any:
"""simple docstring"""
_a = {}
_a = text_config
_a = vision_config
return cls.from_dict(A , **A )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = copy.deepcopy(self.__dict__ )
_a = self.text_config.to_dict()
_a = self.vision_config.to_dict()
_a = self.__class__.model_type
return output
class __A ( A ):
'''simple docstring'''
@property
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def a__ (self ) -> float:
"""simple docstring"""
return 1E-4
def a__ (self , A , A = -1 , A = -1 , A = None , ) -> Mapping[str, Any]:
"""simple docstring"""
_a = super().generate_dummy_inputs(
processor.tokenizer , batch_size=A , seq_length=A , framework=A )
_a = super().generate_dummy_inputs(
processor.image_processor , batch_size=A , framework=A )
return {**text_input_dict, **image_input_dict}
@property
def a__ (self ) -> int:
"""simple docstring"""
return 14
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A)
if n > 1:
factors.append(__A)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase (__A , __A , __A , __A , __A = None , __A = None , __A = None , ):
"""simple docstring"""
if config_name_or_path is None:
_a = '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''
if generator_tokenizer_name_or_path is None:
_a = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
_a = question_encoder_name_or_path
_a = RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration
# Save model.
_a = RagConfig.from_pretrained(__A)
_a = AutoConfig.from_pretrained(__A)
_a = AutoConfig.from_pretrained(__A)
_a = gen_config
_a = question_encoder_config
_a = model_class.from_pretrained_question_encoder_generator(
__A , __A , config=__A)
rag_model.save_pretrained(__A)
# Sanity check.
model_class.from_pretrained(__A)
# Save tokenizers.
_a = AutoTokenizer.from_pretrained(__A)
gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''')
_a = AutoTokenizer.from_pretrained(__A)
question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''')
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
"--model_type",
choices=["rag_sequence", "rag_token"],
required=True,
type=str,
help="RAG model type: rag_sequence, rag_token",
)
parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
parser.add_argument(
"--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
)
parser.add_argument(
"--generator_tokenizer_name_or_path",
type=str,
help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
)
parser.add_argument(
"--question_encoder_tokenizer_name_or_path",
type=str,
help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
)
parser.add_argument(
"--config_name_or_path",
type=str,
help=(
"Identifier of the model config to use, if not provided, resolves to a base config for a given"
" ``model_type``"
),
)
lowercase_ = parser.parse_args()
lowercase_ = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 11 |
'''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
lowercase_ = {
# 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 :
'''simple docstring'''
def __init__(self , A = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError('''Unsupported Group''' )
_a = primes[group]['''prime''']
_a = primes[group]['''generator''']
_a = int(hexlify(urandom(32 ) ) , base=16 )
def a__ (self ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def a__ (self ) -> str:
"""simple docstring"""
_a = pow(self.generator , self.__private_key , self.prime )
return hex(A )[2:]
def a__ (self , A ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(A , (self.prime - 1) // 2 , self.prime ) == 1
)
def a__ (self , A ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
if not self.is_valid_public_key(A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , self.__private_key , self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def a__ (A , A ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(A , (prime - 1) // 2 , A ) == 1
)
@staticmethod
def a__ (A , A , A = 14 ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
_a = int(A , base=16 )
_a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(A , A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , A , A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import math
def lowerCAmelCase (__A):
"""simple docstring"""
_a = [True] * n
_a = False
_a = False
_a = True
for i in range(3 , int(n**0.5 + 1) , 2):
_a = i * 2
while index < n:
_a = False
_a = index + i
_a = [2]
for i in range(3 , __A , 2):
if is_prime[i]:
primes.append(__A)
return primes
def lowerCAmelCase (__A = 999_966_663_333):
"""simple docstring"""
_a = math.floor(math.sqrt(__A)) + 100
_a = prime_sieve(__A)
_a = 0
_a = 0
_a = primes[prime_index]
while (last_prime**2) <= limit:
_a = primes[prime_index + 1]
_a = last_prime**2
_a = next_prime**2
# Get numbers divisible by lps(current)
_a = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
_a = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
_a = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
_a = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 11 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ = logging.getLogger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if os.path.exists(__A):
if os.path.exists(os.path.join(__A , '''config.json''')) and os.path.isfile(
os.path.join(__A , '''config.json''')):
os.remove(os.path.join(__A , '''config.json'''))
if os.path.exists(os.path.join(__A , '''pytorch_model.bin''')) and os.path.isfile(
os.path.join(__A , '''pytorch_model.bin''')):
os.remove(os.path.join(__A , '''pytorch_model.bin'''))
else:
os.makedirs(__A)
model.save_pretrained(__A)
def lowerCAmelCase (__A , __A=False):
"""simple docstring"""
_a = 2
if unlogit:
_a = torch.pow(__A , __A)
_a = p * torch.log(__A)
_a = 0
return -plogp.sum(dim=-1)
def lowerCAmelCase (__A):
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
for row in range(len(__A)):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
else:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))
def lowerCAmelCase (__A , __A , __A , __A=True , __A=True , __A=None , __A=False):
"""simple docstring"""
_a , _a = model.config.num_hidden_layers, model.config.num_attention_heads
_a = torch.zeros(__A , __A).to(args.device)
_a = torch.zeros(__A , __A).to(args.device)
if head_mask is None:
_a = torch.ones(__A , __A).to(args.device)
head_mask.requires_grad_(requires_grad=__A)
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_a = None
_a = 0.0
_a = 0.0
for step, inputs in enumerate(tqdm(__A , desc='''Iteration''' , disable=args.local_rank not in [-1, 0])):
_a = tuple(t.to(args.device) for t in inputs)
((_a) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_a = model(__A , labels=__A , head_mask=__A)
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_a , _a , _a = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__A):
_a = entropy(attn.detach() , __A)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__A).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_a = 2
_a = torch.pow(torch.pow(__A , __A).sum(-1) , 1 / exponent)
head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
if not args.dont_normalize_global_importance:
_a = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''')
print_ad_tensor(__A)
if compute_importance:
logger.info('''Head importance scores''')
print_ad_tensor(__A)
logger.info('''Head ranked by importance scores''')
_a = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device)
_a = torch.arange(
head_importance.numel() , device=args.device)
_a = head_ranks.view_as(__A)
print_ad_tensor(__A)
return attn_entropy, head_importance, total_loss
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a , _a , _a = compute_heads_importance(__A , __A , __A , compute_entropy=__A)
_a = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , __A , original_score * args.masking_threshold)
_a = torch.ones_like(__A)
_a = max(1 , int(new_head_mask.numel() * args.masking_amount))
_a = original_score
while current_score >= original_score * args.masking_threshold:
_a = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_a = float('''Inf''')
_a = head_importance.view(-1).sort()[1]
if len(__A) <= num_to_mask:
print('''BREAK BY num_to_mask''')
break
# mask heads
_a = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist()))
_a = new_head_mask.view(-1)
_a = 0.0
_a = new_head_mask.view_as(__A)
_a = new_head_mask.clone().detach()
print_ad_tensor(__A)
# Compute metric and head importance again
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , head_mask=__A)
_a = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''')
print_ad_tensor(__A)
np.save(os.path.join(args.output_dir , '''head_mask.npy''') , head_mask.detach().cpu().numpy())
return head_mask
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A)
_a = 1 / loss
_a = datetime.now() - before_time
_a = sum(p.numel() for p in model.parameters())
_a = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
}
for k, v in heads_to_prune.items():
if isinstance(__A , __A):
_a = [
v,
]
assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(__A)
_a = sum(p.numel() for p in model.parameters())
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A , actually_pruned=__A , )
_a = 1 / loss
_a = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , __A , __A , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , __A , __A)
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100)
save_model(__A , args.output_dir)
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=__A , type=__A , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=__A , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''')
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''')
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''')
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''')
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=__A , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=__A , help='''Amount to heads to masking at each masking step.''')
parser.add_argument('''--metric_name''' , default='''acc''' , type=__A , help='''Metric to use for head masking.''')
parser.add_argument(
'''--max_seq_length''' , default=128 , type=__A , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=__A , help='''Batch size.''')
parser.add_argument('''--seed''' , type=__A , default=42)
parser.add_argument('''--local_rank''' , type=__A , default=-1 , help='''local_rank for distributed training on gpus''')
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''')
parser.add_argument('''--server_ip''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
parser.add_argument('''--server_port''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
_a = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A)
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_a = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
_a = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
_a = torch.device('''cuda''' , args.local_rank)
_a = 1
torch.distributed.init_process_group(backend='''nccl''') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1)))
_a = GPTaLMHeadModel.from_pretrained(args.model_name_or_path)
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
_a = nn.parallel.DistributedDataParallel(
__A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__A)
elif args.n_gpu > 1:
_a = nn.DataParallel(__A)
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__A)
torch.save(__A , os.path.join(args.output_dir , '''run_args.bin'''))
logger.info('''Training/evaluation parameters %s''' , __A)
# Prepare dataset
_a = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa),
])
_a = (torch.from_numpy(__A),)
_a = TensorDataset(*__A)
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A , batch_size=args.batch_size)
# Compute head entropy and importance score
compute_heads_importance(__A , __A , __A)
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_a = mask_heads(__A , __A , __A)
prune_heads(__A , __A , __A , __A)
if __name__ == "__main__":
main()
| 11 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __A ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({'''content''': datasets.Value('''string''' )} ) , supervised_keys=A , )
def a__ (self , A , A ) -> Optional[Any]:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_dummy_examples()} )]
def a__ (self , A , A ) -> str:
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A )
class __A ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def a__ (self ) -> int:
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) , supervised_keys=A , )
def a__ (self , A , A ) -> str:
"""simple docstring"""
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_nested_examples()} )
]
def a__ (self , A , A ) -> int:
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(A )
def lowerCAmelCase ():
"""simple docstring"""
return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''])]
def lowerCAmelCase ():
"""simple docstring"""
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''])]
class __A ( A ):
'''simple docstring'''
@require_beam
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_a = DummyBeamDataset(cache_dir=A , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A , builder.name , '''default''' , '''0.0.0''' , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
_a = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , A )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , A )
self.assertDictEqual(dset['''train'''][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
import apache_beam as beam
_a = beam.io.parquetio.WriteToParquet
_a = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_a = DummyBeamDataset(cache_dir=A , beam_runner='''DirectRunner''' )
with patch('''apache_beam.io.parquetio.WriteToParquet''' ) as write_parquet_mock:
_a = partial(A , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
A , builder.name , '''default''' , '''0.0.0''' , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
A , builder.name , '''default''' , '''0.0.0''' , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
_a = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , A )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , A )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['''train''']['''content'''] ) , sorted(['''foo''', '''bar''', '''foobar'''] ) )
self.assertTrue(
os.path.exists(os.path.join(A , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_a = DummyBeamDataset(cache_dir=A )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def a__ (self ) -> str:
"""simple docstring"""
_a = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_a = NestedBeamDataset(cache_dir=A , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(A , builder.name , '''default''' , '''0.0.0''' , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) )
_a = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , A )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , A )
self.assertDictEqual(dset['''train'''][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(A , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 1
for i in range(0 , len(__A)):
total *= numbers[i]
_a = str(__A)
steps += 1
return steps
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''additive_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 0
for i in range(0 , len(__A)):
total += numbers[i]
_a = str(__A)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = RobertaTokenizer
__lowerCamelCase : List[Any] = RobertaTokenizerFast
__lowerCamelCase : Union[str, Any] = True
__lowerCamelCase : Dict = {'cls_token': '<s>'}
def a__ (self ) -> int:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = 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(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> Dict:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **A )
def a__ (self , **A ) -> str:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Any:
"""simple docstring"""
_a = '''lower newer'''
_a = '''lower newer'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''lower newer'''
_a = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
_a = tokenizer.tokenize(A ) # , add_prefix_space=True)
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def a__ (self ) -> int:
"""simple docstring"""
_a = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('''Hello world!''' , add_special_tokens=A ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode('''Hello world! cécé herlolip 418''' , add_special_tokens=A ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.tokenizer_class.from_pretrained('''roberta-base''' )
_a = tokenizer.encode('''sequence builders''' , add_special_tokens=A )
_a = tokenizer.encode('''multi-sequence build''' , add_special_tokens=A )
_a = tokenizer.encode(
'''sequence builders''' , add_special_tokens=A , add_prefix_space=A )
_a = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=A , add_prefix_space=A )
_a = tokenizer.build_inputs_with_special_tokens(A )
_a = tokenizer.build_inputs_with_special_tokens(A , A )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.get_tokenizer()
_a = '''Encode this sequence.'''
_a = tokenizer.byte_encoder[''' '''.encode('''utf-8''' )[0]]
# Testing encoder arguments
_a = tokenizer.encode(A , add_special_tokens=A , add_prefix_space=A )
_a = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(A , A )
_a = tokenizer.encode(A , add_special_tokens=A , add_prefix_space=A )
_a = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(A , A )
tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} )
_a = tokenizer.encode(A , add_special_tokens=A )
_a = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(A , A )
# Testing spaces after special tokens
_a = '''<mask>'''
tokenizer.add_special_tokens(
{'''mask_token''': AddedToken(A , lstrip=A , rstrip=A )} ) # mask token has a left space
_a = tokenizer.convert_tokens_to_ids(A )
_a = '''Encode <mask> sequence'''
_a = '''Encode <mask>sequence'''
_a = tokenizer.encode(A )
_a = encoded.index(A )
_a = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(A , A )
_a = tokenizer.encode(A )
_a = encoded.index(A )
_a = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(A , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
pass
def a__ (self ) -> List[Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_a = self.rust_tokenizer_class.from_pretrained(A , **A )
_a = self.tokenizer_class.from_pretrained(A , **A )
_a = '''A, <mask> AllenNLP sentence.'''
_a = tokenizer_r.encode_plus(A , add_special_tokens=A , return_token_type_ids=A )
_a = tokenizer_p.encode_plus(A , add_special_tokens=A , return_token_type_ids=A )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['''token_type_ids'''] ) , sum(tokens_p['''token_type_ids'''] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] ) , sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] ) , )
_a = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
_a = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
A , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
self.assertSequenceEqual(
A , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
def a__ (self ) -> Tuple:
"""simple docstring"""
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
_a = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=A , add_prefix_space=A , trim_offsets=A )
_a = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
_a = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['''add_prefix_space'''] , A )
self.assertEqual(post_processor_state['''add_prefix_space'''] , A )
self.assertEqual(post_processor_state['''trim_offsets'''] , A )
def a__ (self ) -> List[str]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_a = '''hello''' # `hello` is a token in the vocabulary of `pretrained_name`
_a = f'''{text_of_1_token} {text_of_1_token}'''
_a = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
_a = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A ) + 1, len(A ) + 1 + len(A )) , )
_a = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
_a = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A ) + 1, len(A ) + 1 + len(A )) , )
_a = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
_a = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A ), len(A ) + 1 + len(A )) , )
_a = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
_a = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(A ), len(A ) + 1 + len(A )) , )
_a = f''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
_a = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
_a = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(A ) + 1, 1 + len(A ) + 1 + len(A )) , )
_a = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
_a = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(A ), 1 + len(A ) + 1 + len(A )) , )
_a = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , add_prefix_space=A , trim_offsets=A )
_a = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(A )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(A ), 1 + len(A ) + 1 + len(A )) , )
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_a = size if size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = DPTImageProcessor if is_vision_available() else None
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPTImageProcessingTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 11 | 1 |
'''simple docstring'''
lowercase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCAmelCase ():
"""simple docstring"""
_a = input('''Enter message: ''')
_a = input('''Enter key [alphanumeric]: ''')
_a = input('''Encrypt/Decrypt [e/d]: ''')
if mode.lower().startswith('''e'''):
_a = '''encrypt'''
_a = encrypt_message(__A , __A)
elif mode.lower().startswith('''d'''):
_a = '''decrypt'''
_a = decrypt_message(__A , __A)
print(F'''\n{mode.title()}ed message:''')
print(__A)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return translate_message(__A , __A , '''encrypt''')
def lowerCAmelCase (__A , __A):
"""simple docstring"""
return translate_message(__A , __A , '''decrypt''')
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = []
_a = 0
_a = key.upper()
for symbol in message:
_a = LETTERS.find(symbol.upper())
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index])
elif mode == "decrypt":
num -= LETTERS.find(key[key_index])
num %= len(__A)
if symbol.isupper():
translated.append(LETTERS[num])
elif symbol.islower():
translated.append(LETTERS[num].lower())
key_index += 1
if key_index == len(__A):
_a = 0
else:
translated.append(__A)
return "".join(__A)
if __name__ == "__main__":
main()
| 11 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, 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
lowercase_ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __A :
'''simple docstring'''
def __init__(self , A , A=16 , A=13 , A=7 , A=14 , A=10 , A=19 , A=5 , A=4 , A=True , A=16 , A=2 , A=4 , A=4 , A="gelu" , A=0.1 , A=0.1 , A=[1, 2, 3, 4, 5] , A=25 , A=5 , ) -> List[str]:
"""simple docstring"""
_a = d_model
_a = parent
_a = batch_size
_a = prediction_length
_a = context_length
_a = cardinality
_a = num_time_features
_a = lags_sequence
_a = embedding_dimension
_a = is_training
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = context_length
_a = prediction_length + label_length
_a = label_length
_a = moving_average
_a = autocorrelation_factor
def a__ (self ) -> Any:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
_a = config.context_length + max(config.lags_sequence )
_a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, _past_length] )
_a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, config.prediction_length] )
_a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_config()
_a = self.prepare_autoformer_inputs_dict(A )
return config, inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModel(config=A ).to(A ).eval()
_a = model(**A )
_a = outputs.encoder_last_hidden_state
_a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_encoder()
encoder.save_pretrained(A )
_a = AutoformerEncoder.from_pretrained(A ).to(A )
_a , _a , _a , _a , _a = model.create_network_inputs(**A )
_a , _a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_a = encoder(inputs_embeds=A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
_a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_decoder()
decoder.save_pretrained(A )
_a = AutoformerDecoder.from_pretrained(A ).to(A )
_a = decoder(
trend=A , inputs_embeds=A , encoder_hidden_states=A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__lowerCamelCase : Tuple = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_a = model_class(A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a , _a = model_class.from_pretrained(A , output_loading_info=A )
self.assertEqual(info['''missing_keys'''] , [] )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*A )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = inspect.signature(getattr(A , '''forward''' ) )
# The main input is the name of the argument after `self`
_a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(A )] , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
_a = getattr(self.model_tester , '''seq_length''' , A )
_a = getattr(self.model_tester , '''decoder_seq_length''' , A )
_a = getattr(self.model_tester , '''encoder_seq_length''' , A )
_a = getattr(self.model_tester , '''d_model''' , A )
_a = getattr(self.model_tester , '''num_attention_heads''' , A )
_a = d_model // num_attention_heads
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_a = len(A )
_a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(A , A )
# decoder attentions
_a = outputs.decoder_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_a = outputs.cross_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
self.assertEqual(out_len + 2 , len(A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCAmelCase (__A="train-batch.pt"):
"""simple docstring"""
_a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__A , repo_type='''dataset''')
_a = torch.load(__A , map_location=__A)
return batch
@require_torch
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch()
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
_a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
_a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
_a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , A )
_a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=A )
_a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , A , rtol=1E-1 ) )
| 11 | 1 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
lowercase_ = logging.get_logger(__name__)
lowercase_ = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowercase_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : str = field(
default=A , metadata={'help': 'Model type selected in the list: ' + ', '.join(A )} )
__lowerCamelCase : str = field(
default=A , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
__lowerCamelCase : int = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase : int = field(
default=128 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
__lowerCamelCase : int = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
__lowerCamelCase : int = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
__lowerCamelCase : bool = field(
default=A , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
__lowerCamelCase : bool = field(
default=A , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
__lowerCamelCase : float = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase : int = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase : int = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
__lowerCamelCase : int = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : int = 'train'
__lowerCamelCase : Union[str, Any] = 'dev'
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : SquadDataTrainingArguments
__lowerCamelCase : List[SquadFeatures]
__lowerCamelCase : Split
__lowerCamelCase : bool
def __init__(self , A , A , A = None , A = Split.train , A = False , A = None , A = "pt" , ) -> int:
"""simple docstring"""
_a = args
_a = is_language_sensitive
_a = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(A , A ):
try:
_a = Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''' )
_a = mode
# Load data features from cache or dataset file
_a = '''v2''' if args.version_2_with_negative else '''v1'''
_a = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_a = cached_features_file + '''.lock'''
with FileLock(A ):
if os.path.exists(A ) and not args.overwrite_cache:
_a = time.time()
_a = torch.load(A )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
_a = self.old_features['''features''']
_a = self.old_features.get('''dataset''' , A )
_a = self.old_features.get('''examples''' , A )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
''' future run''' )
else:
if mode == Split.dev:
_a = self.processor.get_dev_examples(args.data_dir )
else:
_a = self.processor.get_train_examples(args.data_dir )
_a , _a = squad_convert_examples_to_features(
examples=self.examples , tokenizer=A , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=A , )
_a = time.time()
torch.save(
{'''features''': self.features, '''dataset''': self.dataset, '''examples''': self.examples} , A , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__(self ) -> List[Any]:
"""simple docstring"""
return len(self.features )
def __getitem__(self , A ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
_a = self.features[i]
_a = torch.tensor(feature.input_ids , dtype=torch.long )
_a = torch.tensor(feature.attention_mask , dtype=torch.long )
_a = torch.tensor(feature.token_type_ids , dtype=torch.long )
_a = torch.tensor(feature.cls_index , dtype=torch.long )
_a = torch.tensor(feature.p_mask , dtype=torch.float )
_a = torch.tensor(feature.is_impossible , dtype=torch.float )
_a = {
'''input_ids''': input_ids,
'''attention_mask''': attention_mask,
'''token_type_ids''': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'''cls_index''': cls_index, '''p_mask''': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'''is_impossible''': is_impossible} )
if self.is_language_sensitive:
inputs.update({'''langs''': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
_a = torch.tensor(feature.start_position , dtype=torch.long )
_a = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'''start_positions''': start_positions, '''end_positions''': end_positions} )
return inputs
| 11 |
'''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 :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=False , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> str:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , use_stable_embedding=A , )
def a__ (self , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = OpenLlamaModel(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A )
_a = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Any:
"""simple docstring"""
_a = True
_a = OpenLlamaModel(A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , )
_a = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Dict:
"""simple docstring"""
_a = True
_a = True
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_a = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_a = ids_tensor((self.batch_size, 3) , config.vocab_size )
_a = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_a = torch.cat([input_ids, next_tokens] , dim=-1 )
_a = torch.cat([input_mask, next_mask] , dim=-1 )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )['''hidden_states'''][0]
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )['''hidden_states'''][0]
# select random slice
_a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a = output_from_no_past[:, -3:, random_slice_idx].detach()
_a = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__lowerCamelCase : Any = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__lowerCamelCase : List[Any] = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''single_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''multi_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = ids_tensor([1, 10] , config.vocab_size )
_a = 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
_a = OpenLlamaModel(A )
original_model.to(A )
original_model.eval()
_a = original_model(A ).last_hidden_state
_a = original_model(A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = {'''type''': scaling_type, '''factor''': 10.0}
_a = OpenLlamaModel(A )
scaled_model.to(A )
scaled_model.eval()
_a = scaled_model(A ).last_hidden_state
_a = scaled_model(A ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
| 11 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
lowercase_ = logging.getLogger(__name__)
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__lowerCamelCase : bool = field(
default=A , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
__lowerCamelCase : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
__lowerCamelCase : bool = field(
default=A , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : Optional[str] = field(default=A , metadata={'help': 'The input training data file (a text file).'} )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
__lowerCamelCase : bool = field(
default=A , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
__lowerCamelCase : Optional[int] = field(
default=A , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
__lowerCamelCase : Optional[int] = field(
default=A , metadata={
'help': (
'The maximum total input sequence length after tokenization. If passed, sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase : bool = field(
default=A , metadata={
'help': (
'Whether to pad all samples to the maximum sentence length. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch. More '
'efficient on GPU but very bad for TPU.'
)
} , )
__lowerCamelCase : Optional[int] = field(
default=A , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
__lowerCamelCase : Optional[int] = field(
default=A , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def a__ (self ) -> str:
"""simple docstring"""
if self.train_file is not None:
_a = self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_a = self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : PreTrainedTokenizerBase
__lowerCamelCase : Union[bool, str, PaddingStrategy] = True
__lowerCamelCase : Optional[int] = None
__lowerCamelCase : Optional[int] = None
def __call__(self , A ) -> int:
"""simple docstring"""
_a = '''label''' if '''label''' in features[0].keys() else '''labels'''
_a = [feature.pop(A ) for feature in features]
_a = len(A )
_a = len(features[0]['''input_ids'''] )
_a = [
[{k: v[i] for k, v in feature.items()} for i in range(A )] for feature in features
]
_a = list(chain(*A ) )
_a = self.tokenizer.pad(
A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
# Un-flatten
_a = {k: v.view(A , A , -1 ) for k, v in batch.items()}
# Add back labels
_a = torch.tensor(A , dtype=torch.intaa )
return batch
def lowerCAmelCase ():
"""simple docstring"""
_a = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith('''.json'''):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_a , _a , _a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
_a , _a , _a = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_swag''' , __A , __A)
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout)] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_a = training_args.get_process_log_level()
logger.setLevel(__A)
datasets.utils.logging.set_verbosity(__A)
transformers.utils.logging.set_verbosity(__A)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
logger.info(F'''Training/evaluation parameters {training_args}''')
# Detecting last checkpoint.
_a = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
_a = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'''Use --overwrite_output_dir to overcome.''')
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''')
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
_a = {}
if data_args.train_file is not None:
_a = data_args.train_file
if data_args.validation_file is not None:
_a = data_args.validation_file
_a = data_args.train_file.split('''.''')[-1]
_a = load_dataset(
__A , data_files=__A , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
_a = load_dataset(
'''swag''' , '''regular''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_a = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_a = [F'''ending{i}''' for i in range(4)]
_a = '''sent1'''
_a = '''sent2'''
if data_args.max_seq_length is None:
_a = tokenizer.model_max_length
if max_seq_length > 1_024:
logger.warning(
'''The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'''
''' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'''
''' override this default with `--block_size xxx`.''')
_a = 1_024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''')
_a = min(data_args.max_seq_length , tokenizer.model_max_length)
# Preprocessing the datasets.
def preprocess_function(__A):
_a = [[context] * 4 for context in examples[context_name]]
_a = examples[question_header_name]
_a = [
[F'''{header} {examples[end][i]}''' for end in ending_names] for i, header in enumerate(__A)
]
# Flatten out
_a = list(chain(*__A))
_a = list(chain(*__A))
# Tokenize
_a = tokenizer(
__A , __A , truncation=__A , max_length=__A , padding='''max_length''' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(__A) , 4)] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''')
_a = raw_datasets['''train''']
if data_args.max_train_samples is not None:
_a = min(len(__A) , data_args.max_train_samples)
_a = train_dataset.select(range(__A))
with training_args.main_process_first(desc='''train dataset map pre-processing'''):
_a = train_dataset.map(
__A , batched=__A , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''')
_a = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
_a = min(len(__A) , data_args.max_eval_samples)
_a = eval_dataset.select(range(__A))
with training_args.main_process_first(desc='''validation dataset map pre-processing'''):
_a = eval_dataset.map(
__A , batched=__A , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
_a = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=__A , pad_to_multiple_of=8 if training_args.fpaa else None)
)
# Metric
def compute_metrics(__A):
_a , _a = eval_predictions
_a = np.argmax(__A , axis=1)
return {"accuracy": (preds == label_ids).astype(np.floataa).mean().item()}
# Initialize our Trainer
_a = Trainer(
model=__A , args=__A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=__A , data_collator=__A , compute_metrics=__A , )
# Training
if training_args.do_train:
_a = None
if training_args.resume_from_checkpoint is not None:
_a = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_a = last_checkpoint
_a = trainer.train(resume_from_checkpoint=__A)
trainer.save_model() # Saves the tokenizer too for easy upload
_a = train_result.metrics
_a = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__A)
)
_a = min(__A , len(__A))
trainer.log_metrics('''train''' , __A)
trainer.save_metrics('''train''' , __A)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''')
_a = trainer.evaluate()
_a = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A)
_a = min(__A , len(__A))
trainer.log_metrics('''eval''' , __A)
trainer.save_metrics('''eval''' , __A)
_a = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''multiple-choice''',
'''dataset_tags''': '''swag''',
'''dataset_args''': '''regular''',
'''dataset''': '''SWAG''',
'''language''': '''en''',
}
if training_args.push_to_hub:
trainer.push_to_hub(**__A)
else:
trainer.create_model_card(**__A)
def lowerCAmelCase (__A):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_choices
def a__ (self ) -> str:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxAlbertModelTester(self )
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''albert-base-v2''' )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Dict:
"""simple docstring"""
_a = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
_a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_a = model(A , attention_mask=A )[0]
_a = (1, 11, 768)
self.assertEqual(output.shape , A )
_a = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 11 | 1 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"vocab_file": "spiece.model"}
lowercase_ = {
"vocab_file": {
"AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
"AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
}
}
lowercase_ = {
"AI-Sweden/gpt-sw3-126m": 2_048,
"AI-Sweden/gpt-sw3-350m": 2_048,
"AI-Sweden/gpt-sw3-1.6b": 2_048,
"AI-Sweden/gpt-sw3-6.7b": 2_048,
"AI-Sweden/gpt-sw3-20b": 2_048,
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__lowerCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Union[str, Any] = ['input_ids', 'attention_mask']
def __init__(self , A , A=False , A=False , A=False , A=None , A=None , A=None , A=None , A = None , **A , ) -> None:
"""simple docstring"""
_a = {} if sp_model_kwargs is None else sp_model_kwargs
_a = kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
_a = '''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
_a = '''<|endoftext|>''' if eos_token is None else eos_token
_a = '''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
_a = unk_token if pad_token is None else pad_token
_a = eos_token if bos_token is None else bos_token
else:
_a = '''<pad>''' if pad_token is None else pad_token
_a = '''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=A , remove_space=A , keep_accents=A , bos_token=A , eos_token=A , unk_token=A , pad_token=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
_a = do_lower_case
_a = remove_space
_a = keep_accents
_a = vocab_file
_a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
# Used for whitespace normalization in input texts
# fmt : off
_a = {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
_a = re.compile(
f'''[{''.join(map(A , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8_203] ) )}]''' )
def __getstate__(self ) -> Tuple:
"""simple docstring"""
_a = self.__dict__.copy()
_a = None
return state
def __setstate__(self , A ) -> str:
"""simple docstring"""
_a = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_a = {}
_a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def a__ (self ) -> int:
"""simple docstring"""
return len(self.sp_model )
def a__ (self , A ) -> str:
"""simple docstring"""
_a = self.non_printing_characters_re.sub('''''' , A )
# Normalize whitespaces
_a = ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
_a = unicodedata.normalize('''NFC''' , A )
return text
def a__ (self , A , **A ) -> List[str]:
"""simple docstring"""
_a = self.preprocess_text(A )
return self.sp_model.encode(A , out_type=A )
def a__ (self , A ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(A )
def a__ (self , A ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(A )
@staticmethod
def a__ (A ) -> str:
"""simple docstring"""
return out_string
def a__ (self , A ) -> str:
"""simple docstring"""
_a = []
_a = ''''''
_a = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
_a = True
_a = []
else:
current_sub_tokens.append(A )
_a = False
out_string += self.sp_model.decode(A )
return out_string
def a__ (self ) -> Dict[str, int]:
"""simple docstring"""
_a = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def a__ (self , A , A = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_a = os.path.join(
A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A )
elif not os.path.isfile(self.vocab_file ):
with open(A , '''wb''' ) as fi:
_a = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def a__ (self , A , A = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(A , A ):
_a = self.preprocess_text(A )
_a = self.sp_model.encode(A )
else:
_a = [self.preprocess_text(A ) for t in text]
_a = self.sp_model.encode(A )
if return_tensors is True or return_tensors == "pt":
_a = torch.tensor(A )
return token_ids
def a__ (self , A ) -> str:
"""simple docstring"""
return self.sp_model.decode(A )
def a__ (self , A ) -> List[int]:
"""simple docstring"""
_a = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
_a = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=A )
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a = 0
_a = len(__A) - 2
for i in range(__A , -1 , -2):
# double the value of every second digit
_a = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_a = cc_number[:i] + str(__A) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCAmelCase (__A):
"""simple docstring"""
_a = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''')
return False
if not 13 <= len(__A) <= 16:
print(F'''{error_message} of its length.''')
return False
if not validate_initial_digits(__A):
print(F'''{error_message} of its first two digits.''')
return False
if not luhn_validation(__A):
print(F'''{error_message} it fails the Luhn check.''')
return False
print(F'''{credit_card_number} is a valid credit card number.''')
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 11 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str = 'upernet'
def __init__(self , A=None , A=512 , A=0.02 , A=[1, 2, 3, 6] , A=True , A=0.4 , A=384 , A=256 , A=1 , A=False , A=255 , **A , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**A )
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
_a = CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
elif isinstance(A , A ):
_a = backbone_config.get('''model_type''' )
_a = CONFIG_MAPPING[backbone_model_type]
_a = config_class.from_dict(A )
_a = backbone_config
_a = hidden_size
_a = initializer_range
_a = pool_scales
_a = use_auxiliary_head
_a = auxiliary_loss_weight
_a = auxiliary_in_channels
_a = auxiliary_channels
_a = auxiliary_num_convs
_a = auxiliary_concat_input
_a = loss_ignore_index
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = copy.deepcopy(self.__dict__ )
_a = self.backbone_config.to_dict()
_a = self.__class__.model_type
return output
| 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 | 1 |
'''simple docstring'''
import math
lowercase_ = 10
lowercase_ = 7
lowercase_ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase (__A = 20):
"""simple docstring"""
_a = math.comb(__A , __A)
_a = math.comb(NUM_BALLS - BALLS_PER_COLOUR , __A)
_a = NUM_COLOURS * (1 - missing_colour / total)
return F'''{result:.9f}'''
if __name__ == "__main__":
print(solution(20))
| 11 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase (__A = "laptop"):
"""simple docstring"""
_a = F'''https://www.amazon.in/laptop/s?k={product}'''
_a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
_a = BeautifulSoup(requests.get(__A , headers=__A).text)
# Initialize a Pandas dataframe with the column titles
_a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
_a = item.ha.text
_a = '''https://www.amazon.in/''' + item.ha.a['''href''']
_a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
_a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
_a = '''Not available'''
try:
_a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
_a = ''''''
try:
_a = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
_a = float('''nan''')
except AttributeError:
pass
_a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_a = ''' '''
_a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 11 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
lowercase_ = "platform"
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __A :
'''simple docstring'''
__lowerCamelCase : Dict = PegasusConfig
__lowerCamelCase : Optional[int] = {}
__lowerCamelCase : Any = 'gelu'
def __init__(self , A , A=13 , A=7 , A=True , A=False , A=99 , A=32 , A=5 , A=4 , A=37 , A=0.1 , A=0.1 , A=20 , A=2 , A=1 , A=0 , ) -> Union[str, Any]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = eos_token_id
_a = pad_token_id
_a = bos_token_id
def a__ (self ) -> Any:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
_a = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
_a = np.concatenate([input_ids, eos_tensor] , axis=1 )
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_a = prepare_pegasus_inputs_dict(A , A , A )
return config, inputs_dict
def a__ (self , A , A , A ) -> List[str]:
"""simple docstring"""
_a = 20
_a = model_class_name(A )
_a = model.encode(inputs_dict['''input_ids'''] )
_a , _a = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_a = model.init_cache(decoder_input_ids.shape[0] , A , A )
_a = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
_a = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_a = model.decode(
decoder_input_ids[:, :-1] , A , decoder_attention_mask=A , past_key_values=A , decoder_position_ids=A , )
_a = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
_a = model.decode(
decoder_input_ids[:, -1:] , A , decoder_attention_mask=A , past_key_values=outputs_cache.past_key_values , decoder_position_ids=A , )
_a = model.decode(A , A )
_a = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
def a__ (self , A , A , A ) -> Optional[int]:
"""simple docstring"""
_a = 20
_a = model_class_name(A )
_a = model.encode(inputs_dict['''input_ids'''] )
_a , _a = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_a = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
_a = model.init_cache(decoder_input_ids.shape[0] , A , A )
_a = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_a = model.decode(
decoder_input_ids[:, :-1] , A , decoder_attention_mask=A , past_key_values=A , decoder_position_ids=A , )
_a = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
_a = model.decode(
decoder_input_ids[:, -1:] , A , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=A , decoder_position_ids=A , )
_a = model.decode(A , A , decoder_attention_mask=A )
_a = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
def lowerCAmelCase (__A , __A , __A , __A=None , __A=None , ):
"""simple docstring"""
if attention_mask is None:
_a = np.not_equal(__A , config.pad_token_id).astype(np.inta)
if decoder_attention_mask is None:
_a = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id).astype(np.inta),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Tuple = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
__lowerCamelCase : Optional[int] = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
__lowerCamelCase : Tuple = True
__lowerCamelCase : Optional[Any] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : str = False
def a__ (self ) -> int:
"""simple docstring"""
_a = FlaxPegasusModelTester(self )
_a = ConfigTester(self , config_class=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(A , A , A )
def a__ (self ) -> str:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(A , A , A )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_a = self._prepare_for_class(A , A )
_a = model_class(A )
@jax.jit
def encode_jitted(A , A=None , **A ):
return model.encode(input_ids=A , attention_mask=A )
with self.subTest('''JIT Enabled''' ):
_a = encode_jitted(**A ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
_a = encode_jitted(**A ).to_tuple()
self.assertEqual(len(A ) , len(A ) )
for jitted_output, output in zip(A , A ):
self.assertEqual(jitted_output.shape , output.shape )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_a = model_class(A )
_a = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
_a = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(A , A , A ):
return model.decode(
decoder_input_ids=A , decoder_attention_mask=A , encoder_outputs=A , )
with self.subTest('''JIT Enabled''' ):
_a = decode_jitted(**A ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
_a = decode_jitted(**A ).to_tuple()
self.assertEqual(len(A ) , len(A ) )
for jitted_output, output in zip(A , A ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=A )
_a = np.ones((1, 1) )
_a = model(A )
self.assertIsNotNone(A )
@slow
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''' )
_a = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''' )
_a = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_a = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
_a = tokenizer(A , return_tensors='''np''' , truncation=A , max_length=512 , padding=A )
_a = model.generate(**A , num_beams=2 ).sequences
_a = tokenizer.batch_decode(A , skip_special_tokens=A )
assert tgt_text == decoded
| 11 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if isinstance(__A , torch.Tensor):
return image
elif isinstance(__A , PIL.Image.Image):
_a = [image]
if isinstance(image[0] , PIL.Image.Image):
_a = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
_a = np.concatenate(__A , axis=0)
_a = np.array(__A).astype(np.floataa) / 2_55.0
_a = image.transpose(0 , 3 , 1 , 2)
_a = 2.0 * image - 1.0
_a = torch.from_numpy(__A)
elif isinstance(image[0] , torch.Tensor):
_a = torch.cat(__A , dim=0)
return image
def lowerCAmelCase (__A , __A , __A , __A=0.99_95):
"""simple docstring"""
if not isinstance(__A , np.ndarray):
_a = True
_a = va.device
_a = va.cpu().numpy()
_a = va.cpu().numpy()
_a = np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
if np.abs(__A) > DOT_THRESHOLD:
_a = (1 - t) * va + t * va
else:
_a = np.arccos(__A)
_a = np.sin(__A)
_a = theta_a * t
_a = np.sin(__A)
_a = np.sin(theta_a - theta_t) / sin_theta_a
_a = sin_theta_t / sin_theta_a
_a = sa * va + sa * va
if inputs_are_torch:
_a = torch.from_numpy(__A).to(__A)
return va
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = F.normalize(__A , dim=-1)
_a = F.normalize(__A , dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for param in model.parameters():
_a = value
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
_a = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size['''shortest_edge''']
)
_a = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def a__ (self , A = "auto" ) -> Union[str, Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(A )
def a__ (self ) -> int:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self ) -> str:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = min(int(num_inference_steps * strength ) , A )
_a = max(num_inference_steps - init_timestep , 0 )
_a = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a__ (self , A , A , A , A , A , A=None ) -> List[str]:
"""simple docstring"""
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
_a = image.to(device=A , dtype=A )
if isinstance(A , A ):
_a = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
_a = torch.cat(A , dim=0 )
else:
_a = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 0.18215 * init_latents
_a = init_latents.repeat_interleave(A , dim=0 )
_a = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
_a = self.scheduler.add_noise(A , A , A )
_a = init_latents
return latents
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_a = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_a = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.feature_extractor.preprocess(A )
_a = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a__ (self , A , A , A , A , A , A , A , ) -> Union[str, Any]:
"""simple docstring"""
_a = latents.detach().requires_grad_()
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_a = self.scheduler.alphas_cumprod[timestep]
_a = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_a = torch.sqrt(A )
_a = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
_a = self.scheduler.sigmas[index]
_a = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * sample
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = transforms.Resize(self.feature_extractor_size )(A )
_a = self.normalize(A ).to(latents.dtype )
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
_a = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
_a = latents.detach() + grads * (sigma**2)
_a = noise_pred_original
else:
_a = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> str:
"""simple docstring"""
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
_a = [generator] + [None] * (batch_size - 1)
_a = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_a = [x[0] for x in coca_is_none if x[1]]
_a = ''', '''.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
# get prompt text embeddings for content and style
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_a = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
_a = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
_a = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_a = {}
if accepts_offset:
_a = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_a , _a = self.get_timesteps(A , A , self.device )
_a = timesteps[:1].repeat(A )
# Preprocess image
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = slerp(A , A , A )
if clip_guidance_scale > 0:
_a = self.get_clip_image_embeddings(A , A )
_a = self.get_clip_image_embeddings(A , A )
_a = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_a = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_a = content_text_input.input_ids.shape[-1]
_a = self.tokenizer([''''''] , padding='''max_length''' , max_length=A , return_tensors='''pt''' )
_a = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_a = uncond_embeddings.repeat_interleave(A , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_a = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_a = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_a = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_a = torch.randn(A , generator=A , device='''cpu''' , dtype=A ).to(
self.device )
else:
_a = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
# check if the scheduler accepts generator
_a = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_a = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_a = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_a , _a = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * latents
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 11 | 1 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase (__A , __A):
"""simple docstring"""
assert isinstance(__A , __A)
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True])
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = tmp_path / '''cache'''
_a = {'''text''': '''string'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_a = TextDatasetReader(__A , cache_dir=__A , keep_in_memory=__A).read()
_check_text_dataset(__A , __A)
@pytest.mark.parametrize(
'''features''' , [
None,
{'''text''': '''string'''},
{'''text''': '''int32'''},
{'''text''': '''float32'''},
] , )
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = tmp_path / '''cache'''
_a = {'''text''': '''string'''}
_a = features.copy() if features else default_expected_features
_a = (
Features({feature: Value(__A) for feature, dtype in features.items()}) if features is not None else None
)
_a = TextDatasetReader(__A , features=__A , cache_dir=__A).read()
_check_text_dataset(__A , __A)
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train'''), '''train''', '''test'''])
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = tmp_path / '''cache'''
_a = {'''text''': '''string'''}
_a = TextDatasetReader(__A , cache_dir=__A , split=__A).read()
_check_text_dataset(__A , __A)
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list])
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if issubclass(__A , __A):
_a = text_path
elif issubclass(__A , __A):
_a = [text_path]
_a = tmp_path / '''cache'''
_a = {'''text''': '''string'''}
_a = TextDatasetReader(__A , cache_dir=__A).read()
_check_text_dataset(__A , __A)
def lowerCAmelCase (__A , __A , __A=("train",)):
"""simple docstring"""
assert isinstance(__A , __A)
for split in splits:
_a = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True])
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = tmp_path / '''cache'''
_a = {'''text''': '''string'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_a = TextDatasetReader({'''train''': text_path} , cache_dir=__A , keep_in_memory=__A).read()
_check_text_datasetdict(__A , __A)
@pytest.mark.parametrize(
'''features''' , [
None,
{'''text''': '''string'''},
{'''text''': '''int32'''},
{'''text''': '''float32'''},
] , )
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = tmp_path / '''cache'''
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
_a = {'''text''': '''string'''}
_a = features.copy() if features else default_expected_features
_a = (
Features({feature: Value(__A) for feature, dtype in features.items()}) if features is not None else None
)
_a = TextDatasetReader({'''train''': text_path} , features=__A , cache_dir=__A).read()
_check_text_datasetdict(__A , __A)
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train'''), '''train''', '''test'''])
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if split:
_a = {split: text_path}
else:
_a = '''train'''
_a = {'''train''': text_path, '''test''': text_path}
_a = tmp_path / '''cache'''
_a = {'''text''': '''string'''}
_a = TextDatasetReader(__A , cache_dir=__A).read()
_check_text_datasetdict(__A , __A , splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
| 11 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = CTRLTokenizer
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Any = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = 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(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = '''adapt react readapt apt'''
_a = '''adapt react readapt apt'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt react readapt apt'''
_a = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
| 11 | 1 |
'''simple docstring'''
import random
from typing import Any
def lowerCAmelCase (__A):
"""simple docstring"""
for _ in range(len(__A)):
_a = random.randint(0 , len(__A) - 1)
_a = random.randint(0 , len(__A) - 1)
_a , _a = data[b], data[a]
return data
if __name__ == "__main__":
lowercase_ = [0, 1, 2, 3, 4, 5, 6, 7]
lowercase_ = ["python", "says", "hello", "!"]
print("Fisher-Yates Shuffle:")
print("List", integers, strings)
print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 11 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not all(x.isalpha() for x in string):
raise ValueError('''String must only contain alphabetic characters.''')
_a = sorted(string.lower())
return len(__A) == len(set(__A))
if __name__ == "__main__":
lowercase_ = input("Enter a string ").strip()
lowercase_ = is_isogram(input_str)
print(F"""{input_str} is {'an' if isogram else 'not an'} isogram.""")
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowerCAmelCase (__A):
"""simple docstring"""
if "img_encoder.pos_embed" in name:
_a = name.replace('''img_encoder.pos_embed''' , '''vision_model.embeddings.position_embeddings''')
if "img_encoder.patch_embed.proj" in name:
_a = name.replace('''img_encoder.patch_embed.proj''' , '''vision_model.embeddings.patch_embeddings.projection''')
if "img_encoder.patch_embed.norm" in name:
_a = name.replace('''img_encoder.patch_embed.norm''' , '''vision_model.embeddings.layernorm''')
if "img_encoder.layers" in name:
_a = name.replace('''img_encoder.layers''' , '''vision_model.encoder.stages''')
if "blocks" in name and "res" not in name:
_a = name.replace('''blocks''' , '''layers''')
if "attn" in name and "pre_assign" not in name:
_a = name.replace('''attn''' , '''self_attn''')
if "proj" in name and "self_attn" in name and "text" not in name:
_a = name.replace('''proj''' , '''out_proj''')
if "pre_assign_attn.attn.proj" in name:
_a = name.replace('''pre_assign_attn.attn.proj''' , '''pre_assign_attn.attn.out_proj''')
if "norm1" in name:
_a = name.replace('''norm1''' , '''layer_norm1''')
if "norm2" in name and "pre_assign" not in name:
_a = name.replace('''norm2''' , '''layer_norm2''')
if "img_encoder.norm" in name:
_a = name.replace('''img_encoder.norm''' , '''vision_model.layernorm''')
# text encoder
if "text_encoder.token_embedding" in name:
_a = name.replace('''text_encoder.token_embedding''' , '''text_model.embeddings.token_embedding''')
if "text_encoder.positional_embedding" in name:
_a = name.replace('''text_encoder.positional_embedding''' , '''text_model.embeddings.position_embedding.weight''')
if "text_encoder.transformer.resblocks." in name:
_a = name.replace('''text_encoder.transformer.resblocks.''' , '''text_model.encoder.layers.''')
if "ln_1" in name:
_a = name.replace('''ln_1''' , '''layer_norm1''')
if "ln_2" in name:
_a = name.replace('''ln_2''' , '''layer_norm2''')
if "c_fc" in name:
_a = name.replace('''c_fc''' , '''fc1''')
if "c_proj" in name:
_a = name.replace('''c_proj''' , '''fc2''')
if "text_encoder" in name:
_a = name.replace('''text_encoder''' , '''text_model''')
if "ln_final" in name:
_a = name.replace('''ln_final''' , '''final_layer_norm''')
# projection layers
if "img_projector.linear_hidden." in name:
_a = name.replace('''img_projector.linear_hidden.''' , '''visual_projection.''')
if "img_projector.linear_out." in name:
_a = name.replace('''img_projector.linear_out.''' , '''visual_projection.3.''')
if "text_projector.linear_hidden" in name:
_a = name.replace('''text_projector.linear_hidden''' , '''text_projection''')
if "text_projector.linear_out" in name:
_a = name.replace('''text_projector.linear_out''' , '''text_projection.3''')
return name
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_a = orig_state_dict.pop(__A)
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
_a = key.split('''.''')
_a , _a = int(key_split[2]), int(key_split[4])
_a = config.vision_config.hidden_size
if "weight" in key:
_a = val[:dim, :]
_a = val[dim : dim * 2, :]
_a = val[-dim:, :]
else:
_a = val[:dim]
_a = val[dim : dim * 2]
_a = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
_a = key.split('''.''')
_a = int(key_split[3])
_a = config.text_config.hidden_size
if "weight" in key:
_a = val[:dim, :]
_a = val[
dim : dim * 2, :
]
_a = val[-dim:, :]
else:
_a = val[:dim]
_a = val[dim : dim * 2]
_a = val[-dim:]
else:
_a = rename_key(__A)
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
_a = val.squeeze_()
else:
_a = val
return orig_state_dict
def lowerCAmelCase ():
"""simple docstring"""
_a = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a = Image.open(requests.get(__A , stream=__A).raw)
return im
@torch.no_grad()
def lowerCAmelCase (__A , __A , __A="groupvit-gcc-yfcc" , __A=False):
"""simple docstring"""
_a = GroupViTConfig()
_a = GroupViTModel(__A).eval()
_a = torch.load(__A , map_location='''cpu''')['''model''']
_a = convert_state_dict(__A , __A)
_a , _a = model.load_state_dict(__A , strict=__A)
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__A) == 0)
# verify result
_a = CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
_a = prepare_img()
_a = processor(text=['''a photo of a cat''', '''a photo of a dog'''] , images=__A , padding=__A , return_tensors='''pt''')
with torch.no_grad():
_a = model(**__A)
if model_name == "groupvit-gcc-yfcc":
_a = torch.tensor([[13.35_23, 6.36_29]])
elif model_name == "groupvit-gcc-redcaps":
_a = torch.tensor([[16.18_73, 8.62_30]])
else:
raise ValueError(F'''Model name {model_name} not supported.''')
assert torch.allclose(outputs.logits_per_image , __A , atol=1e-3)
processor.save_pretrained(__A)
model.save_pretrained(__A)
print('''Successfully saved processor and model to''' , __A)
if push_to_hub:
print('''Pushing to the hub...''')
processor.push_to_hub(__A , organization='''nielsr''')
model.push_to_hub(__A , organization='''nielsr''')
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
)
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
parser.add_argument(
"--model_name",
default="groupvit-gccy-fcc",
type=str,
help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
)
lowercase_ = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowercase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_ = 10
lowercase_ = 256
def lowerCAmelCase (__A):
"""simple docstring"""
if len(__A) < MIN_NUM_TOKENS:
return None
_a = MinHash(num_perm=__A)
for token in set(__A):
min_hash.update(token.encode())
return min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}
class __A :
'''simple docstring'''
def __init__(self , *,
A = 0.85 , ) -> Optional[int]:
"""simple docstring"""
_a = duplication_jaccard_threshold
_a = NUM_PERM
_a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_a = defaultdict(A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
_a = self._index.query(A )
if code_key in self._index.keys:
print(f'''Duplicate key {code_key}''' )
return
self._index.insert(A , A )
if len(A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A )
def a__ (self ) -> List[List[Dict]]:
"""simple docstring"""
_a = []
for base, duplicates in self._duplicate_clusters.items():
_a = [base] + list(A )
# reformat the cluster to be a list of dict
_a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(A )
return duplicate_clusters
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_duplicate_clusters()
with open(A , '''w''' ) as f:
json.dump(A , A )
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = element
_a = get_min_hash([t for t in NON_ALPHA.split(data['''content''']) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__A , max_queue_size=10_000) , chunksize=100 , ):
if data is not None:
yield data
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = DuplicationIndex(duplication_jaccard_threshold=__A)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A)) , max_queue_size=100)):
di.add(__A , __A)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = get_tokens(__A)
_a = get_tokens(__A)
return len(tokensa & tokensa) / len(tokensa | tokensa)
lowercase_ = None
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for elementa in cluster:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__A , __A) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_a = 1
extremes.append(__A)
return extremes
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
global _shared_dataset
_a = dataset
_a = []
_a = partial(_find_cluster_extremes_shared , jaccard_threshold=__A)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__A , __A , ) , total=len(__A) , ):
extremes_list.append(__A)
return extremes_list
def lowerCAmelCase (__A , __A = 0.85):
"""simple docstring"""
_a = make_duplicate_clusters(__A , __A)
_a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_a = {}
_a = find_extremes(__A , __A , __A)
for extremes in extremes_clusters:
for element in extremes:
_a = element
_a = duplicate_indices - set(extreme_dict.keys())
_a = dataset.filter(lambda __A , __A: idx not in remove_indices , with_indices=__A)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_a = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(__A)}''')
print(F'''Number of duplicate clusters: {len(__A)}''')
print(F'''Files in duplicate cluster: {len(__A)}''')
print(F'''Unique files in duplicate cluster: {len(__A)}''')
print(F'''Filtered dataset size: {len(__A)}''')
return ds_filter, duplicate_clusters
| 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowercase_ = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
"processing_trocr": ["TrOCRProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrOCRForCausalLM",
"TrOCRPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 | 1 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCAmelCase (__A , __A , __A , __A=5):
"""simple docstring"""
assert masked_input.count('''<mask>''') == 1
_a = torch.tensor(tokenizer.encode(__A , add_special_tokens=__A)).unsqueeze(0) # Batch size 1
_a = model(__A)[0] # The last hidden-state is the first element of the output tuple
_a = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_a = logits[0, masked_index, :]
_a = logits.softmax(dim=0)
_a , _a = prob.topk(k=__A , dim=0)
_a = ''' '''.join(
[tokenizer.convert_ids_to_tokens(indices[i].item()) for i in range(len(__A))])
_a = tokenizer.mask_token
_a = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''')):
_a = predicted_token_bpe.replace('''\u2581''' , ''' ''')
if " {0}".format(__A) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(''' {0}'''.format(__A) , __A),
values[index].item(),
predicted_token,
))
else:
topk_filled_outputs.append(
(
masked_input.replace(__A , __A),
values[index].item(),
predicted_token,
))
return topk_filled_outputs
lowercase_ = CamembertTokenizer.from_pretrained("camembert-base")
lowercase_ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
lowercase_ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 11 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = IFInpaintingSuperResolutionPipeline
__lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
__lowerCamelCase : str = PipelineTesterMixin.required_optional_params - {'latents'}
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def a__ (self , A , A=0 ) -> List[Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = floats_tensor((1, 3, 16, 16) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ (self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def a__ (self ) -> str:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._test_save_load_local()
def a__ (self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 11 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return len(set(__A)) == len(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> List[str]:
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split() , encoding='''utf-8''' , check=A , )
assert hasattr(self , '''env''' )
def a__ (self , A ) -> Optional[Any]:
"""simple docstring"""
_a = f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
# distributed data settings
_a = {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None
# 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=A , instance_count=A , instance_type=self.instance_type , debugger_hook_config=A , hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=A , py_version='''py36''' , )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
TrainingJobAnalytics(A ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''' )
@parameterized.expand([(2,)] )
def a__ (self , A ) -> List[str]:
"""simple docstring"""
_a = self.create_estimator(A )
# run training
estimator.fit()
# result dataframe
_a = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
_a = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
_a = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
_a = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 999_999 )
)
# 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} , A )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
return len(set(__A)) == len(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = ''''''
for i in table:
res += inp[i - 1]
return res
def lowerCAmelCase (__A):
"""simple docstring"""
return data[1:] + data[0]
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = ''''''
for i in range(len(__A)):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = int('''0b''' + data[0] + data[-1] , 2)
_a = int('''0b''' + data[1:3] , 2)
return bin(s[row][col])[2:]
def lowerCAmelCase (__A , __A , __A , __A , __A):
"""simple docstring"""
_a = message[:4]
_a = message[4:]
_a = apply_table(__A , __A)
_a = xor(__A , __A)
_a = apply_sbox(__A , temp[:4]) # noqa: E741
_a = apply_sbox(__A , temp[4:])
_a = '''0''' * (2 - len(__A)) + l # noqa: E741
_a = '''0''' * (2 - len(__A)) + r
_a = apply_table(l + r , __A)
_a = xor(__A , __A)
return temp + right
if __name__ == "__main__":
lowercase_ = input("Enter 10 bit key: ")
lowercase_ = input("Enter 8 bit message: ")
lowercase_ = [6, 3, 7, 4, 8, 5, 10, 9]
lowercase_ = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
lowercase_ = [2, 4, 3, 1]
lowercase_ = [2, 6, 3, 1, 4, 8, 5, 7]
lowercase_ = [4, 1, 3, 5, 7, 2, 8, 6]
lowercase_ = [4, 1, 2, 3, 2, 3, 4, 1]
lowercase_ = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
lowercase_ = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
lowercase_ = apply_table(key, paa_table)
lowercase_ = temp[:5]
lowercase_ = temp[5:]
lowercase_ = left_shift(left)
lowercase_ = left_shift(right)
lowercase_ = apply_table(left + right, pa_table)
lowercase_ = left_shift(left)
lowercase_ = left_shift(right)
lowercase_ = left_shift(left)
lowercase_ = left_shift(right)
lowercase_ = apply_table(left + right, pa_table)
# encryption
lowercase_ = apply_table(message, IP)
lowercase_ = function(expansion, sa, sa, keya, temp)
lowercase_ = temp[4:] + temp[:4]
lowercase_ = function(expansion, sa, sa, keya, temp)
lowercase_ = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
lowercase_ = apply_table(CT, IP)
lowercase_ = function(expansion, sa, sa, keya, temp)
lowercase_ = temp[4:] + temp[:4]
lowercase_ = function(expansion, sa, sa, keya, temp)
lowercase_ = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if len(__A) == 0:
return False
_a = len(__A) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return binary_search(a_list[:midpoint] , __A)
else:
return binary_search(a_list[midpoint + 1 :] , __A)
if __name__ == "__main__":
lowercase_ = input("Enter numbers separated by comma:\n").strip()
lowercase_ = [int(item.strip()) for item in user_input.split(",")]
lowercase_ = int(input("Enter the number to be found in the list:\n").strip())
lowercase_ = "" if binary_search(sequence, target) else "not "
print(F"""{target} was {not_str}found in {sequence}""")
| 11 | 1 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCAmelCase ():
"""simple docstring"""
_a , _a = 9, 14 # noqa: F841
_a = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_a = defaultdict(__A)
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost])
adjancency[nodea].append([nodea, cost])
_a = mst(__A)
_a = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
_a = tuple(answer[:2])
_a = tuple(edge[::-1])
assert edge in result or reverse in result
| 11 |
'''simple docstring'''
class __A :
'''simple docstring'''
def __init__(self , A ) -> None:
"""simple docstring"""
_a = len(A )
_a = [0] * len_array
if len_array > 0:
_a = array[0]
for i in range(1 , A ):
_a = self.prefix_sum[i - 1] + array[i]
def a__ (self , A , A ) -> int:
"""simple docstring"""
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def a__ (self , A ) -> bool:
"""simple docstring"""
_a = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(A )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : List[Any] = ['image_processor', 'feature_extractor']
__lowerCamelCase : int = 'TvltImageProcessor'
__lowerCamelCase : Optional[Any] = 'TvltFeatureExtractor'
def __init__(self , A , A ) -> Optional[Any]:
"""simple docstring"""
super().__init__(image_processor=A , feature_extractor=A )
_a = image_processor
_a = feature_extractor
def __call__(self , A=None , A=None , A=None , A=None , A=False , A=False , *A , **A , ) -> List[Any]:
"""simple docstring"""
if images is None and audio is None:
raise ValueError('''You need to specify either an `images` or `audio` input to process.''' )
_a = None
if images is not None:
_a = self.image_processor(A , mask_pixel=A , *A , **A )
if images_mixed is not None:
_a = self.image_processor(A , is_mixed=A , *A , **A )
if audio is not None:
_a = self.feature_extractor(
A , *A , sampling_rate=A , mask_audio=A , **A )
_a = {}
if audio is not None:
output_dict.update(A )
if images is not None:
output_dict.update(A )
if images_mixed_dict is not None:
output_dict.update(A )
return output_dict
@property
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.image_processor.model_input_names
_a = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 11 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__A)
if n > 1:
factors.append(__A)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def lowerCAmelCase (__A):
"""simple docstring"""
_a = DPTConfig(embedding_type='''hybrid''')
if "large" in checkpoint_url:
_a = 1_024
_a = 4_096
_a = 24
_a = 16
_a = [5, 11, 17, 23]
_a = [256, 512, 1_024, 1_024]
_a = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
_a = 768
_a = [1, 1, 1, 0.5]
_a = [256, 512, 768, 768]
_a = 150
_a = 16
_a = (1, 384, 384)
_a = False
_a = '''project'''
if "ade" in checkpoint_url:
_a = True
_a = 768
_a = [1, 1, 1, 0.5]
_a = 150
_a = 16
_a = '''huggingface/label-files'''
_a = '''ade20k-id2label.json'''
_a = json.load(open(cached_download(hf_hub_url(__A , __A , repo_type='''dataset''')) , '''r'''))
_a = {int(__A): v for k, v in idalabel.items()}
_a = idalabel
_a = {v: k for k, v in idalabel.items()}
_a = [1, 150, 480, 480]
return config, expected_shape
def lowerCAmelCase (__A):
"""simple docstring"""
_a = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(__A , __A)
def lowerCAmelCase (__A):
"""simple docstring"""
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
_a = name.replace('''pretrained.model''' , '''dpt.encoder''')
if "pretrained.model" in name:
_a = name.replace('''pretrained.model''' , '''dpt.embeddings''')
if "patch_embed" in name:
_a = name.replace('''patch_embed''' , '''''')
if "pos_embed" in name:
_a = name.replace('''pos_embed''' , '''position_embeddings''')
if "attn.proj" in name:
_a = name.replace('''attn.proj''' , '''attention.output.dense''')
if "proj" in name and "project" not in name:
_a = name.replace('''proj''' , '''projection''')
if "blocks" in name:
_a = name.replace('''blocks''' , '''layer''')
if "mlp.fc1" in name:
_a = name.replace('''mlp.fc1''' , '''intermediate.dense''')
if "mlp.fc2" in name:
_a = name.replace('''mlp.fc2''' , '''output.dense''')
if "norm1" in name and "backbone" not in name:
_a = name.replace('''norm1''' , '''layernorm_before''')
if "norm2" in name and "backbone" not in name:
_a = name.replace('''norm2''' , '''layernorm_after''')
if "scratch.output_conv" in name:
_a = name.replace('''scratch.output_conv''' , '''head''')
if "scratch" in name:
_a = name.replace('''scratch''' , '''neck''')
if "layer1_rn" in name:
_a = name.replace('''layer1_rn''' , '''convs.0''')
if "layer2_rn" in name:
_a = name.replace('''layer2_rn''' , '''convs.1''')
if "layer3_rn" in name:
_a = name.replace('''layer3_rn''' , '''convs.2''')
if "layer4_rn" in name:
_a = name.replace('''layer4_rn''' , '''convs.3''')
if "refinenet" in name:
_a = int(name[len('''neck.refinenet''') : len('''neck.refinenet''') + 1])
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
_a = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4)}''')
if "out_conv" in name:
_a = name.replace('''out_conv''' , '''projection''')
if "resConfUnit1" in name:
_a = name.replace('''resConfUnit1''' , '''residual_layer1''')
if "resConfUnit2" in name:
_a = name.replace('''resConfUnit2''' , '''residual_layer2''')
if "conv1" in name:
_a = name.replace('''conv1''' , '''convolution1''')
if "conv2" in name:
_a = name.replace('''conv2''' , '''convolution2''')
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
_a = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''')
if "pretrained.act_postprocess2.0.project.0" in name:
_a = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''')
if "pretrained.act_postprocess3.0.project.0" in name:
_a = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''')
if "pretrained.act_postprocess4.0.project.0" in name:
_a = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''')
# resize blocks
if "pretrained.act_postprocess1.3" in name:
_a = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''')
if "pretrained.act_postprocess1.4" in name:
_a = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''')
if "pretrained.act_postprocess2.3" in name:
_a = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''')
if "pretrained.act_postprocess2.4" in name:
_a = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''')
if "pretrained.act_postprocess3.3" in name:
_a = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''')
if "pretrained.act_postprocess4.3" in name:
_a = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''')
if "pretrained.act_postprocess4.4" in name:
_a = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''')
if "pretrained" in name:
_a = name.replace('''pretrained''' , '''dpt''')
if "bn" in name:
_a = name.replace('''bn''' , '''batch_norm''')
if "head" in name:
_a = name.replace('''head''' , '''head.head''')
if "encoder.norm" in name:
_a = name.replace('''encoder.norm''' , '''layernorm''')
if "auxlayer" in name:
_a = name.replace('''auxlayer''' , '''auxiliary_head.head''')
if "backbone" in name:
_a = name.replace('''backbone''' , '''backbone.bit.encoder''')
if ".." in name:
_a = name.replace('''..''' , '''.''')
if "stem.conv" in name:
_a = name.replace('''stem.conv''' , '''bit.embedder.convolution''')
if "blocks" in name:
_a = name.replace('''blocks''' , '''layers''')
if "convolution" in name and "backbone" in name:
_a = name.replace('''convolution''' , '''conv''')
if "layer" in name and "backbone" in name:
_a = name.replace('''layer''' , '''layers''')
if "backbone.bit.encoder.bit" in name:
_a = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''')
if "embedder.conv" in name:
_a = name.replace('''embedder.conv''' , '''embedder.convolution''')
if "backbone.bit.encoder.stem.norm" in name:
_a = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''')
return name
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for i in range(config.num_hidden_layers):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_a = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''')
_a = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''')
# next, add query, keys and values (in that order) to the state dict
_a = in_proj_weight[: config.hidden_size, :]
_a = in_proj_bias[: config.hidden_size]
_a = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_a = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_a = in_proj_weight[
-config.hidden_size :, :
]
_a = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase ():
"""simple docstring"""
_a = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a = Image.open(requests.get(__A , stream=__A).raw)
return im
@torch.no_grad()
def lowerCAmelCase (__A , __A , __A , __A , __A):
"""simple docstring"""
_a , _a = get_dpt_config(__A)
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
_a = torch.load(__A , map_location='''cpu''')
# remove certain keys
remove_ignore_keys_(__A)
# rename keys
for key in state_dict.copy().keys():
_a = state_dict.pop(__A)
_a = val
# read in qkv matrices
read_in_q_k_v(__A , __A)
# load HuggingFace model
_a = DPTForSemanticSegmentation(__A) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__A)
model.load_state_dict(__A)
model.eval()
# Check outputs on an image
_a = 480 if '''ade''' in checkpoint_url else 384
_a = DPTImageProcessor(size=__A)
_a = prepare_img()
_a = image_processor(__A , return_tensors='''pt''')
# forward pass
_a = model(**__A).logits if '''ade''' in checkpoint_url else model(**__A).predicted_depth
if show_prediction:
_a = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=__A , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255).show()
if pytorch_dump_folder_path is not None:
Path(__A).mkdir(exist_ok=__A)
print(F'''Saving model to {pytorch_dump_folder_path}''')
model.save_pretrained(__A)
print(F'''Saving image processor to {pytorch_dump_folder_path}''')
image_processor.save_pretrained(__A)
if push_to_hub:
model.push_to_hub('''ybelkada/dpt-hybrid-midas''')
image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''')
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
type=str,
help="URL of the original DPT checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
parser.add_argument(
"--model_name",
default="dpt-large",
type=str,
help="Name of the model, in case you're pushing to the hub.",
)
parser.add_argument(
"--show_prediction",
action="store_true",
)
lowercase_ = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 11 |
'''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
lowercase_ = {
# 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 :
'''simple docstring'''
def __init__(self , A = 14 ) -> None:
"""simple docstring"""
if group not in primes:
raise ValueError('''Unsupported Group''' )
_a = primes[group]['''prime''']
_a = primes[group]['''generator''']
_a = int(hexlify(urandom(32 ) ) , base=16 )
def a__ (self ) -> str:
"""simple docstring"""
return hex(self.__private_key )[2:]
def a__ (self ) -> str:
"""simple docstring"""
_a = pow(self.generator , self.__private_key , self.prime )
return hex(A )[2:]
def a__ (self , A ) -> bool:
"""simple docstring"""
return (
2 <= key <= self.prime - 2
and pow(A , (self.prime - 1) // 2 , self.prime ) == 1
)
def a__ (self , A ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
if not self.is_valid_public_key(A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , self.__private_key , self.prime )
return shaaaa(str(A ).encode() ).hexdigest()
@staticmethod
def a__ (A , A ) -> bool:
"""simple docstring"""
return (
2 <= remote_public_key_str <= prime - 2
and pow(A , (prime - 1) // 2 , A ) == 1
)
@staticmethod
def a__ (A , A , A = 14 ) -> str:
"""simple docstring"""
_a = int(A , base=16 )
_a = int(A , base=16 )
_a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(A , A ):
raise ValueError('''Invalid public key''' )
_a = pow(A , A , A )
return shaaaa(str(A ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
lowercase_ = logging.getLogger(__name__)
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : Optional[str] = field(
default='tab_fact' , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
__lowerCamelCase : Optional[str] = field(
default='tab_fact' , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} , )
__lowerCamelCase : int = field(
default=1_024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase : bool = field(
default=A , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
__lowerCamelCase : bool = field(
default=A , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
__lowerCamelCase : Optional[int] = field(
default=A , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
__lowerCamelCase : Optional[int] = field(
default=A , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
__lowerCamelCase : Optional[int] = field(
default=A , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'A csv or a json file containing the training data.'} )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'A csv or a json file containing the validation data.'} )
__lowerCamelCase : Optional[str] = field(default=A , metadata={'help': 'A csv or a json file containing the test data.'} )
def a__ (self ) -> Any:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('''Need either a GLUE task, a training/validation file or a dataset name.''' )
else:
_a = self.train_file.split('''.''' )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_a = self.validation_file.split('''.''' )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __A :
'''simple docstring'''
__lowerCamelCase : str = field(
default=A , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__lowerCamelCase : Optional[str] = field(
default=A , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__lowerCamelCase : bool = field(
default=A , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
__lowerCamelCase : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
__lowerCamelCase : bool = field(
default=A , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def lowerCAmelCase ():
"""simple docstring"""
_a = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith('''.json'''):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_a , _a , _a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
_a , _a , _a = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout)] , )
_a = training_args.get_process_log_level()
logger.setLevel(__A)
datasets.utils.logging.set_verbosity(__A)
transformers.utils.logging.set_verbosity(__A)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
logger.info(F'''Training/evaluation parameters {training_args}''')
# Detecting last checkpoint.
_a = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
_a = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'''Use --overwrite_output_dir to overcome.''')
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''')
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_a = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir)
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_a = {'''train''': data_args.train_file, '''validation''': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_a = data_args.train_file.split('''.''')[-1]
_a = data_args.test_file.split('''.''')[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_a = data_args.test_file
else:
raise ValueError('''Need either a GLUE task or a test file for `do_predict`.''')
for key in data_files.keys():
logger.info(F'''load a local file for {key}: {data_files[key]}''')
if data_args.train_file.endswith('''.csv'''):
# Loading a dataset from local csv files
_a = load_dataset('''csv''' , data_files=__A , cache_dir=model_args.cache_dir)
else:
# Loading a dataset from local json files
_a = load_dataset('''json''' , data_files=__A , cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_a = raw_datasets['''train'''].features['''label'''].names
_a = len(__A)
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_a = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=__A , )
_a = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path) , config=__A , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_a = '''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_a = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_a = {'''Refused''': 0, '''Entailed''': 1}
_a = {0: '''Refused''', 1: '''Entailed'''}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''')
_a = min(data_args.max_seq_length , tokenizer.model_max_length)
def preprocess_tabfact_function(__A):
# Tokenize the texts
def _convert_table_text_to_pandas(__A):
_a = [_table_row.split('''#''') for _table_row in _table_text.strip('''\n''').split('''\n''')]
_a = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0])
return _table_pd
_a = examples['''statement''']
_a = list(map(_convert_table_text_to_pandas , examples['''table_text''']))
_a = tokenizer(__A , __A , padding=__A , max_length=__A , truncation=__A)
_a = examples['''label''']
return result
with training_args.main_process_first(desc='''dataset map pre-processing'''):
_a = raw_datasets.map(
__A , batched=__A , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on dataset''' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''')
_a = raw_datasets['''train''']
if data_args.max_train_samples is not None:
_a = train_dataset.select(range(data_args.max_train_samples))
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''')
_a = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
_a = eval_dataset.select(range(data_args.max_eval_samples))
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('''--do_predict requires a test dataset''')
_a = raw_datasets['''test''']
if data_args.max_predict_samples is not None:
_a = predict_dataset.select(range(data_args.max_predict_samples))
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(__A)) , 3):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''')
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__A):
_a = p.predictions[0] if isinstance(p.predictions , __A) else p.predictions
_a = np.argmax(__A , axis=1)
return {"accuracy": (preds == p.label_ids).astype(np.floataa).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_a = default_data_collator
elif training_args.fpaa:
_a = DataCollatorWithPadding(__A , pad_to_multiple_of=8)
else:
_a = None
# Initialize our Trainer
_a = Trainer(
model=__A , args=__A , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__A , tokenizer=__A , data_collator=__A , )
# Training
if training_args.do_train:
_a = None
if training_args.resume_from_checkpoint is not None:
_a = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_a = last_checkpoint
_a = trainer.train(resume_from_checkpoint=__A)
_a = train_result.metrics
_a = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__A)
)
_a = min(__A , len(__A))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''' , __A)
trainer.save_metrics('''train''' , __A)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''')
_a = trainer.evaluate(eval_dataset=__A)
_a = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__A)
_a = min(__A , len(__A))
trainer.log_metrics('''eval''' , __A)
trainer.save_metrics('''eval''' , __A)
if training_args.do_predict:
logger.info('''*** Predict ***''')
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_a = predict_dataset.remove_columns('''label''')
_a = trainer.predict(__A , metric_key_prefix='''predict''').predictions
_a = np.argmax(__A , axis=1)
_a = os.path.join(training_args.output_dir , '''predict_results_tabfact.txt''')
if trainer.is_world_process_zero():
with open(__A , '''w''') as writer:
logger.info('''***** Predict Results *****''')
writer.write('''index\tprediction\n''')
for index, item in enumerate(__A):
_a = label_list[item]
writer.write(F'''{index}\t{item}\n''')
_a = {'''finetuned_from''': model_args.model_name_or_path, '''tasks''': '''text-classification'''}
if training_args.push_to_hub:
trainer.push_to_hub(**__A)
else:
trainer.create_model_card(**__A)
def lowerCAmelCase (__A):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 11 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ = logging.getLogger(__name__)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if os.path.exists(__A):
if os.path.exists(os.path.join(__A , '''config.json''')) and os.path.isfile(
os.path.join(__A , '''config.json''')):
os.remove(os.path.join(__A , '''config.json'''))
if os.path.exists(os.path.join(__A , '''pytorch_model.bin''')) and os.path.isfile(
os.path.join(__A , '''pytorch_model.bin''')):
os.remove(os.path.join(__A , '''pytorch_model.bin'''))
else:
os.makedirs(__A)
model.save_pretrained(__A)
def lowerCAmelCase (__A , __A=False):
"""simple docstring"""
_a = 2
if unlogit:
_a = torch.pow(__A , __A)
_a = p * torch.log(__A)
_a = 0
return -plogp.sum(dim=-1)
def lowerCAmelCase (__A):
"""simple docstring"""
logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__A))))
for row in range(len(__A)):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data))
else:
logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data))
def lowerCAmelCase (__A , __A , __A , __A=True , __A=True , __A=None , __A=False):
"""simple docstring"""
_a , _a = model.config.num_hidden_layers, model.config.num_attention_heads
_a = torch.zeros(__A , __A).to(args.device)
_a = torch.zeros(__A , __A).to(args.device)
if head_mask is None:
_a = torch.ones(__A , __A).to(args.device)
head_mask.requires_grad_(requires_grad=__A)
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
_a = None
_a = 0.0
_a = 0.0
for step, inputs in enumerate(tqdm(__A , desc='''Iteration''' , disable=args.local_rank not in [-1, 0])):
_a = tuple(t.to(args.device) for t in inputs)
((_a) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
_a = model(__A , labels=__A , head_mask=__A)
# (loss), lm_logits, presents, (all hidden_states), (attentions)
_a , _a , _a = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__A):
_a = entropy(attn.detach() , __A)
attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__A).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
_a = 2
_a = torch.pow(torch.pow(__A , __A).sum(-1) , 1 / exponent)
head_importance /= norm_by_layer.unsqueeze(-1) + 1e-20
if not args.dont_normalize_global_importance:
_a = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('''Attention entropies''')
print_ad_tensor(__A)
if compute_importance:
logger.info('''Head importance scores''')
print_ad_tensor(__A)
logger.info('''Head ranked by importance scores''')
_a = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device)
_a = torch.arange(
head_importance.numel() , device=args.device)
_a = head_ranks.view_as(__A)
print_ad_tensor(__A)
return attn_entropy, head_importance, total_loss
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a , _a , _a = compute_heads_importance(__A , __A , __A , compute_entropy=__A)
_a = 1 / loss # instead of downsteam score use the LM loss
logger.info('''Pruning: original score: %f, threshold: %f''' , __A , original_score * args.masking_threshold)
_a = torch.ones_like(__A)
_a = max(1 , int(new_head_mask.numel() * args.masking_amount))
_a = original_score
while current_score >= original_score * args.masking_threshold:
_a = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
_a = float('''Inf''')
_a = head_importance.view(-1).sort()[1]
if len(__A) <= num_to_mask:
print('''BREAK BY num_to_mask''')
break
# mask heads
_a = current_heads_to_mask[:num_to_mask]
logger.info('''Heads to mask: %s''' , str(current_heads_to_mask.tolist()))
_a = new_head_mask.view(-1)
_a = 0.0
_a = new_head_mask.view_as(__A)
_a = new_head_mask.clone().detach()
print_ad_tensor(__A)
# Compute metric and head importance again
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , head_mask=__A)
_a = 1 / loss
logger.info(
'''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __A , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info('''Final head mask''')
print_ad_tensor(__A)
np.save(os.path.join(args.output_dir , '''head_mask.npy''') , head_mask.detach().cpu().numpy())
return head_mask
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A)
_a = 1 / loss
_a = datetime.now() - before_time
_a = sum(p.numel() for p in model.parameters())
_a = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__A))
}
for k, v in heads_to_prune.items():
if isinstance(__A , __A):
_a = [
v,
]
assert sum(len(__A) for h in heads_to_prune.values()) == (1 - head_mask.long()).sum().item()
model.prune_heads(__A)
_a = sum(p.numel() for p in model.parameters())
_a = datetime.now()
_a , _a , _a = compute_heads_importance(
__A , __A , __A , compute_entropy=__A , compute_importance=__A , head_mask=__A , actually_pruned=__A , )
_a = 1 / loss
_a = datetime.now() - before_time
logger.info(
'''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)''' , __A , __A , pruned_num_params / original_num_params * 100 , )
logger.info('''Pruning: score with masking: %f score with pruning: %f''' , __A , __A)
logger.info('''Pruning: speed ratio (original timing / new timing): %f percents''' , original_time / new_time * 100)
save_model(__A , args.output_dir)
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--data_dir''' , default=__A , type=__A , required=__A , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--output_dir''' , default=__A , type=__A , required=__A , help='''The output directory where the model predictions and checkpoints will be written.''' , )
# Other parameters
parser.add_argument(
'''--config_name''' , default='''''' , type=__A , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--tokenizer_name''' , default='''''' , type=__A , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
parser.add_argument(
'''--cache_dir''' , default=__A , type=__A , help='''Where do you want to store the pre-trained models downloaded from s3''' , )
parser.add_argument(
'''--data_subset''' , type=__A , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.''')
parser.add_argument(
'''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory''')
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers''')
parser.add_argument(
'''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )
parser.add_argument(
'''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.''')
parser.add_argument(
'''--masking_threshold''' , default=0.9 , type=__A , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
parser.add_argument(
'''--masking_amount''' , default=0.1 , type=__A , help='''Amount to heads to masking at each masking step.''')
parser.add_argument('''--metric_name''' , default='''acc''' , type=__A , help='''Metric to use for head masking.''')
parser.add_argument(
'''--max_seq_length''' , default=128 , type=__A , help=(
'''The maximum total input sequence length after WordPiece tokenization. \n'''
'''Sequences longer than this will be truncated, sequences shorter padded.'''
) , )
parser.add_argument('''--batch_size''' , default=1 , type=__A , help='''Batch size.''')
parser.add_argument('''--seed''' , type=__A , default=42)
parser.add_argument('''--local_rank''' , type=__A , default=-1 , help='''local_rank for distributed training on gpus''')
parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available''')
parser.add_argument('''--server_ip''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
parser.add_argument('''--server_port''' , type=__A , default='''''' , help='''Can be used for distant debugging.''')
_a = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('''Waiting for debugger attach''')
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__A)
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
_a = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
_a = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
_a = torch.device('''cuda''' , args.local_rank)
_a = 1
torch.distributed.init_process_group(backend='''nccl''') # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device , args.n_gpu , bool(args.local_rank != -1)))
_a = GPTaLMHeadModel.from_pretrained(args.model_name_or_path)
# Distributed and parallel training
model.to(args.device)
if args.local_rank != -1:
_a = nn.parallel.DistributedDataParallel(
__A , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__A)
elif args.n_gpu > 1:
_a = nn.DataParallel(__A)
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__A)
torch.save(__A , os.path.join(args.output_dir , '''run_args.bin'''))
logger.info('''Training/evaluation parameters %s''' , __A)
# Prepare dataset
_a = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa),
])
_a = (torch.from_numpy(__A),)
_a = TensorDataset(*__A)
_a = RandomSampler(__A)
_a = DataLoader(__A , sampler=__A , batch_size=args.batch_size)
# Compute head entropy and importance score
compute_heads_importance(__A , __A , __A)
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_a = mask_heads(__A , __A , __A)
prune_heads(__A , __A , __A , __A)
if __name__ == "__main__":
main()
| 11 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Tuple = DebertaTokenizer
__lowerCamelCase : Dict = True
__lowerCamelCase : str = DebertaTokenizerFast
def a__ (self ) -> str:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''[UNK]''',
]
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
_a = {'''unk_token''': '''[UNK]'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = 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(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> str:
"""simple docstring"""
_a = '''lower newer'''
_a = '''lower newer'''
return input_text, output_text
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = self.get_tokenizer()
_a = '''lower newer'''
_a = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.get_tokenizer()
_a = tokenizer('''Hello''' , '''World''' )
_a = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd['''token_type_ids'''] , A )
@slow
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
_a = tokenizer.encode('''sequence builders''' , add_special_tokens=A )
_a = tokenizer.encode('''multi-sequence build''' , add_special_tokens=A )
_a = tokenizer.encode(
'''sequence builders''' , add_special_tokens=A , add_prefix_space=A )
_a = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=A , add_prefix_space=A )
_a = tokenizer.build_inputs_with_special_tokens(A )
_a = tokenizer.build_inputs_with_special_tokens(A , A )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
_a = tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
_a = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
_a = tokenizer(A , padding=A )
_a = [tokenizer.decode(A , skip_special_tokens=A ) for seq in encoding['''input_ids''']]
# fmt: off
_a = {
'''input_ids''': [
[1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
],
'''token_type_ids''': [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
'''attention_mask''': [
[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],
[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],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
]
}
# fmt: on
_a = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
self.assertDictEqual(encoding.data , A )
for expected, decoded in zip(A , A ):
self.assertEqual(A , A )
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 1
for i in range(0 , len(__A)):
total *= numbers[i]
_a = str(__A)
steps += 1
return steps
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''additive_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''')
_a = 0
_a = str(__A)
while len(__A) != 1:
_a = [int(__A) for i in num_string]
_a = 0
for i in range(0 , len(__A)):
total += numbers[i]
_a = str(__A)
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 | 1 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Dict = (DPMSolverSDEScheduler,)
__lowerCamelCase : Union[str, Any] = 10
def a__ (self , **A ) -> List[str]:
"""simple docstring"""
_a = {
'''num_train_timesteps''': 1_100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**A )
return config
def a__ (self ) -> Tuple:
"""simple docstring"""
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=A , beta_end=A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=A )
def a__ (self ) -> int:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma
_a = sample.to(A )
for i, t in enumerate(scheduler.timesteps ):
_a = scheduler.scale_model_input(A , A )
_a = model(A , A )
_a = scheduler.step(A , A , A )
_a = output.prev_sample
_a = torch.sum(torch.abs(A ) )
_a = torch.mean(torch.abs(A ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1E-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1E-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config(prediction_type='''v_prediction''' )
_a = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps )
_a = self.dummy_model()
_a = self.dummy_sample_deter * scheduler.init_noise_sigma
_a = sample.to(A )
for i, t in enumerate(scheduler.timesteps ):
_a = scheduler.scale_model_input(A , A )
_a = model(A , A )
_a = scheduler.step(A , A , A )
_a = output.prev_sample
_a = torch.sum(torch.abs(A ) )
_a = torch.mean(torch.abs(A ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1E-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1E-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1E-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1E-3
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps , device=A )
_a = self.dummy_model()
_a = self.dummy_sample_deter.to(A ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_a = scheduler.scale_model_input(A , A )
_a = model(A , A )
_a = scheduler.step(A , A , A )
_a = output.prev_sample
_a = torch.sum(torch.abs(A ) )
_a = torch.mean(torch.abs(A ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1E-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1E-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config()
_a = scheduler_class(**A , use_karras_sigmas=A )
scheduler.set_timesteps(self.num_inference_steps , device=A )
_a = self.dummy_model()
_a = self.dummy_sample_deter.to(A ) * scheduler.init_noise_sigma
_a = sample.to(A )
for t in scheduler.timesteps:
_a = scheduler.scale_model_input(A , A )
_a = model(A , A )
_a = scheduler.step(A , A , A )
_a = output.prev_sample
_a = torch.sum(torch.abs(A ) )
_a = torch.mean(torch.abs(A ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
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 DPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=18 , A=30 , A=400 , A=True , A=None , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , ) -> str:
"""simple docstring"""
_a = size if size is not None else {'''height''': 18, '''width''': 18}
_a = parent
_a = batch_size
_a = num_channels
_a = image_size
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_normalize
_a = image_mean
_a = image_std
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = DPTImageProcessor if is_vision_available() else None
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPTImageProcessingTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , '''image_mean''' ) )
self.assertTrue(hasattr(A , '''image_std''' ) )
self.assertTrue(hasattr(A , '''do_normalize''' ) )
self.assertTrue(hasattr(A , '''do_resize''' ) )
self.assertTrue(hasattr(A , '''size''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
_a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
_a = image_processing(A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 11 | 1 |
'''simple docstring'''
import numpy as np
def lowerCAmelCase (__A , __A , __A , __A , __A):
"""simple docstring"""
_a = int(np.ceil((x_end - xa) / h))
_a = np.zeros((n + 1,))
_a = ya
_a = xa
for k in range(__A):
_a = f(__A , y[k])
_a = f(x + 0.5 * h , y[k] + 0.5 * h * ka)
_a = f(x + 0.5 * h , y[k] + 0.5 * h * ka)
_a = f(x + h , y[k] + h * ka)
_a = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 11 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, 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
lowercase_ = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class __A :
'''simple docstring'''
def __init__(self , A , A=16 , A=13 , A=7 , A=14 , A=10 , A=19 , A=5 , A=4 , A=True , A=16 , A=2 , A=4 , A=4 , A="gelu" , A=0.1 , A=0.1 , A=[1, 2, 3, 4, 5] , A=25 , A=5 , ) -> List[str]:
"""simple docstring"""
_a = d_model
_a = parent
_a = batch_size
_a = prediction_length
_a = context_length
_a = cardinality
_a = num_time_features
_a = lags_sequence
_a = embedding_dimension
_a = is_training
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = context_length
_a = prediction_length + label_length
_a = label_length
_a = moving_average
_a = autocorrelation_factor
def a__ (self ) -> Any:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def a__ (self , A ) -> List[Any]:
"""simple docstring"""
_a = config.context_length + max(config.lags_sequence )
_a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, _past_length] )
_a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_a = floats_tensor([self.batch_size, config.prediction_length] )
_a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.get_config()
_a = self.prepare_autoformer_inputs_dict(A )
return config, inputs_dict
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ (self , A , A ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModel(config=A ).to(A ).eval()
_a = model(**A )
_a = outputs.encoder_last_hidden_state
_a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_encoder()
encoder.save_pretrained(A )
_a = AutoformerEncoder.from_pretrained(A ).to(A )
_a , _a , _a , _a , _a = model.create_network_inputs(**A )
_a , _a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_a = encoder(inputs_embeds=A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
_a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_a = model.get_decoder()
decoder.save_pretrained(A )
_a = AutoformerDecoder.from_pretrained(A ).to(A )
_a = decoder(
trend=A , inputs_embeds=A , encoder_hidden_states=A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__lowerCamelCase : Optional[Any] = (AutoformerForPrediction,) if is_torch_available() else ()
__lowerCamelCase : Tuple = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = AutoformerModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_a = model_class(A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A )
_a , _a = model_class.from_pretrained(A , output_loading_info=A )
self.assertEqual(info['''missing_keys'''] , [] )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*A )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def a__ (self ) -> Tuple:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = inspect.signature(getattr(A , '''forward''' ) )
# The main input is the name of the argument after `self`
_a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(A )] , A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
_a = getattr(self.model_tester , '''seq_length''' , A )
_a = getattr(self.model_tester , '''decoder_seq_length''' , A )
_a = getattr(self.model_tester , '''encoder_seq_length''' , A )
_a = getattr(self.model_tester , '''d_model''' , A )
_a = getattr(self.model_tester , '''num_attention_heads''' , A )
_a = d_model // num_attention_heads
for model_class in self.all_model_classes:
_a = True
_a = False
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_a = len(A )
_a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(A , A )
# decoder attentions
_a = outputs.decoder_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_a = outputs.cross_attentions
self.assertIsInstance(A , (list, tuple) )
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_a = True
_a = True
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
self.assertEqual(out_len + 2 , len(A ) )
_a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCAmelCase (__A="train-batch.pt"):
"""simple docstring"""
_a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=__A , repo_type='''dataset''')
_a = torch.load(__A , map_location=__A)
return batch
@require_torch
@slow
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch()
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
_a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
_a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , A )
_a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=A )
self.assertTrue(torch.allclose(output[0, :3, :3] , A , atol=A ) )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(A )
_a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
_a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
_a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , A )
_a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=A )
_a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , A , rtol=1E-1 ) )
| 11 | 1 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
lowercase_ = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def lowerCAmelCase (__A):
"""simple docstring"""
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
return max(metric_fn(__A , __A) for gt in ground_truths)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = [line.strip() for line in open(__A , '''r''').readlines()]
_a = []
if args.gold_data_mode == "qa":
_a = pd.read_csv(__A , sep='''\t''' , header=__A)
for answer_list in data[1]:
_a = ast.literal_eval(__A)
answers.append(__A)
else:
_a = [line.strip() for line in open(__A , '''r''').readlines()]
_a = [[reference] for reference in references]
_a = _a = _a = 0
for prediction, ground_truths in zip(__A , __A):
total += 1
em += metric_max_over_ground_truths(__A , __A , __A)
fa += metric_max_over_ground_truths(__A , __A , __A)
_a = 1_00.0 * em / total
_a = 1_00.0 * fa / total
logger.info(F'''F1: {fa:.2f}''')
logger.info(F'''EM: {em:.2f}''')
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = args.k
_a = [line.strip() for line in open(__A , '''r''').readlines()]
_a = [line.strip() for line in open(__A , '''r''').readlines()]
_a = _a = 0
for hypo, reference in zip(__A , __A):
_a = set(hypo.split('''\t''')[:k])
_a = set(reference.split('''\t'''))
total += 1
em += len(hypo_provenance & ref_provenance) / k
_a = 1_00.0 * em / total
logger.info(F'''Precision@{k}: {em: .2f}''')
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
def strip_title(__A):
if title.startswith('''"'''):
_a = title[1:]
if title.endswith('''"'''):
_a = title[:-1]
return title
_a = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__A , return_tensors='''pt''' , padding=__A , truncation=__A , )['''input_ids'''].to(args.device)
_a = rag_model.rag.question_encoder(__A)
_a = question_enc_outputs[0]
_a = rag_model.retriever(
__A , question_enc_pool_output.cpu().detach().to(torch.floataa).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors='''pt''' , )
_a = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
_a = []
for docs in all_docs:
_a = [strip_title(__A) for title in docs['''title''']]
provenance_strings.append('''\t'''.join(__A))
return provenance_strings
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
with torch.no_grad():
_a = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__A , return_tensors='''pt''' , padding=__A , truncation=__A)
_a = inputs_dict.input_ids.to(args.device)
_a = inputs_dict.attention_mask.to(args.device)
_a = rag_model.generate( # rag_model overwrites generate
__A , attention_mask=__A , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=__A , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
_a = rag_model.retriever.generator_tokenizer.batch_decode(__A , skip_special_tokens=__A)
if args.print_predictions:
for q, a in zip(__A , __A):
logger.info('''Q: {} - A: {}'''.format(__A , __A))
return answers
def lowerCAmelCase ():
"""simple docstring"""
_a = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''' , choices=['''rag_sequence''', '''rag_token''', '''bart'''] , type=__A , help=(
'''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
''' model_name_or_path'''
) , )
parser.add_argument(
'''--index_name''' , default=__A , choices=['''exact''', '''compressed''', '''legacy'''] , type=__A , help='''RAG model retriever type''' , )
parser.add_argument(
'''--index_path''' , default=__A , type=__A , help='''Path to the retrieval index''' , )
parser.add_argument('''--n_docs''' , default=5 , type=__A , help='''Number of retrieved docs''')
parser.add_argument(
'''--model_name_or_path''' , default=__A , type=__A , required=__A , help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--eval_mode''' , choices=['''e2e''', '''retrieval'''] , default='''e2e''' , type=__A , help=(
'''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
''' precision@k.'''
) , )
parser.add_argument('''--k''' , default=1 , type=__A , help='''k for the precision@k calculation''')
parser.add_argument(
'''--evaluation_set''' , default=__A , type=__A , required=__A , help='''Path to a file containing evaluation samples''' , )
parser.add_argument(
'''--gold_data_path''' , default=__A , type=__A , required=__A , help='''Path to a tab-separated file with gold samples''' , )
parser.add_argument(
'''--gold_data_mode''' , default='''qa''' , type=__A , choices=['''qa''', '''ans'''] , help=(
'''Format of the gold data file'''
'''qa - a single line in the following format: question [tab] answer_list'''
'''ans - a single line of the gold file contains the expected answer string'''
) , )
parser.add_argument(
'''--predictions_path''' , type=__A , default='''predictions.txt''' , help='''Name of the predictions file, to be stored in the checkpoints directory''' , )
parser.add_argument(
'''--eval_all_checkpoints''' , action='''store_true''' , help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''' , )
parser.add_argument(
'''--eval_batch_size''' , default=8 , type=__A , help='''Batch size per GPU/CPU for evaluation.''' , )
parser.add_argument(
'''--recalculate''' , help='''Recalculate predictions even if the prediction file exists''' , action='''store_true''' , )
parser.add_argument(
'''--num_beams''' , default=4 , type=__A , help='''Number of beams to be used when generating answers''' , )
parser.add_argument('''--min_length''' , default=1 , type=__A , help='''Min length of the generated answers''')
parser.add_argument('''--max_length''' , default=50 , type=__A , help='''Max length of the generated answers''')
parser.add_argument(
'''--print_predictions''' , action='''store_true''' , help='''If True, prints predictions while evaluating.''' , )
parser.add_argument(
'''--print_docs''' , action='''store_true''' , help='''If True, prints docs retried while generating.''' , )
_a = parser.parse_args()
_a = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')
return args
def lowerCAmelCase (__A):
"""simple docstring"""
_a = {}
if args.model_type is None:
_a = infer_model_type(args.model_name_or_path)
assert args.model_type is not None
if args.model_type.startswith('''rag'''):
_a = RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
_a = args.n_docs
if args.index_name is not None:
_a = args.index_name
if args.index_path is not None:
_a = args.index_path
else:
_a = BartForConditionalGeneration
_a = (
[f.path for f in os.scandir(args.model_name_or_path) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info('''Evaluate the following checkpoints: %s''' , __A)
_a = get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
_a = evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path) and (not args.recalculate):
logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path))
score_fn(__A , args.predictions_path , args.gold_data_path)
continue
logger.info('''***** Running evaluation for {} *****'''.format(__A))
logger.info(''' Batch size = %d''' , args.eval_batch_size)
logger.info(''' Predictions will be stored under {}'''.format(args.predictions_path))
if args.model_type.startswith('''rag'''):
_a = RagRetriever.from_pretrained(__A , **__A)
_a = model_class.from_pretrained(__A , retriever=__A , **__A)
model.retriever.init_retrieval()
else:
_a = model_class.from_pretrained(__A , **__A)
model.to(args.device)
with open(args.evaluation_set , '''r''') as eval_file, open(args.predictions_path , '''w''') as preds_file:
_a = []
for line in tqdm(__A):
questions.append(line.strip())
if len(__A) == args.eval_batch_size:
_a = evaluate_batch_fn(__A , __A , __A)
preds_file.write('''\n'''.join(__A) + '''\n''')
preds_file.flush()
_a = []
if len(__A) > 0:
_a = evaluate_batch_fn(__A , __A , __A)
preds_file.write('''\n'''.join(__A))
preds_file.flush()
score_fn(__A , args.predictions_path , args.gold_data_path)
if __name__ == "__main__":
lowercase_ = get_args()
main(args)
| 11 |
'''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 :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=False , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> str:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = scope
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ (self ) -> Optional[int]:
"""simple docstring"""
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , use_stable_embedding=A , )
def a__ (self , A , A , A , A , A , A , A ) -> Any:
"""simple docstring"""
_a = OpenLlamaModel(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A )
_a = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Any:
"""simple docstring"""
_a = True
_a = OpenLlamaModel(A )
model.to(A )
model.eval()
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , )
_a = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ (self , A , A , A , A , A , A , A , A , A , ) -> Dict:
"""simple docstring"""
_a = True
_a = True
_a = OpenLlamaForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , use_cache=A , )
_a = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_a = ids_tensor((self.batch_size, 3) , config.vocab_size )
_a = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_a = torch.cat([input_ids, next_tokens] , dim=-1 )
_a = torch.cat([input_mask, next_mask] , dim=-1 )
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , output_hidden_states=A , )['''hidden_states'''][0]
_a = model(
A , attention_mask=A , encoder_hidden_states=A , encoder_attention_mask=A , past_key_values=A , output_hidden_states=A , )['''hidden_states'''][0]
# select random slice
_a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_a = output_from_no_past[:, -3:, random_slice_idx].detach()
_a = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
__lowerCamelCase : Any = (OpenLlamaForCausalLM,) if is_torch_available() else ()
__lowerCamelCase : List[Any] = (
{
'feature-extraction': OpenLlamaModel,
'text-classification': OpenLlamaForSequenceClassification,
'text-generation': OpenLlamaForCausalLM,
'zero-shot': OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = OpenLlamaModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_a = type
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Dict:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''single_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = 3
_a = '''multi_label_classification'''
_a = input_dict['''input_ids''']
_a = input_ids.ne(1 ).to(A )
_a = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_a = OpenLlamaForSequenceClassification(A )
model.to(A )
model.eval()
_a = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''' )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = ids_tensor([1, 10] , config.vocab_size )
_a = 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
_a = OpenLlamaModel(A )
original_model.to(A )
original_model.eval()
_a = original_model(A ).last_hidden_state
_a = original_model(A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_a = {'''type''': scaling_type, '''factor''': 10.0}
_a = OpenLlamaModel(A )
scaled_model.to(A )
scaled_model.eval()
_a = scaled_model(A ).last_hidden_state
_a = scaled_model(A ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1E-5 ) )
| 11 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"FALCON_PRETRAINED_MODEL_ARCHIVE_LIST",
"FalconForCausalLM",
"FalconModel",
"FalconPreTrainedModel",
"FalconForSequenceClassification",
"FalconForTokenClassification",
"FalconForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> List[str]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_choices
def a__ (self ) -> str:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxAlbertModelTester(self )
@slow
def a__ (self ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''albert-base-v2''' )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Dict:
"""simple docstring"""
_a = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
_a = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_a = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_a = model(A , attention_mask=A )[0]
_a = (1, 11, 768)
self.assertEqual(output.shape , A )
_a = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 11 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self , A ) -> str:
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['''bs'''] , model_result['''ss'''] ):
_a = model_result['''result'''][batch_size][sequence_length]
self.assertIsNotNone(A )
def a__ (self ) -> str:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = '''sgugger/tiny-distilbert-classification'''
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , only_pretrain_model=A , )
_a = PyTorchBenchmark(A )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , torchscript=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == '''cpu''' , '''Cant do half precision''' )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , fpaa=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a__ (self ) -> int:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
_a = AutoConfig.from_pretrained(A )
# set architectures equal to `None`
_a = None
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A , configs=[config] )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a__ (self ) -> str:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == '''cpu''' , '''Can\'t do half precision''' )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , fpaa=A , multi_process=A , )
_a = PyTorchBenchmark(A )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
_a = AutoConfig.from_pretrained(A )
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A , configs=[config] )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = '''sshleifer/tinier_bart'''
_a = AutoConfig.from_pretrained(A )
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A , configs=[config] )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a__ (self ) -> Any:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
_a = AutoConfig.from_pretrained(A )
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A , configs=[config] )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = '''sshleifer/tinier_bart'''
_a = AutoConfig.from_pretrained(A )
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , multi_process=A , )
_a = PyTorchBenchmark(A , configs=[config] )
_a = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a__ (self ) -> int:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
with tempfile.TemporaryDirectory() as tmp_dir:
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , save_to_csv=A , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(A , '''inf_time.csv''' ) , train_memory_csv_file=os.path.join(A , '''train_mem.csv''' ) , inference_memory_csv_file=os.path.join(A , '''inf_mem.csv''' ) , train_time_csv_file=os.path.join(A , '''train_time.csv''' ) , env_info_csv_file=os.path.join(A , '''env.csv''' ) , multi_process=A , )
_a = PyTorchBenchmark(A )
benchmark.run()
self.assertTrue(Path(os.path.join(A , '''inf_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''train_time.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''inf_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''train_mem.csv''' ) ).exists() )
self.assertTrue(Path(os.path.join(A , '''env.csv''' ) ).exists() )
def a__ (self ) -> str:
"""simple docstring"""
_a = '''sshleifer/tiny-gpt2'''
def _check_summary_is_not_empty(A ):
self.assertTrue(hasattr(A , '''sequential''' ) )
self.assertTrue(hasattr(A , '''cumulative''' ) )
self.assertTrue(hasattr(A , '''current''' ) )
self.assertTrue(hasattr(A , '''total''' ) )
with tempfile.TemporaryDirectory() as tmp_dir:
_a = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=A , inference=A , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(A , '''log.txt''' ) , log_print=A , trace_memory_line_by_line=A , multi_process=A , )
_a = PyTorchBenchmark(A )
_a = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(A , '''log.txt''' ) ).exists() )
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a = 0
_a = len(__A) - 2
for i in range(__A , -1 , -2):
# double the value of every second digit
_a = int(cc_number[i])
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_a = cc_number[:i] + str(__A) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__A) - 1 , -1 , -2):
total += int(cc_number[i])
return total % 10 == 0
def lowerCAmelCase (__A):
"""simple docstring"""
_a = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''')
return False
if not 13 <= len(__A) <= 16:
print(F'''{error_message} of its length.''')
return False
if not validate_initial_digits(__A):
print(F'''{error_message} of its first two digits.''')
return False
if not luhn_validation(__A):
print(F'''{error_message} it fails the Luhn check.''')
return False
print(F'''{credit_card_number} is a valid credit card number.''')
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 11 | 1 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
lowercase_ = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
lowercase_ = {
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
lowercase_ = {
"vocab_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
),
},
}
lowercase_ = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
lowercase_ = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
lowercase_ = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
lowercase_ = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowercase_ = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowercase_ = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Optional[int] = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : str = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Optional[Any] = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase : int = DPRContextEncoderTokenizer
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Dict = VOCAB_FILES_NAMES
__lowerCamelCase : str = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : str = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Union[str, Any] = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase : List[str] = DPRQuestionEncoderTokenizer
lowercase_ = collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
lowercase_ = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
lowercase_ = R"\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `'tf'`: Return TensorFlow `tf.constant` objects.\n - `'pt'`: Return PyTorch `torch.Tensor` objects.\n - `'np'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer's default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Return:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n "
@add_start_docstrings(A )
class __A :
'''simple docstring'''
def __call__(self , A , A = None , A = None , A = False , A = False , A = None , A = None , A = None , **A , ) -> BatchEncoding:
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
A , padding=A , truncation=A , max_length=A , return_tensors=A , return_attention_mask=A , **A , )
elif titles is None or texts is None:
_a = titles if texts is None else texts
return super().__call__(
A , A , padding=A , truncation=A , max_length=A , return_tensors=A , return_attention_mask=A , **A , )
_a = titles if not isinstance(A , A ) else [titles]
_a = texts if not isinstance(A , A ) else [texts]
_a = len(A )
_a = questions if not isinstance(A , A ) else [questions] * n_passages
assert len(A ) == len(
A ), f'''There should be as many titles than texts but got {len(A )} titles and {len(A )} texts.'''
_a = super().__call__(A , A , padding=A , truncation=A )['''input_ids''']
_a = super().__call__(A , add_special_tokens=A , padding=A , truncation=A )['''input_ids''']
_a = {
'''input_ids''': [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(A , A )
]
}
if return_attention_mask is not False:
_a = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
_a = attention_mask
return self.pad(A , padding=A , max_length=A , return_tensors=A )
def a__ (self , A , A , A = 16 , A = 64 , A = 4 , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
_a = reader_input['''input_ids''']
_a , _a , _a = reader_output[:3]
_a = len(A )
_a = sorted(range(A ) , reverse=A , key=relevance_logits.__getitem__ )
_a = []
for doc_id in sorted_docs:
_a = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
_a = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
_a = sequence_ids.index(self.pad_token_id )
else:
_a = len(A )
_a = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=A , top_spans=A , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=A , start_index=A , end_index=A , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(A ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def a__ (self , A , A , A , A , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
_a = []
for start_index, start_score in enumerate(A ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
_a = sorted(A , key=lambda A : x[1] , reverse=A )
_a = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
_a = end_index - start_index + 1
assert length <= max_answer_length, f'''Span is too long: {length} > {max_answer_length}'''
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(A ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(A )
class __A ( A , A ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Any = READER_PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[Any] = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Dict = READER_PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase : Optional[Any] = ['input_ids', 'attention_mask']
__lowerCamelCase : Optional[int] = DPRReaderTokenizer
| 11 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
def __init__(self , *A , **A ) -> None:
"""simple docstring"""
warnings.warn(
'''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use LayoutLMv2ImageProcessor instead.''' , A , )
super().__init__(*A , **A )
| 11 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase (__A = "laptop"):
"""simple docstring"""
_a = F'''https://www.amazon.in/laptop/s?k={product}'''
_a = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
_a = BeautifulSoup(requests.get(__A , headers=__A).text)
# Initialize a Pandas dataframe with the column titles
_a = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
_a = item.ha.text
_a = '''https://www.amazon.in/''' + item.ha.a['''href''']
_a = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
_a = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
_a = '''Not available'''
try:
_a = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
_a = ''''''
try:
_a = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
_a = float('''nan''')
except AttributeError:
pass
_a = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
_a = ''' '''
_a = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = "headphones"
get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
| 11 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"The `inpainting.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionInpaintPipeline` instead."
)
| 11 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if isinstance(__A , torch.Tensor):
return image
elif isinstance(__A , PIL.Image.Image):
_a = [image]
if isinstance(image[0] , PIL.Image.Image):
_a = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
_a = np.concatenate(__A , axis=0)
_a = np.array(__A).astype(np.floataa) / 2_55.0
_a = image.transpose(0 , 3 , 1 , 2)
_a = 2.0 * image - 1.0
_a = torch.from_numpy(__A)
elif isinstance(image[0] , torch.Tensor):
_a = torch.cat(__A , dim=0)
return image
def lowerCAmelCase (__A , __A , __A , __A=0.99_95):
"""simple docstring"""
if not isinstance(__A , np.ndarray):
_a = True
_a = va.device
_a = va.cpu().numpy()
_a = va.cpu().numpy()
_a = np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
if np.abs(__A) > DOT_THRESHOLD:
_a = (1 - t) * va + t * va
else:
_a = np.arccos(__A)
_a = np.sin(__A)
_a = theta_a * t
_a = np.sin(__A)
_a = np.sin(theta_a - theta_t) / sin_theta_a
_a = sin_theta_t / sin_theta_a
_a = sa * va + sa * va
if inputs_are_torch:
_a = torch.from_numpy(__A).to(__A)
return va
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = F.normalize(__A , dim=-1)
_a = F.normalize(__A , dim=-1)
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
def lowerCAmelCase (__A , __A):
"""simple docstring"""
for param in model.parameters():
_a = value
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , )
_a = (
feature_extractor.size
if isinstance(feature_extractor.size , A )
else feature_extractor.size['''shortest_edge''']
)
_a = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , A )
set_requires_grad(self.clip_model , A )
def a__ (self , A = "auto" ) -> Union[str, Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(A )
def a__ (self ) -> int:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , A )
def a__ (self ) -> Dict:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self ) -> str:
"""simple docstring"""
set_requires_grad(self.unet , A )
def a__ (self , A , A , A ) -> Optional[Any]:
"""simple docstring"""
_a = min(int(num_inference_steps * strength ) , A )
_a = max(num_inference_steps - init_timestep , 0 )
_a = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a__ (self , A , A , A , A , A , A=None ) -> List[str]:
"""simple docstring"""
if not isinstance(A , torch.Tensor ):
raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A )}''' )
_a = image.to(device=A , dtype=A )
if isinstance(A , A ):
_a = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A )
]
_a = torch.cat(A , dim=0 )
else:
_a = self.vae.encode(A ).latent_dist.sample(A )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 0.18215 * init_latents
_a = init_latents.repeat_interleave(A , dim=0 )
_a = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A )
# get latents
_a = self.scheduler.add_noise(A , A , A )
_a = init_latents
return latents
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = self.coca_transform(A ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_a = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_a = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' )
def a__ (self , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.feature_extractor.preprocess(A )
_a = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half()
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = image_embeddings_clip.repeat_interleave(A , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a__ (self , A , A , A , A , A , A , A , ) -> Union[str, Any]:
"""simple docstring"""
_a = latents.detach().requires_grad_()
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_a = self.scheduler.alphas_cumprod[timestep]
_a = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_a = torch.sqrt(A )
_a = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , A ):
_a = self.scheduler.sigmas[index]
_a = latents - sigma * noise_pred
else:
raise ValueError(f'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * sample
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = transforms.Resize(self.feature_extractor_size )(A )
_a = self.normalize(A ).to(latents.dtype )
_a = self.clip_model.get_image_features(A )
_a = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A )
_a = spherical_dist_loss(A , A ).mean() * clip_guidance_scale
_a = -torch.autograd.grad(A , A )[0]
if isinstance(self.scheduler , A ):
_a = latents.detach() + grads * (sigma**2)
_a = noise_pred_original
else:
_a = noise_pred_original - torch.sqrt(A ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__(self , A , A , A = None , A = None , A = 512 , A = 512 , A = 0.6 , A = 50 , A = 7.5 , A = 1 , A = 0.0 , A = 100 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> str:
"""simple docstring"""
if isinstance(A , A ) and len(A ) != batch_size:
raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(A , torch.Generator ) and batch_size > 1:
_a = [generator] + [None] * (batch_size - 1)
_a = [
('''model''', self.coca_model is None),
('''tokenizer''', self.coca_tokenizer is None),
('''transform''', self.coca_transform is None),
]
_a = [x[0] for x in coca_is_none if x[1]]
_a = ''', '''.join(A )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(A ):
raise ValueError(
f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
if style_prompt is None:
if len(A ):
raise ValueError(
f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
_a = self.get_image_description(A )
# get prompt text embeddings for content and style
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_a = self.tokenizer(
A , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors='''pt''' , )
_a = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_a = slerp(A , A , A )
# duplicate text embeddings for each generation per prompt
_a = text_embeddings.repeat_interleave(A , dim=0 )
# set timesteps
_a = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_a = {}
if accepts_offset:
_a = 1
self.scheduler.set_timesteps(A , **A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_a , _a = self.get_timesteps(A , A , self.device )
_a = timesteps[:1].repeat(A )
# Preprocess image
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = preprocess(A , A , A )
_a = self.prepare_latents(
A , A , A , text_embeddings.dtype , self.device , A )
_a = slerp(A , A , A )
if clip_guidance_scale > 0:
_a = self.get_clip_image_embeddings(A , A )
_a = self.get_clip_image_embeddings(A , A )
_a = slerp(
A , A , A )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_a = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_a = content_text_input.input_ids.shape[-1]
_a = self.tokenizer([''''''] , padding='''max_length''' , max_length=A , return_tensors='''pt''' )
_a = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_a = uncond_embeddings.repeat_interleave(A , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_a = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_a = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_a = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_a = torch.randn(A , generator=A , device='''cpu''' , dtype=A ).to(
self.device )
else:
_a = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_a = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
# check if the scheduler accepts generator
_a = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_a = generator
with self.progress_bar(total=A ):
for i, t in enumerate(A ):
# expand the latents if we are doing classifier free guidance
_a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_a = self.scheduler.scale_model_input(A , A )
# predict the noise residual
_a = self.unet(A , A , encoder_hidden_states=A ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_a , _a = noise_pred.chunk(2 )
_a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_a = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_a , _a = self.cond_fn(
A , A , A , A , A , A , A , )
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(A , A , A , **A ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_a = 1 / 0.18215 * latents
_a = self.vae.decode(A ).sample
_a = (image / 2 + 0.5).clamp(0 , 1 )
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(A )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 11 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
lowercase_ = re.compile(R"\s+")
def lowerCAmelCase (__A):
"""simple docstring"""
return {"hash": hashlib.mda(re.sub(__A , '''''' , example['''content''']).encode('''utf-8''')).hexdigest()}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = [len(__A) for line in example['''content'''].splitlines()]
return {"line_mean": np.mean(__A), "line_max": max(__A)}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = np.mean([c.isalnum() for c in example['''content''']])
return {"alpha_frac": alpha_frac}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if example["hash"] in uniques:
uniques.remove(example['''hash'''])
return True
else:
return False
def lowerCAmelCase (__A , __A=5):
"""simple docstring"""
_a = ['''auto-generated''', '''autogenerated''', '''automatically generated''']
_a = example['''content'''].splitlines()
for _, line in zip(range(__A) , __A):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def lowerCAmelCase (__A , __A=5 , __A=0.05):
"""simple docstring"""
_a = ['''unit tests''', '''test file''', '''configuration file''']
_a = example['''content'''].splitlines()
_a = 0
_a = 0
# first test
for _, line in zip(range(__A) , __A):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
_a = example['''content'''].count('''\n''')
_a = int(coeff * nlines)
for line in lines:
count_config += line.lower().count('''config''')
count_test += line.lower().count('''test''')
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = ['''def ''', '''class ''', '''for ''', '''while ''']
_a = example['''content'''].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def lowerCAmelCase (__A , __A=4):
"""simple docstring"""
_a = example['''content'''].splitlines()
_a = 0
for line in lines:
counter += line.lower().count('''=''')
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = tokenizer(example['''content'''] , truncation=__A)['''input_ids''']
_a = len(example['''content''']) / len(__A)
return {"ratio": ratio}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = {}
results.update(get_hash(__A))
results.update(line_stats(__A))
results.update(alpha_stats(__A))
results.update(char_token_ratio(__A))
results.update(is_autogenerated(__A))
results.update(is_config_or_test(__A))
results.update(has_no_keywords(__A))
results.update(has_few_assignments(__A))
return results
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if not check_uniques(__A , __A):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def lowerCAmelCase (__A):
"""simple docstring"""
with open(__A , '''rb''') as f_in:
with gzip.open(str(__A) + '''.gz''' , '''wb''' , compresslevel=6) as f_out:
shutil.copyfileobj(__A , __A)
os.unlink(__A)
# Settings
lowercase_ = HfArgumentParser(PreprocessingArguments)
lowercase_ = parser.parse_args()
if args.num_workers is None:
lowercase_ = multiprocessing.cpu_count()
lowercase_ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
lowercase_ = time.time()
lowercase_ = load_dataset(args.dataset_name, split="train")
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
lowercase_ = time.time()
lowercase_ = ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
lowercase_ = set(ds.unique("hash"))
lowercase_ = len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
lowercase_ = time.time()
lowercase_ = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args})
print(F"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(F"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
lowercase_ = time.time()
lowercase_ , lowercase_ = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(F"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
lowercase_ = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / "duplicate_clusters.json", "w") as f:
json.dump(duplicate_clusters, f)
lowercase_ = output_dir / "data"
data_dir.mkdir(exist_ok=True)
lowercase_ = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
lowercase_ = str(data_dir / F"""file-{file_number+1:012}.json""")
lowercase_ = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"""Time to save dataset: {time.time()-t_start:.2f}""")
| 11 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = CTRLTokenizer
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Any = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_a = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
_a = dict(zip(A , range(len(A ) ) ) )
_a = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
_a = {'''unk_token''': '''<unk>'''}
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_a = 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(A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A ) )
def a__ (self , **A ) -> int:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **A )
def a__ (self , A ) -> Tuple:
"""simple docstring"""
_a = '''adapt react readapt apt'''
_a = '''adapt react readapt apt'''
return input_text, output_text
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_a = '''adapt react readapt apt'''
_a = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
_a = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_a = tokens + [tokenizer.unk_token]
_a = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
| 11 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __A :
'''simple docstring'''
def __init__(self , A , A=3 , A=32 , A=3 , A=10 , A=[8, 16, 32, 64] , A=[1, 1, 2, 1] , A=True , A=True , A="relu" , A=3 , A=None , A=["stage2", "stage3", "stage4"] , A=[2, 3, 4] , A=1 , ) -> Any:
"""simple docstring"""
_a = parent
_a = batch_size
_a = image_size
_a = num_channels
_a = embeddings_size
_a = hidden_sizes
_a = depths
_a = is_training
_a = use_labels
_a = hidden_act
_a = num_labels
_a = scope
_a = len(A )
_a = out_features
_a = out_indices
_a = num_groups
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.num_labels )
_a = self.get_config()
return config, pixel_values, labels
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def a__ (self , A , A , A ) -> List[str]:
"""simple docstring"""
_a = BitModel(config=A )
model.to(A )
model.eval()
_a = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def a__ (self , A , A , A ) -> List[Any]:
"""simple docstring"""
_a = self.num_labels
_a = BitForImageClassification(A )
model.to(A )
model.eval()
_a = model(A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a__ (self , A , A , A ) -> Any:
"""simple docstring"""
_a = BitBackbone(config=A )
model.to(A )
model.eval()
_a = model(A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_a = None
_a = BitBackbone(config=A )
model.to(A )
model.eval()
_a = model(A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def a__ (self ) -> Any:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a = config_and_inputs
_a = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
__lowerCamelCase : str = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : int = False
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Optional[Any] = False
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = BitModelTester(self )
_a = ConfigTester(self , config_class=A , has_text_modality=A )
def a__ (self ) -> str:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def a__ (self ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def a__ (self ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def a__ (self ) -> Dict:
"""simple docstring"""
pass
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , A )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*A )
def a__ (self ) -> Any:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(config=A )
for name, module in model.named_modules():
if isinstance(A , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
def a__ (self ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(A , A , A ):
_a = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_a = model(**self._prepare_for_class(A , A ) )
_a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_a = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_a = layer_type
_a = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a = True
check_hidden_states_output(A , A , A )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def a__ (self ) -> int:
"""simple docstring"""
pass
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def a__ (self ) -> int:
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = BitModel.from_pretrained(A )
self.assertIsNotNone(A )
def lowerCAmelCase ():
"""simple docstring"""
_a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
return image
@require_torch
@require_vision
class __A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def a__ (self ) -> str:
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def a__ (self ) -> int:
"""simple docstring"""
_a = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A )
_a = self.default_image_processor
_a = prepare_img()
_a = image_processor(images=A , return_tensors='''pt''' ).to(A )
# forward pass
with torch.no_grad():
_a = model(**A )
# verify the logits
_a = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , A )
_a = torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A , atol=1E-4 ) )
@require_torch
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = (BitBackbone,) if is_torch_available() else ()
__lowerCamelCase : List[str] = BitConfig
__lowerCamelCase : Any = False
def a__ (self ) -> Dict:
"""simple docstring"""
_a = BitModelTester(self )
| 11 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
"/attention/": "/0/SelfAttention/",
"/self_attention/": "/0/SelfAttention/",
"/encoder_decoder_attention/": "/1/EncDecAttention/",
"value": "v",
"query": "q",
"key": "k",
"out": "o",
"pre_self_attention_layer_norm": "0/layer_norm",
"pre_cross_attention_layer_norm": "1/layer_norm",
"pre_attention_layer_norm": "0/layer_norm", # previously 1, but seems wrong
"token_embedder": "shared",
"encoder_norm": "final_layer_norm",
"decoder_norm": "final_layer_norm",
"relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
"router/router_weights/w/": "router/classifier/",
"roer/roer_weights/w/": "router/classifier/",
"logits_dense": "lm_head",
}
def lowerCAmelCase (__A):
"""simple docstring"""
_a = list(s_dict.keys())
for key in keys:
_a = r'''.*/layers_(\d+)'''
_a = key
if re.match(__A , __A):
_a = re.sub(r'''layers_(\d+)''' , r'''block/\1/layer''' , __A)
_a = r'''(encoder|decoder)\/'''
if re.match(__A , __A):
_a = re.match(__A , __A).groups()
if groups[0] == "encoder":
_a = re.sub(r'''/mlp/''' , r'''/1/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/1/layer_norm/''' , __A)
elif groups[0] == "decoder":
_a = re.sub(r'''/mlp/''' , r'''/2/mlp/''' , __A)
_a = re.sub(r'''/pre_mlp_layer_norm/''' , r'''/2/layer_norm/''' , __A)
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
_a = new_key.replace(__A , __A)
print(F'''{key} -> {new_key}''')
_a = s_dict.pop(__A)
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
_a = s_dict[
'''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys()):
if "expert" in key:
_a = s_dict[key].shape[0]
_a = s_dict[key]
for idx in range(__A):
_a = expert_weihts[idx]
print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')
s_dict.pop(__A)
return s_dict
lowercase_ = {
"NUM_ENCODER_LAYERS": "num_layers",
"NUM_DECODER_LAYERS": "num_decoder_layers",
"NUM_HEADS": "num_heads",
"HEAD_DIM": "d_kv",
"EMBED_DIM": "d_model",
"MLP_DIM": "d_ff",
"NUM_SELECTED_EXPERTS": "num_selected_experts",
"NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
"NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
"dense.MlpBlock.activations": "feed_forward_proj",
}
def lowerCAmelCase (__A , __A):
"""simple docstring"""
import regex as re
with open(__A , '''r''') as f:
_a = f.read()
_a = re.findall(r'''(.*) = ([0-9.]*)''' , __A)
_a = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
_a = float(__A) if '''.''' in value else int(__A)
_a = re.findall(r'''(.*activations) = \(\'(.*)\',\)''' , __A)[0]
_a = str(activation[1])
_a = num_experts
_a = SwitchTransformersConfig(**__A)
return config
def lowerCAmelCase (__A , __A , __A=None , __A="./" , __A=8):
"""simple docstring"""
print(F'''Loading flax weights from : {flax_checkpoint_path}''')
_a = checkpoints.load_tax_checkpoint(__A)
if gin_file is not None:
_a = convert_gin_to_config(__A , __A)
else:
_a = SwitchTransformersConfig.from_pretrained(__A)
_a = SwitchTransformersForConditionalGeneration(__A)
_a = flax_params['''target''']
_a = flatten_dict(__A , sep='''/''')
_a = rename_keys(__A)
_a = unflatten_dict(__A , sep='''/''')
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(__A , __A)
print(F'''Save PyTorch model to {pytorch_dump_path}''')
pt_model.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 11 | 1 |
'''simple docstring'''
import os
import string
import sys
lowercase_ = 1 << 8
lowercase_ = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
lowercase_ = KEYMAP["up"]
lowercase_ = KEYMAP["left"]
if sys.platform == "win32":
lowercase_ = []
lowercase_ = {
B"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
B"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
B"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
B"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
B"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
B"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
B"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
B"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(10):
lowercase_ = ord(str(i))
def lowerCAmelCase ():
"""simple docstring"""
if os.name == "nt":
import msvcrt
_a = '''mbcs'''
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(__A) == 0:
# Read the keystroke
_a = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
_a = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
_a = chr(WIN_KEYMAP[cha])
WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int''']))
WIN_CH_BUFFER.append(__A)
if ord(__A) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126))
_a = chr(KEYMAP['''esc'''])
except KeyError:
_a = cha[1]
else:
_a = ch.decode(__A)
else:
_a = WIN_CH_BUFFER.pop(0)
elif os.name == "posix":
import termios
import tty
_a = sys.stdin.fileno()
_a = termios.tcgetattr(__A)
try:
tty.setraw(__A)
_a = sys.stdin.read(1)
finally:
termios.tcsetattr(__A , termios.TCSADRAIN , __A)
return ch
def lowerCAmelCase ():
"""simple docstring"""
_a = get_raw_chars()
if ord(__A) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(__A) == KEYMAP["esc"]:
_a = get_raw_chars()
if ord(__A) == KEYMAP["mod_int"]:
_a = get_raw_chars()
if ord(__A) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__A) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(__A) + ARROW_KEY_FLAG)
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 11 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 1 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __A ( A ):
'''simple docstring'''
@require_torch
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = '''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
_a = '''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
_a = '''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
_a = '''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(A )
BertModel.from_pretrained(A )
BertTokenizer.from_pretrained(A )
pipeline(task='''fill-mask''' , model=A )
# baseline - just load from_pretrained with normal network
_a = [sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
_a = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_a = '''1'''
_a = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def a__ (self ) -> Dict:
"""simple docstring"""
_a = '''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
_a = '''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
_a = '''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
_a = '''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(A )
BertModel.from_pretrained(A )
BertTokenizer.from_pretrained(A )
pipeline(task='''fill-mask''' , model=A )
# baseline - just load from_pretrained with normal network
_a = [sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
_a = self.get_env()
_a = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = '''
from transformers import BertConfig, BertModel, BertTokenizer
'''
_a = '''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
'''
_a = '''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
'''
# baseline - just load from_pretrained with normal network
_a = [sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
_a = self.get_env()
_a = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
# next emulate no network
_a = [sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_a = '''1'''
_a = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = '''
from transformers import pipeline
'''
_a = '''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
'''
_a = '''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
'''
_a = self.get_env()
_a = '''1'''
_a = [sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
_a = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
'''You cannot infer task automatically within `pipeline` when using offline mode''' , result.stderr.decode().replace('''\n''' , '''''' ) , )
@require_torch
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = '''
from transformers import AutoModel
'''
_a = '''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
'''
# baseline - just load from_pretrained with normal network
_a = [sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
_a = self.get_env()
_a = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
_a = '''1'''
_a = subprocess.run(A , env=A , check=A , capture_output=A )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
| 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
lowercase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_ = 10
lowercase_ = 256
def lowerCAmelCase (__A):
"""simple docstring"""
if len(__A) < MIN_NUM_TOKENS:
return None
_a = MinHash(num_perm=__A)
for token in set(__A):
min_hash.update(token.encode())
return min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}
class __A :
'''simple docstring'''
def __init__(self , *,
A = 0.85 , ) -> Optional[int]:
"""simple docstring"""
_a = duplication_jaccard_threshold
_a = NUM_PERM
_a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_a = defaultdict(A )
def a__ (self , A , A ) -> None:
"""simple docstring"""
_a = self._index.query(A )
if code_key in self._index.keys:
print(f'''Duplicate key {code_key}''' )
return
self._index.insert(A , A )
if len(A ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(A )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(A )
def a__ (self ) -> List[List[Dict]]:
"""simple docstring"""
_a = []
for base, duplicates in self._duplicate_clusters.items():
_a = [base] + list(A )
# reformat the cluster to be a list of dict
_a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(A )
return duplicate_clusters
def a__ (self , A ) -> None:
"""simple docstring"""
_a = self.get_duplicate_clusters()
with open(A , '''w''' ) as f:
json.dump(A , A )
def lowerCAmelCase (__A):
"""simple docstring"""
_a , _a = element
_a = get_min_hash([t for t in NON_ALPHA.split(data['''content''']) if len(t.strip()) > 0])
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def lowerCAmelCase (__A):
"""simple docstring"""
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__A , max_queue_size=10_000) , chunksize=100 , ):
if data is not None:
yield data
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = DuplicationIndex(duplication_jaccard_threshold=__A)
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A)) , max_queue_size=100)):
di.add(__A , __A)
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = get_tokens(__A)
_a = get_tokens(__A)
return len(tokensa & tokensa) / len(tokensa | tokensa)
lowercase_ = None
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = []
for elementa in cluster:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__A , __A) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_a = 1
extremes.append(__A)
return extremes
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
global _shared_dataset
_a = dataset
_a = []
_a = partial(_find_cluster_extremes_shared , jaccard_threshold=__A)
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__A , __A , ) , total=len(__A) , ):
extremes_list.append(__A)
return extremes_list
def lowerCAmelCase (__A , __A = 0.85):
"""simple docstring"""
_a = make_duplicate_clusters(__A , __A)
_a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_a = {}
_a = find_extremes(__A , __A , __A)
for extremes in extremes_clusters:
for element in extremes:
_a = element
_a = duplicate_indices - set(extreme_dict.keys())
_a = dataset.filter(lambda __A , __A: idx not in remove_indices , with_indices=__A)
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_a = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(__A)}''')
print(F'''Number of duplicate clusters: {len(__A)}''')
print(F'''Files in duplicate cluster: {len(__A)}''')
print(F'''Unique files in duplicate cluster: {len(__A)}''')
print(F'''Filtered dataset size: {len(__A)}''')
return ds_filter, duplicate_clusters
| 11 | 1 |
'''simple docstring'''
from math import factorial
class __A :
'''simple docstring'''
def __init__(self , A , A ) -> str:
"""simple docstring"""
_a = real
if isinstance(A , A ):
_a = [1] * rank
else:
_a = rank
def __repr__(self ) -> List[Any]:
"""simple docstring"""
return (
f'''{self.real}+'''
f'''{'+'.join(str(A )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}'''
)
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , A )
def __add__(self , A ) -> int:
"""simple docstring"""
if not isinstance(A , A ):
return Dual(self.real + other , self.duals )
_a = self.duals.copy()
_a = other.duals.copy()
if len(A ) > len(A ):
o_dual.extend([1] * (len(A ) - len(A )) )
elif len(A ) < len(A ):
s_dual.extend([1] * (len(A ) - len(A )) )
_a = []
for i in range(len(A ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , A )
__lowerCamelCase : Optional[Any] = __add__
def __sub__(self , A ) -> Optional[Any]:
"""simple docstring"""
return self + other * -1
def __mul__(self , A ) -> Union[str, Any]:
"""simple docstring"""
if not isinstance(A , A ):
_a = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , A )
_a = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , A )
__lowerCamelCase : Optional[Any] = __mul__
def __truediv__(self , A ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(A , A ):
_a = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , A )
raise ValueError
def __floordiv__(self , A ) -> str:
"""simple docstring"""
if not isinstance(A , A ):
_a = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , A )
raise ValueError
def __pow__(self , A ) -> Optional[Any]:
"""simple docstring"""
if n < 0 or isinstance(A , A ):
raise ValueError('''power must be a positive integer''' )
if n == 0:
return 1
if n == 1:
return self
_a = self
for _ in range(n - 1 ):
x *= self
return x
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
if not callable(__A):
raise ValueError('''differentiate() requires a function as input for func''')
if not isinstance(__A , (float, int)):
raise ValueError('''differentiate() requires a float as input for position''')
if not isinstance(__A , __A):
raise ValueError('''differentiate() requires an int as input for order''')
_a = Dual(__A , 1)
_a = func(__A)
if order == 0:
return result.real
return result.duals[order - 1] * factorial(__A)
if __name__ == "__main__":
import doctest
doctest.testmod()
def lowerCAmelCase (__A):
"""simple docstring"""
return y**2 * y**4
print(differentiate(f, 9, 2))
| 11 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class __A ( nn.Module ):
'''simple docstring'''
def __init__(self ) -> Dict:
"""simple docstring"""
super().__init__()
_a = nn.Linear(3 , 4 )
_a = nn.BatchNormad(4 )
_a = nn.Linear(4 , 5 )
def a__ (self , A ) -> Dict:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(A ) ) )
class __A ( A ):
'''simple docstring'''
def a__ (self , A , *A , **A ) -> Optional[Any]:
"""simple docstring"""
return (args[0] + 1,) + args[1:], kwargs
class __A ( A ):
'''simple docstring'''
def a__ (self , A , A ) -> int:
"""simple docstring"""
return output + 1
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
self.assertEqual(test_model._hf_hook , A )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
_a = ModelHook()
add_hook_to_module(A , A )
add_hook_to_module(A , A , append=A )
self.assertEqual(isinstance(test_model._hf_hook , A ) , A )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(A , '''_old_forward''' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , '''forward''' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['''x'''] )
remove_hook_from_module(A )
self.assertFalse(hasattr(A , '''_hf_hook''' ) )
self.assertFalse(hasattr(A , '''_old_forward''' ) )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(x + 1 )
_a = test_model(x + 2 )
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PreForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , A , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , A , atol=1E-5 )
def a__ (self ) -> str:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_a = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(A , A )
_a = test_model(A )
assert torch.allclose(A , output + 2 , atol=1E-5 )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
_a = torch.randn(2 , 3 )
_a = test_model(A )
_a = PostForwardHook()
add_hook_to_module(A , A )
_a = test_model(A )
self.assertTrue(torch.allclose(A , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_a = True
_a = test_model(A )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(A , AlignDevicesHook(io_same_device=A ) )
_a = torch.randn(2 , 3 ).to(0 )
_a = model(A )
self.assertEqual(output.device , torch.device(0 ) )
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(hook_kwargs['''execution_device'''] )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
_a = {
'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
'''offload''': True,
'''offload_buffers''': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**A ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**A ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(A , execution_device=A , offload=A )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(A , execution_device=A , offload=A , offload_buffers=A )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
def a__ (self ) -> Any:
"""simple docstring"""
_a = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# This will move each submodule on different devices
_a = 0 if torch.cuda.is_available() else '''cpu'''
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
# Buffers are not included in the offload by default, so are on the execution device
_a = torch.device(A )
self.assertEqual(model.batchnorm.running_mean.device , A )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
A , execution_device=A , offload=A , weights_map=model.state_dict() , offload_buffers=A , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''meta''' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('''meta''' ) )
_a = torch.randn(2 , 3 )
_a = model(A )
self.assertEqual(output.device , A )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(A )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('''cpu''' ) )
self.assertEqual(model.lineara.weight.device , torch.device('''cpu''' ) )
| 11 | 1 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase_ = data_utils.TransfoXLTokenizer
lowercase_ = data_utils.TransfoXLCorpus
lowercase_ = data_utils
lowercase_ = data_utils
def lowerCAmelCase (__A , __A , __A , __A):
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(__A , '''rb''') as fp:
_a = pickle.load(__A , encoding='''latin1''')
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
_a = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
print(F'''Save vocabulary to {pytorch_vocab_dump_path}''')
_a = corpus.vocab.__dict__
torch.save(__A , __A)
_a = corpus.__dict__
corpus_dict_no_vocab.pop('''vocab''' , __A)
_a = pytorch_dump_folder_path + '''/''' + CORPUS_NAME
print(F'''Save dataset to {pytorch_dataset_dump_path}''')
torch.save(__A , __A)
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
_a = os.path.abspath(__A)
_a = os.path.abspath(__A)
print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''')
# Initialise PyTorch model
if transfo_xl_config_file == "":
_a = TransfoXLConfig()
else:
_a = TransfoXLConfig.from_json_file(__A)
print(F'''Building PyTorch model from configuration: {config}''')
_a = TransfoXLLMHeadModel(__A)
_a = load_tf_weights_in_transfo_xl(__A , __A , __A)
# Save pytorch-model
_a = os.path.join(__A , __A)
_a = os.path.join(__A , __A)
print(F'''Save PyTorch model to {os.path.abspath(__A)}''')
torch.save(model.state_dict() , __A)
print(F'''Save configuration file to {os.path.abspath(__A)}''')
with open(__A , '''w''' , encoding='''utf-8''') as f:
f.write(config.to_json_string())
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
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(
"--tf_checkpoint_path",
default="",
type=str,
help="An optional path to a TensorFlow checkpoint path to be converted.",
)
parser.add_argument(
"--transfo_xl_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--transfo_xl_dataset_file",
default="",
type=str,
help="An optional dataset file to be converted in a vocabulary.",
)
lowercase_ = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 11 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __A ( A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[Any] = IFInpaintingSuperResolutionPipeline
__lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
__lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
__lowerCamelCase : str = PipelineTesterMixin.required_optional_params - {'latents'}
def a__ (self ) -> List[Any]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def a__ (self , A , A=0 ) -> List[Any]:
"""simple docstring"""
if str(A ).startswith('''mps''' ):
_a = torch.manual_seed(A )
else:
_a = torch.Generator(device=A ).manual_seed(A )
_a = floats_tensor((1, 3, 16, 16) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''original_image''': original_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def a__ (self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def a__ (self ) -> str:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def a__ (self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self._test_save_load_local()
def a__ (self ) -> Any:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 11 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
lowercase_ = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
),
"google/tapas-base-finetuned-wikisql-supervised": (
"https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
),
"google/tapas-base-finetuned-tabfact": (
"https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
),
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Optional[int] = 'tapas'
def __init__(self , A=30_522 , A=768 , A=12 , A=12 , A=3_072 , A="gelu" , A=0.1 , A=0.1 , A=1_024 , A=[3, 256, 256, 2, 256, 256, 10] , A=0.02 , A=1E-12 , A=0 , A=10.0 , A=0 , A=1.0 , A=None , A=1.0 , A=False , A=None , A=1.0 , A=1.0 , A=False , A=False , A="ratio" , A=None , A=None , A=64 , A=32 , A=False , A=True , A=False , A=False , A=True , A=False , A=None , A=None , **A , ) -> Dict:
"""simple docstring"""
super().__init__(pad_token_id=A , **A )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_sizes
_a = initializer_range
_a = layer_norm_eps
# Fine-tuning task hyperparameters
_a = positive_label_weight
_a = num_aggregation_labels
_a = aggregation_loss_weight
_a = use_answer_as_supervision
_a = answer_loss_importance
_a = use_normalized_answer_loss
_a = huber_loss_delta
_a = temperature
_a = aggregation_temperature
_a = use_gumbel_for_cells
_a = use_gumbel_for_aggregation
_a = average_approximation_function
_a = cell_selection_preference
_a = answer_loss_cutoff
_a = max_num_rows
_a = max_num_columns
_a = average_logits_per_cell
_a = select_one_column
_a = allow_empty_column_selection
_a = init_cell_selection_weights_to_zero
_a = reset_position_index_per_cell
_a = disable_per_token_loss
# Aggregation hyperparameters
_a = aggregation_labels
_a = no_aggregation_label_index
if isinstance(self.aggregation_labels , A ):
_a = {int(A ): v for k, v in aggregation_labels.items()}
| 11 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __A :
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=6 , A=17 , A=23 , A=11 , A=True , ) -> Tuple:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = act_dim
_a = state_dim
_a = hidden_size
_a = max_length
_a = is_training
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = floats_tensor((self.batch_size, self.seq_length, 1) )
_a = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000 )
_a = random_attention_mask((self.batch_size, self.seq_length) )
_a = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def a__ (self ) -> str:
"""simple docstring"""
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def a__ (self , A , A , A , A , A , A , A , ) -> List[Any]:
"""simple docstring"""
_a = DecisionTransformerModel(config=A )
model.to(A )
model.eval()
_a = model(A , A , A , A , A , A )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __A ( A , A , A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCamelCase : List[str] = ()
__lowerCamelCase : Tuple = {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCamelCase : str = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCamelCase : List[str] = False
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : str = False
__lowerCamelCase : Dict = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Tuple = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[str] = False
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = DecisionTransformerModelTester(self )
_a = ConfigTester(self , config_class=A , hidden_size=37 )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DecisionTransformerModel.from_pretrained(A )
self.assertIsNotNone(A )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(A )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(A )] , A )
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = 2 # number of steps of autoregressive prediction we will perform
_a = 10 # defined by the RL environment, may be normalized
_a = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
_a = model.to(A )
_a = model.config
torch.manual_seed(0 )
_a = torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ) # env.reset()
_a = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=A )
_a = torch.tensor(A , device=A , dtype=torch.floataa ).reshape(1 , 1 , 1 )
_a = state
_a = torch.zeros(1 , 0 , config.act_dim , device=A , dtype=torch.floataa )
_a = torch.zeros(1 , 0 , device=A , dtype=torch.floataa )
_a = torch.tensor(0 , device=A , dtype=torch.long ).reshape(1 , 1 )
for step in range(A ):
_a = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=A )] , dim=1 )
_a = torch.cat([rewards, torch.zeros(1 , 1 , device=A )] , dim=1 )
_a = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
_a , _a , _a = model(
states=A , actions=A , rewards=A , returns_to_go=A , timesteps=A , attention_mask=A , return_dict=A , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1E-4 ) )
_a , _a , _a , _a = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=A , dtype=torch.floataa ),
1.0,
False,
{},
)
_a = action_pred[0, -1]
_a = torch.cat([states, state] , dim=1 )
_a = returns_to_go[0, -1] - reward
_a = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
_a = torch.cat(
[timesteps, torch.ones((1, 1) , device=A , dtype=torch.long ) * (step + 1)] , dim=1 )
| 11 | 1 |
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